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1905 movie network news　On January 21st, the film Detective Chinatown 3, written and directed by Chen Sicheng, exposed a set of "brain-burning" posters. Wang Baoqiang, Haoran Liu, Tsumabuki Satoshi, Tony Jaa, Masami Nagasawa, Tadanobu Asano, Miura Youhe, Shang Yuxian and Zhang Zifeng all appeared "brain-burning". In the face of the brand-new case in Tokyo, nine actors have been caught in brainstorming, not only sweating, but also smoking hot smoke on their heads, and they are very happy with their "burned out" look.

After Bangkok and new york, the third stop of the "Tang Detective" series came to the hot spot of reasoning — — Tokyo. Wang Baoqiang and Haoran Liu led the gathering of "Asian All-Star Team", which continued the style of "comedy+reasoning" to the end and solved the problem of the secret room together. The film Detective Chinatown 3 will be released nationwide on January 25th.

　Detective Chinatown 3’s "Brain Burning" Poster-Haoran Liu

Asian stars burn their brains together

Tang Tan’s "Comic Reasoning Style" continues to enjoy the volume.

In "Tang Detective 3", Wang Baoqiang and Haoran Liu, the "Tang Detective Group", welcomed a new partner, and joined hands with Tsumabuki Satoshi, a Japanese detective, and Tony Jia, a Thai detective, to form the "Asian Detective Union" to celebrate Tokyo. Not only that, Masami Nagasawa, Tadanobu Asano, Tomokazu Miura and old friends Shang Yuxian and Zifeng Zhang also joined in. Three generations of powerful actors from "old, middle and young" gathered together, and the international lineup was very eye-catching.

In the poster of this exposure, the actors hold their heads, hold their cheeks and frown … … The appearance of all the staff burning their brains is very funny. Not only did they stage a case-solving scene without a clue, but the familiar feeling of Tang Tanxi also came to my face. Wang Baoqiang also said in the interview, "Compared with the first two films, the third film has stronger visual impact, richer comedy elements and denser laughter."

　Detective Chinatown 3’s "Brain Burning" Poster-Tsumabuki Satoshi

"Tang Tan 3" was filmed in four languages.

Chen Sicheng’s directing depends entirely on "acting"

Asian stars compete on the same stage, not only colliding with many unexpected jokes in the film, but also the scene is full of joy. According to Haoran Liu, four languages, Chinese, Japanese, Thai, and sometimes English, are often sounded at the shooting scene of "Tang Tan 3". In order to save time in language translation, director Chen Sicheng basically relied on acting when he spoke the play, and told everyone the play by personal demonstration, playing all the important roles in the film.

Chen Sicheng’s "self-directed and self-acting" shooting enthusiasm has also infected actors from other countries. Tsumabuki Satoshi said, "When reading the script, I feel that his ideas are particularly interesting, and the live performance will surpass the script. This is a very happy thing." Tony Jaa bluntly said, "He will give a demonstration to all the actors. He is like a fire and burns everyone’s heart."

　Detective Chinatown 3’s "Brain Burning" Poster-Tony Jaa

It is reported that the film Detective Chinatown 3 will be released on New Year’s Day in 2020.

The heart of the machine column

Machine zhixin editorial department

Recently, researchers from monash university in Australia, ant group, IBM research institute and other institutions have explored the application of model reprogramming in large language models (LLMs). It also puts forward a brand-new perspective: efficiently reprogramming the large language model for general time series prediction-its proposed Time-LLM framework can realize high-precision time series prediction without modifying the language model, surpassing the traditional time series model in multiple data sets and prediction tasks, and making LLMs perform well in dealing with cross-modal time series data, just like an elephant dancing!

Recently, inspired by the big language model in the field of general intelligence, the new direction of "big model+time series/spatio-temporal data" has made a lot of relevant progress in generate. At present, LLMs has the potential to completely change the way of time series/spatio-temporal data mining, thus promoting the efficient decision-making of typical complex systems such as cities, energy, transportation and remote sensing, and moving towards a more universal intelligent form of time series/spatio-temporal analysis.

Large-scale models, such as language and other related basic models, can be trained and skillfully readjusted to deal with time series and spatio-temporal data in a series of general tasks and applications in special fields. Source: https://arxiv.org/pdf/2310.10196.pdf.

Recent studies have expanded large-scale language models from dealing with natural languages to time series and spatio-temporal tasks. This new research direction, that is, "large model+time series/spatio-temporal data", has given birth to many related developments, such as LLMTime directly using LLMs for zero-sample time series prediction and reasoning. Although LLMs has powerful learning and presentation ability, and can effectively capture complex patterns and long-term dependencies in text sequence data, as a "black box" focusing on natural language, its application in time series and spatio-temporal tasks still faces challenges. Compared with traditional time series models such as TimesNet and TimeMixer, LLMs can be compared with "elephant" for its huge parameters and scale.

Therefore, how to "tame" LLMs, which is trained in the field of natural language, so that it can process numerical sequence data across text patterns and exert its powerful reasoning and prediction ability in time series and spatio-temporal tasks has become the key focus of current research. Therefore, a deeper theoretical analysis is needed to explore the potential pattern similarity between language and time series data, and effectively apply it to specific time series and spatio-temporal tasks.

This paper expounds how to make general time series prediction by reprogramming LLM Reprogramming. It puts forward two key technologies, namely, (1) reprogramming the time sequence input and (2) prefixing the prompt, which transforms the time sequence prediction task into a "language" task that can be effectively solved by LLMs, and successfully activates the ability of large language model to do high-precision time sequence reasoning.

Address: https://openreview.net/pdf?. id=Unb5CVPtae

Paper code: https://github.com/KimMeen/Time-LLM.

1. Background of the problem

Time series data exist widely in reality, among which time series prediction is of great significance in many dynamic systems in the real world and has been widely studied. Different from natural language processing (NLP) and computer vision (CV), a single large-scale model can handle multiple tasks, and the time series prediction model often needs to be specially designed to meet the needs of different tasks and application scenarios. Although the basic model based on pre-training has made great progress in NLP and CV fields, its development in time series field is still limited by data sparsity. Recent research shows that large language models (LLMs) have reliable pattern recognition and reasoning ability when dealing with complex tag sequences. However, how to effectively align the two modes of time series data and natural language, and how to use the reasoning ability of large language model itself to deal with the task of time series analysis is still a challenge.

2. Overview of the paper

In this work, the author puts forward Time-LLM, which is a general framework of LLM Reprogramming. LLM can be easily used for general time series prediction without any training for the large language model itself. Time-LLM first reprogrammed the input time series data with Text Prototypes, and expressed the semantic information of the time series data by using natural language representation, and then aligned two different data modes, so that the large language model could understand the information behind the other data mode without any modification.

In order to further enhance LLM’s understanding of input time-series data and corresponding tasks, the author puts forward a paradigm of Prompt-as-Prefix (PaP), which fully activates LLM’s processing ability in time-series tasks by adding additional context prompts and task instructions before time-series data representation. In this work, the author has carried out sufficient experiments on the mainstream time series benchmark data sets, and the results show that Time-LLM can surpass the traditional time series model in most cases, and has greatly improved the learning tasks of Few-shot and Zero-shot.

The main contributions in this work can be summarized as follows:

1. This work puts forward a new concept of time series analysis by reprogramming large language model, without any modification to the main language model. The author shows that time series prediction can be regarded as another "language" task that can be effectively solved by ready-made LLM.

2. This work puts forward a general language model reprogramming framework, namely Time-LLM, which includes reprogramming the input time series data into a more natural text prototype representation, and enhancing the input context through declarative prompts (such as domain expert knowledge and task description) to guide LLM to conduct effective cross-domain reasoning. This technology provides a solid foundation for the development of multi-modal time series basic model.

3. The performance of Time-LLM in mainstream forecasting tasks always exceeds the performance of the best existing models, especially in scenes with few samples and zero samples. In addition, Time-LLM can achieve higher performance while maintaining excellent model reprogramming efficiency. Greatly release LLM’s untapped potential in time series and other sequential data.

3. Model framework

As shown in ① and ② in the model frame diagram above, the input time series data is normalized by RevIN, and then cut into different patch and mapped to the hidden space.

There are significant differences between time series data and text data in expression, and they belong to different modes. Time series can neither be directly edited nor described in natural language, which brings great challenges to prompting)LLM to understand time series. Therefore, we need to align the time series input features to the natural language text domain.

A common method to align different modes is cross-attention, as shown in ③ in the model frame diagram, which only needs to make a cross-attention for the embedding and time sequence input features of all words (in which the time sequence input feature is Query, and the embedding of all words is Key and Value). However, LLM’s inherent vocabulary is very large, so it can’t directly align the time series features to all words effectively, and not all words have aligned semantic relations with time series. In order to solve this problem, this work combines vocabularies linearly to obtain text prototypes, in which the number of text prototypes is much smaller than the original vocabulary, and the combination can be used to represent the changing characteristics of time series data, such as "short rise or slow decline", as shown in the above figure.

In order to fully activate LLM’s ability to specify time-series tasks, this work puts forward the paradigm of prompt prefix, which is a simple and effective method, as shown in the model frame diagram. Recent progress shows that other data patterns, such as images, can be seamlessly integrated into the prefix of prompts, thus making effective reasoning based on these inputs. Inspired by these findings, in order to make their methods directly applicable to real-world time series, the authors put forward an alternative question: Can hints be used as prefix information to enrich the input context and guide the transformation of reprogramming time series patches? This concept is called Prompt-as-Prefix (PaP). In addition, the author also observed that it significantly improved LLM’s adaptability to downstream tasks and supplemented the reprogramming of patches. Generally speaking, it means that some prior information of time series data set is used as prefix prompt in the way of natural language, and it is spliced with aligned time series features to feed to LLM. Can it improve the prediction effect?

The figure above shows two prompting methods. In Patch-as-Prefix, the language model is prompted to predict the subsequent values in the time series, which are expressed in natural language. This method encounters some constraints: (1) Language models usually show low sensitivity when dealing with high-precision numbers without the assistance of external tools, which brings great challenges to the accurate processing of long-term forecasting tasks; (2) For different language models, complex customized post-processing is needed, because they are pre-trained on different corpora and may use different word segmentation types when generating high-precision numbers. This leads to the prediction expressed in different natural language formats, such as [‘0′,’.’,’ 6′,’ 1′] and [‘0′,’.’,’ 61′], representing 0.61.

In practice, the author identified three key components to construct effective hints: (1) the context of data set; (2) Task instruction, which makes LLM adapt to different downstream tasks; (3) Statistical descriptions, such as trends and time delays, enable LLM to better understand the characteristics of time series data. The following figure shows an example of a prompt.

4. Experimental effect

We have conducted a comprehensive test on eight classic public data sets in long-range forecasting. As shown in the following table, the benchmark comparison of Time-LLM is significantly superior to the previous best results in the field. In addition, compared with GPT4TS which directly uses GPT-2, Time-LLM which adopts the idea of reprogramming and prompts as-prefix is also significantly improved, which shows the effectiveness of this method.

In addition, we evaluated the zero-shot learning ability of reprogrammed LLM within the framework of cross-domain adaptation. Thanks to the reprogramming ability, we fully activated LLM’s prediction ability in cross-domain scenarios. As shown in the following table, Time-LLM also showed extraordinary prediction effect in zero-shot scenarios.

Step 5 summarize

The rapid development of large-scale language models (LLMs) has greatly promoted the progress of artificial intelligence in multimodal scenes, and promoted their wide application in many fields. However, LLMs’s huge parameter scale and its design mainly aimed at natural language processing (NLP) scenarios have brought many challenges to its cross-modal and cross-domain applications. In view of this, we put forward a new idea of reprogramming large model, aiming at realizing the cross-modal interaction between text and sequence data, and this method is widely used to deal with large-scale time series and spatio-temporal data. In this way, we expect LLMs to be like a flexible dancing elephant, which can show its powerful ability in a wider application scenario.

Interested friends are welcome to read the paper (https://arxiv.org/abs/2310.01728) or visit the project page (https://github.com/KimMeen/Time-LLM) for more information.

This project has been fully supported by NextEvo, the AI innovation R&D department under the Intelligent Engine Division of Ant Group, especially thanks to the close cooperation between the language and machine intelligence team and the optimization intelligence team. Under the leadership and guidance of Zhou Jun, Vice President of Intelligent Engine Division, and Lu Xingyu, Head of Intelligent Optimization Team, we have successfully completed this important achievement hand in hand.

1905 movie network news According to the data of Lighthouse Professional Edition, as of 17: 22 on October 4, the box office list of new National Day movies in 2023 (including point screening and pre-sale) has reached 2.3 billion yuan. The competition is fierce, and it is temporarily in the top three in the rankings.

The director’s "Rock Solid" continued the box office growth momentum of the previous days, temporarily ranking first with 755 million yuan. The addition of many powerful actors such as,,,,,, adds a lot of highlights to this film. The infighting between Zhang Guoli and Yu Hewei in the play outlines various crises hidden behind the appearance of a prosperous city.

With the theme of the emotional state of contemporary urbanites, "Ex-4: Early Marriage" drives the box office with its high reputation. Meng Yun, who plays, tries a blind date, while Yu Fei, who plays, and his girlfriend create their own "marriage cooling-off period" to preview their marriage life in advance. The audience gained laughter and moved, but also had a deeper understanding of feelings. As of press time, "Ex-4: Early Marriage" has reached 585 million at the box office, temporarily ranking second.

The war giant "Volunteer Army: Attack by Heroes" has always attracted people’s attention with the highest score in the box office list of the new National Day movies in 2023. As of press time, the box office of the film has reached 423 million, temporarily ranking third. As the first part of the trilogy "Volunteers", the film tells the story from the decision to send troops to Chinese people’s Volunteer Army’s entry into the DPRK and the first and second battles in a more grand narrative way. It fills the blank of similar works in conception and perspective.

The box office adapted from real events continued to grow steadily, reaching 311 million, temporarily ranking fourth. The extreme confrontation between harmony and others has become a topic of great interest to the audience. Sports comedies temporarily ranked fifth, with a box office of 91.512 million as of press time.

　　Hollow vermicelli is an ancient traditional pasta snack. There is an instant noodle museum in Yokohama, Japan. Zhang Hui

　　As the saying goes: "The heaven and the earth in the bowl are wide, and the Kun is twisted in the face." As a representative of ancient food, noodles have traversed both the east and the west, as well as the ancient and modern times. The Silk Road was once called the "noodle road" because of its spread, and chopsticks in Chinese tableware culture were probably born of it. Until today, noodles are still popular in the east and west, and they have won the top spot in modern fast food culture, and even been sent to outer space.

　　All the way to the east, noodle culture swept across East Asia.

　　The story of noodles should start from the "noodle empire" — — China said. China has a vast territory. Thousands of years of cooking smoke have bred more than 2,000 kinds of noodles, which has influenced the noodle culture in East Asia. However, neither noodles nor wheat, the main raw material for making noodles, originated here.

　　China’s hieroglyph "Mai" originally refers to "people from afar", and when combined with the word "noodle", it becomes "noodle" (the traditional Chinese character of "noodle"), and noodles made of wheat and wheat flour did come from afar thousands of years ago. Wheat, with an annual output of 600 million tons, has become the staple food for 60% of the population on the earth. It was first born in the farming civilization in Mesopotamia 9000 years ago. In 6500 years after its birth, wheat planting technology and flour making technology began to spread from the Middle East to the Mediterranean, across the Balkans and Alps, and eastward to China through Central Asia. Therefore, this "Silk Road" starting from Chang ‘an and passing through Gansu, Xinjiang, Central Asia, West Asia and even Mediterranean countries is also called "Noodle Road".

　　Arab merchants traveling along the Silk Road carry dough with them as dry food. Before eating, they divide the dough into small pieces, rub it into strips to dry, and bake it on the fire. This is almost the embryonic form of early noodles in human history. Such noodles entered the mainland of China along the Silk Road and began to flourish in the Central Plains. However, the Central Plains people, who like soup, cooked noodles with water, and when noodles were taken from hot soup, chopsticks were born.

　　Although China is not the earliest birthplace of noodles, it is the earliest place where noodles have been discovered. More than 4,000 years ago, a sudden earthquake destroyed a village in the northwest of China, followed by a flood that froze it. More than 4,000 years later, exactly in 2005, archaeologists discovered noodles about 50 cm long and 3 mm wide exposed in the rock stratum in Lajia Village, Qinghai Province, known as "Pompeii, China". But at that time, its name was probably not called "noodles".

　　In China, the earliest noodles were called "cakes" or "soup cakes". Qi Min Yao Shu, 1400 years ago, recorded for the first time the "water-inducing method" of making noodles — —

　　"It’s as big as a bamboo stick, and a foot is broken. If the plate is flooded, it’s advisable to put your hand on it and cook it as thin as leek leaves."

　　"It’s like a big finger, two inches is broken, and it’s appropriate to dip it in a basin. It’s advisable to slap it to the side of the basin by hand and cook it in a hurry."

　　To put it simply, "water-led noodles" are noodles that are drawn to the thickness of chopsticks, pinched into a foot shape, and then quickly kneaded into flat strips on the steam of a boiling pot, cooked and topped with chicken soup. The method of making delicious noodles is similar to that of introducing noodles with water, but the shape is more like flat noodles, but the taste is "smooth and beautiful", which is the earliest noodle soup in the Central Plains.

　　When noodles were first introduced to Europe, China’s noodles had entered a dazzling period of prosperity. Kaifeng, the capital of the Song Dynasty, was also known as the "noodle capital". This international city with a population of 500,000 (compared with 100,000 in Paris in the same period) saw the earliest commercial street in the world. The restaurants in the commercial street appeared more than 500 years earlier than the large European restaurants. Handmade noodles, knife section, Lamian Noodles … … There are more than 30 mainstream noodle practices in restaurants. Noodles that entered the mainland of China along the Silk Road spread northward and eastward from here, entered Mongolia, South Korea and Japan, and entered Vietnam, Thailand and Indonesia southward. Noodles culture swept across East Asia and Southeast Asia.

　　The manual "dough-pressing machine" made by the Han people (putting dough into a groove, opening a number of small holes under the groove, and squeezing the noodles out of the small holes by pressure through the lever principle) is still used in North Korea, Bhutan and other countries. Since the Tang Dynasty, slender noodles have a beautiful meaning of longevity and continuity in Chinese food culture, so a bowl of longevity noodles is indispensable for a birthday girl. The same color head is also popular in Asian noodle circles: South Korea should have noodles at the banquet on the wedding day, which means "grow old together and last forever"; Thailand eats noodles on traditional celebration days in order to "have endless happy events"; Japanese people are popular to eat vegetarian noodles on the seventh day of July to pray for "good luck", and they also eat noodles on New Year’s Eve, which is called "Nianyue Noodles" — — This kind of thin and long food, like a bond, started from China and contacted the kitchens of Asian countries.

　　The most typical transmission of pasta culture is Japan. Also in the Song Dynasty, a Japanese monk named "Yuan Er Bian Yuan" (later called "Noodle Monk") returned from Lin ‘an. In addition to the Buddhist scriptures of Zen Buddhism, there is also a design of a grinding machine with wheels driven by waterwheels in his luggage. The technology of grinding noodles from China, together with the fashion of eating noodles, spread to Japanese temple kitchens through Japanese monks. Noodles made of wheat or buckwheat flour, cooked and boiled with Shanghai algae and mushrooms, Japanese monks are only allowed to make a sound when eating noodles, and the spread of noodles from monks to Japanese folk has also formed a unique custom: the louder the sound when eating noodles, the more polite it is.

　　All the way west, spaghetti changed from aristocratic cuisine to civilian cuisine.

　　Compared with the history of bread in Europe for tens of thousands of years, noodles came late, and spaghetti was first mentioned in a medical book in the 13th century.

　　Italy is a unique existence in the European countries where bread is the staple food. They raise the "national noodle" spaghetti to the height of the national symbol. Every day, when it comes to meals, almost the whole country is full of the same thing — — Spaghetti Spaghetti is an important staple food for Italians, so that the appetizer is called "antipasto" (meaning before noodles). It is said that Mussolini, who loves spaghetti very much, promised the soldiers every time he mobilized before the war: "There will be a plate of delicious noodles later."

　　Since its birth, there have been hundreds of patterns behind the name spaghetti: fine noodles, spiral noodles, cat’s ear noodles, macaroni … … Add artichokes, meat sauce, mushroom sauce, olive oil or cheese powder ingredients to change thousands of combinations. So who brought this kind of noodles to Europe and changed the diet pattern in Italy and even Europe?

　　China people want to believe that it is Kyle Poirot. Born in Venice in the 14th century, this merchant descendant had a prominent family because he was engaged in selling spices. At the age of 15, he came to China to travel around and enjoy the local customs of Chinese culture, and did not return to his native land until ten years later. There is a saying that Kyle Poirot saw all kinds of noodle making when he traveled around China, so he brought the noodle making technology from China back to Italy.

　　However, in Kyle Polo’s Travels of Marco Polo, his description of noodles has never been found. On the contrary, in the doctor’s prescription of Genoa, another Italian city, in 1244, historians found the advice that "patients with fragile stomachs should try to eat less Terry". The "Terry" here was proved to be spaghetti, and this record was decades earlier than Kyle Poirot’s return to China. So Kyle Poirot was not the one who introduced noodles to Italy.

　　Another story comes from the Italians themselves, who pushed the history of this national food back to the Roman Empire, thinking that Etruscans had already started eating noodles at that time. However, in the archaeological records of Roman diet, only "miscellaneous vegetable soup, seafood, grilled fish and bread similar to pizza" can be confirmed so far, and noodles are still hard to find.

　　So the real matchmaker should still be Arabs. Just as Arab merchants introduced noodles to China people on the Silk Road and then introduced them to the Central Plains, in 827 AD, the Aghlabid dynasty in North Africa captured Sicily at the southern tip of Italy, and brought the method of "Persian dried noodles" similar to knife noodles to the island. Dry noodles are a kind of dry food which is convenient to store, and are very suitable for camel traders or Islamic armies who travel long distances. Dry noodles need to be exposed to the sun, and the climatic conditions in Sicily are just right for them. As a result, Italy is not only used to this slender pasta, but also uses the convenience of maritime trade to sell dry noodles that are not easy to deteriorate to all parts of Europe. As a handwritten geography book in Arabia said when introducing Sicily in 1154, "They make linear Itri (spaghetti) here and sell this food to the world."

　　Similarly, just like the custom that China people began to use chopsticks after eating noodles, European aristocrats felt embarrassed when eating spaghetti with soup and juice, so they began to use forks. Noodles once again reshaped the tableware culture. But interestingly, Arabs, who are used to grasping food with their hands, began to eat only short noodles gradually for the convenience of eating, but the slender long noodles disappeared from their diet.

　　In Europe, even though the price of noodles in the Middle Ages was four times that of bread, people never gave up. Noodles have been a luxury in Europe for more than 400 years, and the groups that can be enjoyed are limited to European popes and nobles, and then slowly expanded to the gradually prosperous middle class. The noodle makers are almost all women, and most of them are nuns from monasteries. Only they can finely knead, cut and dry the dough in such leisure time. Take Italy’s "helicoid" as an example: "The finest, whitest and smallest thing, but it also costs the most. The dough must be kneaded, divided into strips, and then rolled into snails by skillful girls. "

　　In 17th-century Italy, spaghetti was introduced to all walks of life from aristocratic high-class cuisine, from the kitchens of nobles and bishops to mobile stalls on the street. The improvement of kneading machines in the 19th century, the progress of milling technology in the 20th century and the appearance of electric engines made the price of wheat drop continuously. The "white skill" of noodles became a "white processing industry", and flour and noodles became cheaper day by day. Industrially produced noodles make it a popular diet all over the world, especially in big cities with rapid population concentration.

　　Instant noodles, a fast food in modern society.

　　Edo was a rapidly expanding metropolis in the 18th century, with a population of nearly 1 million at its peak. In order to ensure the loyalty of local nobles, Tokugawa asked Daming to live in Edo every two years, and their wives and concubines had to stay in Edo as hostages. Daming and his family needed a decent palace to live in Edo. At that time, Edo City gathered the most exquisite craftsmen and craftsmen in the country, and built buildings and streets. These industrial and commercial classes, known as "machi people", have promoted Japanese pasta culture, among which soba noodles are the most typical.

　　A large number of civilians live in narrow wooden "town houses", and cooking with a fire is easy to cause fires. The needs of "town people" are often solved in the form of eating outside, and the mobile food stalls called "houses" are their "kitchens and restaurants". In 1804, 6,065 snack bars were registered in Edo. In addition to traditional Japanese food such as tempura and sushi, these snack bars also sell instant buckwheat noodles. After ordering, the buckwheat noodles that have been watered in advance are gently picked up from the pot, and a spoonful of boiled soup is added to the bowl, which can be handed to the diners on the side (they usually stand at the stall and finish a bowl of noodles quickly). The whole process takes less than three minutes, which can be said to be the originator of the fast food industry.

　　Standing to eat noodles can also be seen everywhere in Japanese ramen restaurants that once rose after World War II. Roll the noodles into long strips and fold them in half. Grab one end with one hand and pull the other hand in the middle. Dance your hands back and forth to tighten the dough and then overlap. Every time it is pulled, the number of noodles is multiplied by two, and it is also thinner. Lamian Noodles’s technology originally originated in Gansu, China, and local alkali is the best material to strengthen the elasticity of noodles. Japan does not produce alkali, but learns from China’s Lamian Noodles method to add alkaline water, so Lamian Noodles was originally called "Chinese Lamian Noodles" in Japan. In Japan after World War II, millions of people poured into a metropolis like Tokyo from the countryside because of post-war reconstruction. Like the laborers in the Edo period, their houses were narrow and it was difficult for them to have their own kitchens and cooking time, so the new Japanese Lamian Noodles appeared like mushrooms after rain. No matter in the period of rapid economic rise or after the bursting of the economic bubble, Lamian Noodles fever remained in the limelight. At present, there are 40,000 Lamian Noodles shops in Japan, including thousands in Sapporo, the capital of Lamian Noodles. When a noodle restaurant arrives for a meal, there are often long queues.

　　Noodles are very popular. Besides being delicious and cheap, there is another beauty. The beauty of this is that it can be prepared in advance, and it is an ever-changing taste to cook temporarily with different toppings and soups, so it has an innate advantage in Asian fast food culture. However, there is a Japanese named Ando Baifu who hopes to eat noodles more quickly. The standardized actions of two businesses, heating with water and mixing seasonings, can also be completed instantly in an ordinary family without or without using the kitchen.

　　He tried repeatedly in the kitchen in his backyard, and finally found that the moisture in noodles could be completely drained by frying (in medieval Italy, they spent several days or even a week drying), so that noodles could be preserved for a long time, and at the same time, the fried holes could quickly absorb water when injecting water. In 1958, Ando Baifu introduced the first batch of instant noodles to the market with the product of "Chicken Lamian Noodles", and with the help of the introduction of TV in 1959 and the offensive of Tokyo Olympic Games in 1964, this kind of "cheap, delicious and easy to cook" noodles was publicized. Especially, in the fire of Osaka Chihiro Department Store in 1972, free cup noodles were provided for firefighters, which made this instant noodle deeply rooted in people’s hearts. The Japanese media even praised Ando Baifu’s instant noodles as the greatest invention in Japan in the 20th century, and Ando himself benefited a lot from it. The Nissin Group founded by him now has an annual income of 250 million euros. According to the statistics and prediction of Christopher Nahart, the author of The Book of Pasta, "In 2005, human beings consumed 85 billion portions of instant noodles a year, and everyone ate 12 cups on average. It will reach 100 billion in 2010. " Whether young people living alone, Antarctic delegations or even astronauts in the space station are enjoying a bowl of noodles quickly in the form of instant noodles.

　　Passing the East and the West along the Silk Road, with the ever-changing food styles in various places, has also grasped the rhythm in the rise of modern cities — — Noodles have been developing beyond imagination for more than 4,000 years. It is the best footnote to "dry Kun in the face" that lasts forever.

　　Variety, Shanxi people’s pasta.

　　If China is the "country of pasta", then Shanxi is the "city of pasta".

　　Noodles are the staple food of Shanxi people, and they are often eaten three times a day. Shanxi people are good at dough making techniques, such as knife cutting (first rolling the dough thin, then cutting it into strips with a knife), pulling pieces (tearing the rolled dough into pieces), cat ears (first tearing the dough into pieces, and then rubbing it into a cat’s ear shape with the thumb tip on the palm of your hand), picking tips (unique to Shanxi, using a chopstick to pull out strips from the kneaded soft dough and directly put them into the pot), and Lamian Noodles. Direct cooking), river fishing (scraping noodles with a scraper) and spiral noodles (kneading into short spirals with the palm of your hand), the flour used for making noodles, such as wheat, rye, rye, barley, buckwheat, potato, corn, all kinds of beans and rice, is combined with steaming, frying, roasting, frying, stewing, frying and pasting. According to Beijing Daily