An Empirical Study on Fertility Proposals Using Multi-Grained Topic Analysis Methods

Fertility issues are closely related to population security, in 60 years China's population for the first time in a negative growth trend, the change of fertility policy is of great concern to the community. 2023 "two sessions" proposal "suggests that the country in the form of legislation, the birth of the registration of the cancellation of the marriage restriction" This topic was once a hot topic on the Internet, and "unbundling" the relationship between birth registration and marriage has become the focus of social debate. In this paper, we adopt co-occurrence semantic analysis, topic analysis and sentiment analysis to conduct multi-granularity semantic analysis of microblog comments. It is found that the discussion on the proposal of "removing marriage restrictions from birth registration" involves the individual, society and the state at three dimensions, and is detailed into social issues such as personal behaviour, social ethics and law, and national policy, with people's sentiment inclined to be negative in most of the topics. Based on this, eight proposals were made to provide a reference for governmental decision making and to form a reference method for researching public opinion on political issues.Comment: 7 pages, 4 figures, 1 tabl

Performance analysis of end-to-end SNR estimators for AF relaying

Many existing signal-to-noise ratio (SNR) estimators were designed and evaluated for conventional one-hop communications systems. However, for a relaying system, it is the end-to-end SNR that determines the system performance. In this paper, we will fill this gap by evaluating the performances of the existing SNR estimators in a dual-hop relaying system used for each hop. The probability density functions of the SNR estimators are first derived, whose parameters are fitted as functions of the sample size and the true value of SNR. Using them, the cumulative distribution functions of the end-to-end SNR and the bit error rate performance for a relaying system are derived. Numerical results show that the squared signal-to-noise variance estimator has the best performance for small SNRs and the second-order fourth-order moments estimator has the best performance for large SNRs, while the signalto-variation ratio estimator has the worst performance, among the existing SNR estimators, for AF relaying systems

THE INFLUENCE OF COORDINATION BETWEEN UPPER LIMBS\u27 JOINTS ON SPORT LEVEL IN SNOOKER

This study explored the effect of coordination between upper limb joints on the technical level of shooting in billiards.Eight professional and eight amateur players were asked to shoot according to a specific rounte and vector coding method used to quantify the coordination of the motions of the limbs during the shooting stage.For coordination between the flexion and extension of shoulder and flexion and extension of elbow,the proportion of the anti-phase and elbow-phase coordination in the professional group was higher than the amateur group,and the proportion of the shoulder-phase was lower for professional than amateur group. For coordination of the flexion and extension of shoulder and the the adduction and outreach of wrist,the proportion of the wrist-phase coordination in the professional group was higher than the amateur group,and the proportion of the shoulder-phase was lower for the professional than amateur group.These indicators can be used as diagnostic indicators for snooker player\u27s shooting motio

ESTIMATION OF LOWER LIMB KINETICS FROM LANDMARKS DURING SIDESTEPPING VIA ARTIFICIAL NEURAL NETWORKS

The purpose of this study was to determine the validity of kinetics estimated from 3D coordinates of landmarks during sidestepping by artificial neural networks (ANN). 71 male college professional soccer athletes performed sidestepping with two directions (left and right) and two cutting angles (45° and 90°) 3times for every task, totally 12 times. Coordinates of reflective markers, ground reaction forces (GRF) and lower limb joint moments were measured. All 18 body landmarks such as joints center were obtained by reflective markers as inputs to estimate GRF and lower joint moments in the ANN whose type was multilayer perceptron. The most of kinetics estimated by ANN showed strong correlation(r\u3e0.9) with measured results. Just few kinetic curves of ANN existed significant differences in a few time points compared to measured results. ANN could accurately estimate kinetics from the coordinates of body landmarks druing sidestepping

Numerical study of divertor detachment in the MAST-U tokamak

Divertor detachment is a promising method to reduce heat loading and erosion in tokamak devices or even in future magnetic fusion reactors. In this thesis, two detachment regimes (increasing upstream density and seeding impurity) leading to the decrease of the divertor ion flux is numerically studied through modelling the super-X divertor in MAST-U like conditions. This thesis builds on previous work using the original SD1D modules of BOUT++, which is established to simulate parallel transport process from upstream to the target. We implement an upgrade in SD1D module by adding molecule-plasma interactions and impurity seeding in order to making simulations more self-consistent. To understand the role of molecules in density ramp detachment process, comparisons are made between the cases with different recycling conditions. It is found that if the recycling in divertor is more likely to produce neutral molecule, the roll-over of ion flux at the target occurs at a higher upstream density and a lower target temperature. We also find that molecule–plasma interactions are as crucial as atom–plasma interactions during divertor detachment, both of which account for the main plasma momentum loss. Molecule–plasma interactions can even cause a strong rise of Halpha signal in the detachment process, which agrees with the measurement on other devices (e.g TCV tokamak). The divertor detachment induced by seeding impurity (e.g. neon) is simulated in order to understand the difference between the two detachment regimes. It is found that increasing the puffing rate of neon impurity cannot quickly reduce the target temperature, thus the density of molecule species is small during detachment due to the high molecule dissociation rate, while atom-plasma interactions become dominant and account for the most of plasma momentum loss. Different from the density ramp induced detachment, we cannot find the strong rise of Halpha signal in this case

The Determinants of the Success of Crowdfunding

Crowdfunding allows the crowd to donate small amounts of money to entrepreneurs through online platforms. Comparing with traditional financial institutions, this new method facilitates the financing process through direct and easy online contact between initiators and investors. Based on the data obtained from Kickstarter, the largest crowdfunding platform, I investigated 27,117 crowdfunding projects from Jan 1st 2015 to Jun 30th 2015, and I find that a crowd funding campaign with a realistic funding goal, a suitable funding period, and more updates and interactions with investors, is much likely to be successfully funded. In addition, the different types of founders are very influential in crowdfunding outcomes. For example, females tend to collect funds more successfully than males do. Founders in the form of teams, companies or a specific project are also beneficial to funding outcomes

Multi-hop relaying using energy harvesting

In this letter, the performance of multi-hop relaying using energy harvesting is evaluated. Both amplify-and-forward and decode-and-forward relaying protocols are considered. The evaluation is conducted for time-switching energy harvesting as well as power-splitting energy harvesting. The largest number of hops given an initial amount of energy from the source node is calculated. Numerical results show that, in order to extend the network coverage using multi-hop relaying, time-switching is a better option than power splitting and in some cases, decode-and-forward also supports more hops than amplify-and-forward

Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks

Deep Graph Learning (DGL) has emerged as a crucial technique across various domains. However, recent studies have exposed vulnerabilities in DGL models, such as susceptibility to evasion and poisoning attacks. While empirical and provable robustness techniques have been developed to defend against graph modification attacks (GMAs), the problem of certified robustness against graph injection attacks (GIAs) remains largely unexplored. To bridge this gap, we introduce the node-aware bi-smoothing framework, which is the first certifiably robust approach for general node classification tasks against GIAs. Notably, the proposed node-aware bi-smoothing scheme is model-agnostic and is applicable for both evasion and poisoning attacks. Through rigorous theoretical analysis, we establish the certifiable conditions of our smoothing scheme. We also explore the practical implications of our node-aware bi-smoothing schemes in two contexts: as an empirical defense approach against real-world GIAs and in the context of recommendation systems. Furthermore, we extend two state-of-the-art certified robustness frameworks to address node injection attacks and compare our approach against them. Extensive evaluations demonstrate the effectiveness of our proposed certificates

Homophily-Driven Sanitation View for Robust Graph Contrastive Learning

We investigate adversarial robustness of unsupervised Graph Contrastive Learning (GCL) against structural attacks. First, we provide a comprehensive empirical and theoretical analysis of existing attacks, revealing how and why they downgrade the performance of GCL. Inspired by our analytic results, we present a robust GCL framework that integrates a homophily-driven sanitation view, which can be learned jointly with contrastive learning. A key challenge this poses, however, is the non-differentiable nature of the sanitation objective. To address this challenge, we propose a series of techniques to enable gradient-based end-to-end robust GCL. Moreover, we develop a fully unsupervised hyperparameter tuning method which, unlike prior approaches, does not require knowledge of node labels. We conduct extensive experiments to evaluate the performance of our proposed model, GCHS (Graph Contrastive Learning with Homophily-driven Sanitation View), against two state of the art structural attacks on GCL. Our results demonstrate that GCHS consistently outperforms all state of the art baselines in terms of the quality of generated node embeddings as well as performance on two important downstream tasks

Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,

Graph Neural Networks (GNNs) have become widely used in the field of graph mining. However, these networks are vulnerable to structural perturbations. While many research efforts have focused on analyzing vulnerability through poisoning attacks, we have identified an inefficiency in current attack losses. These losses steer the attack strategy towards modifying edges targeting misclassified nodes or resilient nodes, resulting in a waste of structural adversarial perturbation. To address this issue, we propose a novel attack loss framework called the Cost Aware Poisoning Attack (CA-attack) to improve the allocation of the attack budget by dynamically considering the classification margins of nodes. Specifically, it prioritizes nodes with smaller positive margins while postponing nodes with negative margins. Our experiments demonstrate that the proposed CA-attack significantly enhances existing attack strategie