Research on the Problems and Optimization Paths of Sports Event Governance in China

The State Sports General Administration issued Some opinions on promoting the reform of sports event approval system on December 30, 2014, clearly canceling the right of approval of mass sports events. Based on this, the community has reflected well, and the quantity and quality of sports events have been significantly improved, but this change has also increased the difficulty of sports event governance. This paper aims to summarize the problems existing in the governance of sports events and the corresponding optimization paths and provides suggestions for the healthy development of sports event. The literature was searched using a keyword “sports event governance” in CNKI. A total of 32 documents were retrieved. Based on the combination of literature content and certain expansion, thirty documents were included in this review. The research progress of sports event governance is expounded from the aspects of concept definition, background of sports event governance, problems existing in sports event governance, and the optimization paths of sports event in China. The problems in the governance of sports event in China include: (a) The power and responsibility of government departments are unclear; (b)The market system is not perfect and the market regulation function is weakened; (c) There lacks materialization of sports social organizations; (d) There are problems in the distribution of benefits among relevant departments, market players, and sports social organizations. The optimization paths of sports event governance include: (a) Clearing government \u27 s power and responsibility in sports event governance; (b) Improving the market mechanism to enhance the main regulatory capacity; (c) Establishing new supervision mechanism between government and society. The decentralization at the government level has greatly promoted the enthusiasm of social organizations to host sports event, but the new order will always be accompanied by some chaos at the early stage. There are some problems at the government level, the market level, and the social sports organization level. The solutions to these problems are still at the research stage. For example, the optimization schemes of various subjects mentioned in this paper have been gradually studied and summarized

Near Minimax-Optimal Distributional Temporal Difference Algorithms and The Freedman Inequality in Hilbert Spaces

Distributional reinforcement learning (DRL) has achieved empirical success in various domains. One of the core tasks in the field of DRL is distributional policy evaluation, which involves estimating the return distribution $\eta^\pi$ for a given policy $\pi$. The distributional temporal difference (TD) algorithm has been accordingly proposed, which is an extension of the temporal difference algorithm in the classic RL literature. In the tabular case, \citet{rowland2018analysis} and \citet{rowland2023analysis} proved the asymptotic convergence of two instances of distributional TD, namely categorical temporal difference algorithm (CTD) and quantile temporal difference algorithm (QTD), respectively. In this paper, we go a step further and analyze the finite-sample performance of distributional TD. To facilitate theoretical analysis, we propose a non-parametric distributional TD algorithm (NTD). For a $\gamma$-discounted infinite-horizon tabular Markov decision process, we show that for NTD we need $\tilde{O}\left(\frac{1}{\varepsilon^{2p}(1-\gamma)^{2p+1}}\right)$ iterations to achieve an $\varepsilon$-optimal estimator with high probability, when the estimation error is measured by the $p$-Wasserstein distance. This sample complexity bound is minimax optimal (up to logarithmic factors) in the case of the $1$-Wasserstein distance. To achieve this, we establish a novel Freedman's inequality in Hilbert spaces, which would be of independent interest. In addition, we revisit CTD, showing that the same non-asymptotic convergence bounds hold for CTD in the case of the $p$-Wasserstein distance

Translational selection in human: more pronounced in housekeeping genes

Background: Translational selection is a ubiquitous and significant mechanism to regulate protein expression in prokaryotes and unicellular eukaryotes. Recent evidence has shown that translational selection is weakly operative in highly expressed genes in human and other vertebrates. However, it remains unclear whether translational selection acts differentially on human genes depending on their expression patterns. Results: Here we report that human housekeeping (HK) genes that are strictly defined as genes that are expressed ubiquitously and consistently in most or all tissues, are under stronger translational selection. Conclusions: These observations clearly show that translational selection is also closely associated with expression pattern. Our results suggest that human HK genes are more efficiently and/or accurately translated into proteins, which will inevitably open up a new understanding of HK genes and the regulation of gene expression. Reviewers This article was reviewed by Yuan Yuan, Baylor College of Medicine; Han Liang, University of Texas MD Anderson Cancer Center (nominated by Dr Laura Landweber) Eugene Koonin, NCBI, NLM, NIH, United States of America Sandor Pongor, International Centre for Genetic Engineering and biotechnology (ICGEB), Italy

DVI-SLAM: A Dual Visual Inertial SLAM Network

Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress. However, how to make full use of visual information as well as better integrate with inertial measurement unit (IMU) in visual SLAM has potential research value. This paper proposes a novel deep SLAM network with dual visual factors. The basic idea is to integrate both photometric factor and re-projection factor into the end-to-end differentiable structure through multi-factor data association module. We show that the proposed network dynamically learns and adjusts the confidence maps of both visual factors and it can be further extended to include the IMU factors as well. Extensive experiments validate that our proposed method significantly outperforms the state-of-the-art methods on several public datasets, including TartanAir, EuRoC and ETH3D-SLAM. Specifically, when dynamically fusing the three factors together, the absolute trajectory error for both monocular and stereo configurations on EuRoC dataset has reduced by 45.3% and 36.2% respectively.Comment: 7 pages, 3 figure