Table tennis 2 crowns 2 Asian 2 seasons! Millions of luxury bonuses were released, and Wang Chuqin and other four people won 840,000. Sasha regretted it.

On April 23rd, Beijing time, WTT Macau Championship entered the final day of competition, which has become a performance of national table tennis. The coaching staff can also relax and watch the game. As the women’s singles won the semi-finals in advance, Ma Lin has already watched the game in the audience one day in advance. The results of women’s singles are still good. This year, three consecutive high-level events all won the semi-finals. Although the men’s singles didn’t win the semi-finals, they all won the championship and runner-up. The finals were all held among table tennis players.

Players’ hard work, for honor, for bonus, for world ranking, etc., bonus is an indispensable part of the game, and it is also a very important part, especially for high-level events, which attract experts to participate and compete for higher bonuses. The championship is second only to the Grand Slam level.

The singles champion of the championship can get 240,000 RMB, the second place is about 180,000 RMB, the top four is about 120,000 RMB, the top eight is about 100,000 RMB, and the top 16 is about 80,000 RMB. There is still a bonus of 60,000 RMB after stopping the first round. If the level is low, the champion is only tens of thousands of yuan, and the first round is even lower by several thousand yuan. Therefore, taking a high-level champion can offset many low-level bonuses. In addition, the points of the championship will be 1400 points for winning the championship and 1000 points for the runner-up, so it is also important for the world ranking. The reason why Qian Tianyi can quickly squeeze into the top ten is to reach the final at the highest level Grand Slam.

The table tennis won two championships, two runners-up and two runners-up. The results were gratifying. The champions of the first two stops were Sun Yingsha and Fan Zhendong, and this stop was changed. Chen Meng, Manyu Wang and Wang Chuqin did not reach the final in the first two stops, and Malone reached the final in Singapore and won the runner-up. The table tennis was still fierce, which was more conducive to progress.

In men’s singles, Wang Chuqin and Ma Long are 240,000 and 180,000, then 420,000, Fan Zhendong Top Eight is 100,000, Liang Jingkun Top 16 is 80,000, and Lin Shidong is 60,000 in the first round. The men’s singles won a total of 660,000 yuan.

In women’s singles, Manyu Wang and Chen Meng are 240,000 and 180,000, which is also 420,000; Sun Yingsha and Wang Yidi each get 120,000; Qian Tianyi gets 100,000; Chen Xingtong gets 60,000 in the first round, totaling 820,000 yuan, which makes a total of 1.48 million yuan.

It is already very high to get such a bonus in a week’s game. Of course, it can’t be different from football and basketball in commercial value. If there is a market and commercial value of football, the bonus in table tennis will naturally be higher.

For Guoping, it’s a luxury prize. The four finalists, Malone, Wang Chuqin, Chen Meng and Manyu Wang, got a combined prize of 840,000, but Sun Yingsha was more sorry, only getting 120,000. For other associations, it is more miserable. Most of them stop the first round or the second round. Because of the limited places, many players are not qualified to participate in the competition. They must participate in low-level competitions, and some need to start from the qualifiers.

As for the final share, it is said to be 40%. If it is really 40%, it will not even be half, but it must be admitted that there are so many bonuses announced by the ITTF. For example, Sun Yingsha grabbed 770,000 yuan in Singapore, 750,000 yuan in Fan Zhendong, and 240,000 yuan in Xinxiang, all with 1 million yuan in less than a month.

New vitality of Su River! 2023 Shanghai Suzhou River Half Marathon was held.

Transfer from: People’s Daily Client Shanghai Channel

Cao Lingjuan

At 7: 00 a.m. on April 22nd, in front of the Tianan Qianshu, a landmark building on the Suzhou River in Putuo District, the 2023 Shanghai Suzhou River Half Marathon started, and the road runners shared a sports feast in the early morning sunshine.

On the same day, Mari took the lead in crossing the finish line and won the championship in 1 hour, 03 minutes and 46 seconds. Liu Min won the first place in the women’s team in 1 hour, 13 minutes and 19 seconds. After waiting for 3 hours and 15 minutes, the first Shanghai Suzhou River Half Marathon ushered in the "closing time", and the race completion rate reached 98.78%.

The wind blows thousands of trees and waters in a chain, half of the Masuhe River and 18 bays. In recent years, Putuo District has achieved a comprehensive connection of the coastline of the Putuo section of Suzhou River for 21 kilometers, and made every effort to build a "semi-Masuhe" world-class waterfront. The Suzhou Creek in Putuo District witnessed the rapid changes of Shanghai’s history and culture. The 2023 Shanghai Suzhou Creek Half Marathon was held here, which made people feel the fireworks on the banks of the Suzhou Creek.

2023 Shanghai Suzhou River Half Marathon

The theme of this year’s competition is "Shanghai-style fireworks land, new vitality of Suzhou River", which connects industrial civilization and modern creative buildings spanning over a hundred years in series. The starting point of the competition is located in Tian ‘an Qianshu (Moganshan Road) in Dayang Jingdian, and the end point is Banmashu Park (Yunling East Road). The track passes through Putuo landmark buildings such as Tian ‘an Qianshu, Shanghai Mint Museum and Banmashu Park, with a total length of 21.0975 kilometers and a total of 4,000 participants. Runners can feel the red genes, industrial civilization, development vitality, colorful life and pleasant ecology along the "semi-Masu River".

2023 Shanghai Suzhou River Half Marathon

The 2023 Shanghai Suzhou River Half Marathon is the first time to be held. As one of the stops in the Shangma series, the "Suzhou River Half Horse" puts the safety of runners in the first place. At the start and end of the track and along the way, in addition to street security personnel, 917 volunteers from Putuo District and universities in Shanghai, together with more than 130 referees, woven a safety net. During the competition, the public security traffic management department took traffic control measures on some roads involving the track. The police of the traffic police detachment of Putuo Public Security Bureau rode motorcycles to clear the way, and the police of the special police detachment rode police bicycles to escort the whole competition.

The event was hosted by Shanghai Sports Federation, Putuo District People’s Government and Donghao Lansheng (Group) Co., Ltd.

Infrastructure for training AI to solve common problems

In order to train artificial intelligence models that can solve common problems, infrastructure is needed to provide support. These infrastructures are usually composed of hardware, software and tools to improve the efficiency and accuracy of model training. This article will introduce the infrastructure for training AI to solve common problems.

I. Hardware infrastructure

When training artificial intelligence models, it is usually necessary to use high-performance computing hardware to provide support. The following are several common hardware infrastructures:

  1. CPU: The central processing unit (CPU) is a general-purpose computing hardware, which can be used to run various types of software, including artificial intelligence models. Although the performance of CPU is relatively low, it is still useful in training small models or debugging.

  2. GPU: A graphics processor is a special computing hardware, which is usually used to process images and videos. Because of its highly parallel structure, GPU can provide higher computing performance than CPU when training artificial intelligence models, so it is widely used.

  3. TPU: Tensor processor is a kind of hardware specially used for artificial intelligence computing, developed by Google. The performance of TPU is higher than that of GPU, and it is suitable for large-scale artificial intelligence model training and reasoning.

Second, the software infrastructure

In addition to hardware infrastructure, some software tools are needed to support the training of artificial intelligence model. The following are some common software infrastructures:

  1. Operating system: Artificial intelligence models usually need to run on an operating system, such as Linux, Windows or macOS.

  2. Development environment: Development environment usually includes programming language, editor and integrated development environment (IDE) for writing and testing artificial intelligence models. Common development environments include Python, TensorFlow, PyTorch and Jupyter Notebook.

  3. Frames and libraries: Frames and libraries provide some common artificial intelligence model algorithms and data processing tools, making model development and training more convenient. Common frameworks and libraries include TensorFlow, PyTorch, Keras and Scikit-Learn.

Third, the tool infrastructure

In addition to the hardware and software infrastructure, some tools are needed to support the training of artificial intelligence models. The following are several common tool infrastructures:

Dataset tool: Dataset tool is used to process and prepare training datasets, such as data cleaning, preprocessing, format conversion, etc. Common data set tools include Pandas, NumPy and SciPy.

2 Visualization tools: Visualization tools are used to visualize the training process and results to help users better understand the performance and behavior of the model. Common visualization tools include Matplotlib, Seaborn and Plotly.

Automatic parameter tuning tool: The automatic parameter tuning tool is used to optimize the parameters of the model to improve the performance and accuracy of the model. Common automatic parameter tuning tools include Optuna, Hyperopt and GridSearchCV.

In short, training artificial intelligence models to solve common problems requires the use of a variety of infrastructures, including hardware, software and tools. These infrastructures are designed to improve the efficiency and accuracy of model training, so that the model can better solve various practical problems. In practical application, users need to choose the appropriate infrastructure according to specific requirements and data characteristics, and design and implement it accordingly.