In the northern region, the rain and snow are cooling down, and many places in beginning of winter are welcome to start the heating season

  Beijing, Nov. 8 (Lang Lang) Today is beginning of winter in the 24 solar terms. For people in the northern region, the feeling of seasonal change is becoming more and more obvious. According to the weather forecast, in the next few days, due to the influence of cold air, there will be rain and snow in most parts of the north, and some parts of the northwest will be accompanied by dust.

Data Map: People in Taiyuan, Shanxi Province wear thick clothes to travel. China News Service reporter Wu Junjie photo

  Many places in the north welcome beginning of winter in the rain and snow.

  According to the forecast of the Central Meteorological Observatory, from the night of the 7 th to the 11 th, there were small to moderate rains (snow) or sleet from west to east in northern Xinjiang, eastern northwest, north China, Huanghuai and Northeast China, and there was heavy snow or blizzard in the local area; There are 4~6 winds and 7~9 gusts in most of the above areas, accompanied by dusty weather in the local area; The process is cooled by 4~6℃, and the local temperature reaches 8~12℃.

  Around beginning of winter (7-10 days), new cold air will strike, which will bring 4 ~ 6℃ cooling to the north, with a wide range of influence, basically affecting most of the north.

  According to the China Weather Network, it is expected that there will be a precipitation cooling process from west to east in northwest, north China and northeast China, and there will be light rain (snow) or sleet in northern Xinjiang, and there will be local snowstorms. There is little to moderate rain (snow) or sleet in the central and eastern Inner Mongolia, North China and Northeast China. Most of these areas are accompanied by 4 ~ 6℃ cooling, and the temperature in northern Xinjiang is 8 ~ 10℃.

  Specifically, on the 7 th, the temperature in Beijing dropped, with the lowest temperature at night being only 1 C and the highest temperature during the day being 12 C. There is occasional light rain on the 9th night. In Hebei, according to the forecast, from the night of the 9th to the 10th, there will be light rain in the cloudy, central and northern parts of Hebei province and moderate rain in the local area, including light snow or sleet in Zhangjiakou and Chengde, moderate to heavy snow in the local area and light rain or sporadic rain in other areas.

On October 31, Xining City ushered in the first snowfall in the second half of the year. Ma Mingyan

  Beginning of winter ≠ When will the real winter come?

  Beginning of winter is the 19th solar term in a year and the first solar term in winter, which generally falls on November 7th & mdash; 8th.

  Although beginning of winter has arrived, it doesn’t necessarily mean the arrival of winter. In meteorology, the average daily temperature for five consecutive days is below 10℃ as the standard for the start of winter. According to this standard, most parts of northern China have entered winter before beginning of winter, while many parts of southern are still in autumn.

  For example, in Northeast China, as early as late October, Shenyang Meteorological Observatory has issued a blue warning signal for cold wave. Since October 25th, the moving average temperature has decreased steadily by 10℃, and it has entered the winter in the meteorological sense, which is 13 days later than the previous year (October 12th) and 1 day earlier than last year (October 26th).

  For the northern region, after beginning of winter, cold air activities will gradually become more frequent, and the downward trend of temperature will accelerate. Every time cold air visits, it will bring obvious cooling, strong wind and even rain and snow, and the first snow in some parts of North China often falls at this time. Compared with the north, which is accelerating the pace of winter, it seems that it will take some time for people in the south to feel the cold. According to the Central Meteorological Observatory, it will be difficult for cold air to cross the Yangtze River in the coming week.

November every year to March next year is the heating season in the north, and it is also the peak period of gas consumption. Four "Shaanxi-Beijing pipelines" built from Shaanxi to Beijing by different routes are the "gas supply lifeline" of the capital. The picture shows that on October 28th, employees of Miyun Offtake Station are inspecting the equipment. China News Service reporter Sultan photo

  Cold air frequently opens the heating season in many places in North China.

  The continuous exertion of cold air has also led to the heating season in the northern region. At present, central heating has been started in many places in North China.

  According to the information released by Beijing, on November 7, the regional boiler rooms in Beijing were ignited for trial operation, and the cogeneration heating enterprises basically completed the area investment of residential users of heating network.

  In Tianjin, Tianjin follows 2018— After heating in advance in the heating season of 2019, it will be heated half a month in advance again. With the approval of Tianjin Municipal Party Committee and Municipal Government, Tianjin Public Utilities Administration requires all coal-fired boilers to be ignited and started on October 28th, and formally meet the standard for heating on November 1st. From 2018— In the heating season of 2019, the official heating in Tianjin was not only advanced by half a month, but also delayed by half a month, extending the heating period that ended on March 15 to March 31.

  In Hebei province, heating season will be started in many places in Hebei province in November. For example, Qinhuangdao will officially provide heating at 0: 00 on November 5, Shijiazhuang will officially provide heating on November 15, and the whole network of the city will reach the standard for heating trial operation on November 10. On November 15, Langfang, Baoding, Tangshan and Cangzhou will officially provide heating.

Tenghahe said that Degea’s passing performance is problematic and needs attention. In addition, Tenghahe also talked about Dean Ma and Cupid.

This article is translated from TA’s team LW: Erik Ten Hag Addresses David de Gea’s poor kicking display against real betis:’ From today I can’t ignore it’

Manchester United manager Hach said that he could not ignore Degea’s poor performance in the game against Betis, and the Spanish goalkeeper had difficulty in getting the ball out of the backcourt.

Tenghahe believes that Degea’s passing ability has improved this season, but he raised concerns before Sunday’s home game against Southampton.

Degea’s footwork was very poor in the first leg of Manchester United’s game against Betis Europa League 16, especially in the first half, but Manchester United won 4-1.

In a serve, Degea passed the ball directly to Juanmi, but pellegrini’s team failed to take advantage of the opportunity.

When asked if Degea can improve this problem, Tenghahe replied: "From today on, I can’t ignore this problem, but I think we have seen him perform very well in many competitions."

"I don’t know what the reason is. At that time, there was a strong wind and different football was used. Maybe he had some problems in this respect, but I know that we can handle this problem well and he will perform better on Sunday. "

"We have trained in this area, but Degea is also working hard. I think we have seen him improving this season and he will continue to improve, I believe. "

Tenghahe said that although Dean Ma has returned to training, they must be cautious about this player to avoid being injured again.

When asked if he was worried that Dean Ma would be injured again, Tenghahe said, "Yes, but we have always had a good plan, and so far it has not always worked."

Although Dean Ma may return to the lineup, they will carefully manage his health.

In addition, cupid, who has been absent for several months due to injury, is another player who has attracted much attention.

Tenghahe expressed confidence in Eriksson’s injury. Eriksson suffered an ankle injury in Manchester United’s FA Cup match against Reading on January 28th, but the midfielder is expected to return in April.

Tenghahe said: "I think he will definitely play again."

"But I’m not sure when he can come back, but his recovery progress is not bad, as planned. We will definitely see him again, but not before the international break. "

"Yes, he participated in the discussion of the team. But at present, his main concern is to come back as soon as possible because we need all the players. "

Manchester United will face the challenge of Southampton, which is currently ranked 19th in the league.

AI decision is not controlled? Past lives who knows AI in one minute.

Artificial intelligence (AI) means that machines simulate human intelligent behavior by means of learning, reasoning and automation technology. The rise of AI industry originated from the field of computer science, and began to develop since 1950s, but the period of real rapid development was in recent years, especially after 2010. This paper will make a deep analysis of the AI industry from the perspectives of the development history of the AI industry, the upstream and downstream interpretation of the industrial chain, the analysis of key companies and people, and the analysis of industry hotspots.

First, the development process of AI industry

Since 1950, the AI industry has started research and experiments for decades, but it didn’t really enter the outbreak period until after 2010. In 2010, Watson of IBM was in Jeopardy! Beating two famous players in the game attracted global attention. Since then, AlphaGo has defeated the top players in the Go game, which proves the powerful ability of AI technology. In 2017, AlphaGo Zero developed by Google’s DeepMind company defeated the previous AlphaGo, showing a higher level of intelligence. In addition, with the progress of hardware technology, the application of AI industry has become more and more extensive, such as face recognition, autonomous driving, smart home and other fields, which has brought the development of AI industry into a new stage.

Second, the upstream and downstream interpretation of the industrial chain

The industrial chain of AI industry can be divided into three parts: hardware, algorithm and application. In terms of hardware, it mainly includes chips, sensors, servers and other equipment. The algorithm part is the core of AI technology, mainly including machine learning, deep learning, natural language processing and other technologies. The application part is the landing application of AI technology, involving artificial intelligence medical care, smart home, autonomous driving, smart city and other fields.

In the upstream of the industrial chain, chip manufacturers are an important part of the AI industry. At present, the world’s major AI chip manufacturers include Intel, NVIDIA, AMD, Broadcom and other companies. The chip products of these companies can be used in cloud computing, Internet of Things and other scenarios. In the middle reaches, AI algorithm companies are key players, such as Baidu, Google, IBM, Microsoft and other companies have their own AI algorithm research teams. In addition, there are some AI companies that focus on a certain field, such as artificial intelligence medical companies. Downstream, AI application companies are the main components of the AI industry chain, including artificial intelligence medical care, smart home, autonomous driving and other companies. These companies have applied AI technology to actual scenes, which has brought great commercial value.

Third, the analysis of key companies and people

In the AI industry, some key companies and figures have attracted much attention. Among them, Google and Facebook are considered as leading companies in the AI field. Google has strong strength in artificial intelligence research. Through its AI subsidiary DeepMind, Google has made many achievements in the fields of AlphaGo and AlphaZero. Facebook, on the other hand, has shown great strength in the fields of face recognition and speech recognition. By acquiring companies such as Face.com and Wit.ai, Facebook has made many attempts in the field of AI.

In addition, Andrew Ng is considered as one of the important figures in the field of artificial intelligence. He was the leader of Google’s artificial intelligence team and played an important role in some AI projects of Google. Later, he joined Baidu and founded Baidu’s Artificial Intelligence Research Institute, transforming Baidu from a traditional search engine company into an AI company.

Fourth, analysis of industry hotspots

The hot issues in the AI industry mainly involve technology, safety and ethics. In terms of technology, the development of artificial intelligence still faces some challenges, such as how to improve the efficiency of machine learning and how to deal with the problem of data scale. In terms of security, the black box problem of AI has always been concerned, that is, the decision-making process of AI model is opaque and difficult to explain. Ethical issues are also an important topic in the AI industry, such as how to ensure that the development of artificial intelligence is in line with the interests of mankind, how to avoid the negative impact of AI on society, and so on.

In addition, autonomous driving is also an important hot spot in the AI industry. Autopilot technology has a wide application prospect in automobile, logistics and other fields. However, the realization of autonomous driving technology still faces many technical challenges, such as how to deal with complex traffic environment and how to ensure the safety of vehicles.

In short, with the continuous development of AI technology, the prospect of artificial intelligence industry is very broad. At the same time, the development of AI technology also needs the joint efforts of society to ensure that the application of AI is in line with the interests of mankind and avoid negative impacts on society.

Quantum machine learning: the intersection of quantum computing and artificial intelligence

With the continuous progress of technology, the concept of quantum computing is more and more widely known. As a new computing paradigm, quantum computing is very different from traditional computing methods. It can deal with problems that traditional computers can’t handle, which makes quantum computing have broad application prospects in the field of artificial intelligence. Quantum machine learning, as an important field where quantum computing and artificial intelligence intersect, has a wide and far-reaching application prospect. This paper will introduce the basic concept, principle and application of quantum machine learning, and analyze its future development trend.

First, the basic concepts of quantum machine learning

Quantum machine learning is a technology that uses quantum computing to realize machine learning. Its main purpose is to use the advantages of quantum computing to deal with problems that traditional computers can’t handle and improve the efficiency and accuracy of machine learning. The main difference between quantum machine learning and traditional machine learning is that it uses qubits to store and process data instead of classical bits used in traditional machine learning.

Second, the principle of quantum machine learning

The principles of quantum machine learning mainly include quantum data coding, quantum state preparation and quantum algorithm design. Among them, quantum data coding is the process of coding classical data into quantum States, so that the efficiency and accuracy of machine learning can be improved by using the characteristics of superposition and entanglement of quantum States. Preparation of quantum states is a process of putting qubits into the required quantum states. By controlling and operating qubits, the conversion between different quantum states can be realized, thus realizing various algorithms in machine learning. The design of quantum algorithms is the process of designing and implementing quantum algorithms, which can be optimized on quantum computers, thus achieving the purpose of machine learning.

Third, the application of quantum machine learning

Quantum machine learning is widely used, including classification, clustering, regression, dimensionality reduction and other fields. Here are some applications:

  1. Quantum neural network

Quantum neural network is a new type of neural network, which uses quantum bits to store and process data. Quantum neural network can deal with complex nonlinear problems, which makes it have a wide application prospect in image recognition, speech recognition and other fields.

  1. Quantum support vector machine

Quantum support vector machine is a support vector machine algorithm based on quantum computing, which can process high-dimensional and nonlinear data sets faster and improve the accuracy and efficiency of classification. Quantum support vector machine is widely used in bioinformatics, image processing, financial forecasting and other fields.

  1. Quantum clustering

Quantum clustering is a method to realize clustering analysis by quantum computing, which can process a large number of data faster and improve the accuracy of clustering. Quantum clustering is widely used in biology, image processing, market analysis and other fields.

Quantum dimensionality reduction is a method to realize dimensionality reduction analysis by quantum computing, which can process high-dimensional data faster and reduce the complexity and storage space of data. Quantum dimensionality reduction is widely used in data mining, image processing, natural language processing and other fields.

Fourth, the future development trend of quantum machine learning

With the continuous progress of quantum computing technology, the application prospect of quantum machine learning will be more and more extensive. In the future, the development trend of quantum machine learning mainly includes the following aspects:

  1. Further improvement of hardware technology

At present, the performance of quantum computer needs to be improved, and the development of hardware technology will help to improve the efficiency and accuracy of quantum machine learning.

  1. Innovation of algorithm design

With the deepening and development of quantum machine learning theory, algorithm design will become more and more important. In the future, quantum machine learning algorithms will be more complex and efficient.

  1. Expansion of application scenarios

With the continuous expansion of the application scenarios of quantum machine learning, the future will involve more fields, including physics, chemistry, biology, finance, transportation and so on.

To sum up, quantum machine learning, as an important field where quantum computing and artificial intelligence intersect, has a very broad application prospect. In the future, quantum machine learning will continue to develop and innovate in hardware technology, algorithm design and application scenarios, thus bringing more benefits and development opportunities to human society.