China’s “Tianyan” is a 500 meter spherical radio telescope Telescope), or fast for short, is located in the karst depression of dawodang, Kedu Town, Pingtang County, Qiannan Buyei and Miao Autonomous Prefecture, Guizhou Province. The project is a major national science and technology infrastructure. The “Tianyan” project consists of active reflector system, feed support system, measurement and control system, receiver and terminal, and observation base. On January 11, 2020, the 500m spherical radio telescope passed the national acceptance and was put into operation.

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This major scientific and technological achievement was proposed by Chinese astronomer Nan Rendong in 1994 and completed in 22 years. It was completed and put into use on September 25, 2016. Led by the National Astronomical Observatory of the Chinese Academy of Sciences, it is the world’s largest single aperture and most sensitive radio telescope with independent intellectual property rights in China. Its comprehensive performance is ten times that of the famous radio telescope Arecibo.

However, searching for extraterrestrial civilization is only one of the seven scientific objectives of “China Tianyan”:

1. Fast has the ability to extend the observation of neutral hydrogen to the edge of the universe and reproduce the early images of the universe.
2. It can discover thousands of pulsars in one year, establish pulsar timing array, and participate in autonomous navigation and gravitational wave detection of pulsars in the future.
3. Leading the international very long baseline interferometry network to obtain the hyperfine structure of celestial bodies.
4. Carry out high-resolution microwave inspection to detect weak spatial signals.
5. Participate in the search for extraterrestrial civilization.
6. Participate in meridian chain engineering to improve the performance of incoherent scatter radar dual system.
7. Extend the deep space communication ability to the outer planets of the solar system, and increase the satellite data receiving ability by 100 times.

“China’s heavenly eye” has already opened the search for extraterrestrial civilization

As early as 2018, China Tianyan installed and debugged back-end equipment dedicated to extraterrestrial civilization search, according to science and technology daily. This function is a bit like the back-end equipment of the sieve. It mainly selects useful narrow-band candidate signals from the vast electromagnetic signals of “China Tianyan”, and excludes the celestial bodies and artificial signals.

“At present, ‘China Tianyan’ is upgrading its back-end equipment, and it is expected to launch new observations in September.” Zhang Tongjie revealed that “China Tianyan” is preparing a new observation plan, at which time, the search for extraterrestrial civilization will also be launched simultaneously. However, due to the synchronic observation mode, the alien search will not interfere with the normal scientific observation of the “Chinese sky eye”.

Is it really a reliable and serious scientific research to search for aliens with “Chinese sky eye”? Zhang Tongjie’s answer is “yes”. “The research and exploration of extraterrestrial civilization is absolutely not the business of astronomers and science fiction writers, but the serious task of astronomers, only by scientific means.”

Compared with the so-called “dark forest theory”, Zhang Tongjie advocated “dark sea theory”. “Sailing in the dark rough sea, if you see a little starlight in the distance, it’s from another sailboat. At this time, do people hunt and kill each other like hunters passing through the dark forest, or do they keep watch and help each other? ” Zhang Tongjie tends to the latter. He believes that the search for extraterrestrials is not only of scientific significance, but also of more practical significance for the future space migration plan of mankind.

Samsung’s first quarter operating profit fell 7 trillion won ($570 million) month on month, driven by the same factors that dragged down revenue. Operating profit increased by KRW 0.2 trillion (US $160 million) compared with a year ago, due to improved product mix of mobile business and further diversification of the company’s customer base in the field of mobile OLED.

In the first quarter, the trend of foreign exchange had little impact on Samsung’s overall operating profit, as the positive impact of the stronger dollar and euro against the Korean won was mainly reflected in the parts business, offset by the weakness of major emerging market currencies.

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Thanks to strong demand for servers and personal computers, and steady demand from mobile devices, Samsung’s memory business has improved. The profit of logic chip business increased as the supply of mobile components to major customers increased, while the profit of OEM business decreased as the demand for high performance computing (HPC) decreased in China.

In terms of display business, China’s sales volume decreased due to the seasonal weakness and related production stoppage caused by the new crown epidemic, the revenue of mobile display business decreased, while the loss of large panel business narrowed.

In spite of weak sales at the end of the first quarter, Samsung’s profits in its mobile communications business increased from the previous quarter and the same period last year. The increase in profits was due to improved product mix, launch of the flagship Galaxy S20 and reduced marketing costs.

Samsung’s consumer electronics division reported lower earnings as a result of seasonal weakness and the new crown epidemic affecting quarterly results. Compared with the previous year, due to the pricing pressure caused by increased competition, the profit of Samsung TV business decreased, while the home appliance business improved under the strong support of new high-end product sales.

Looking forward to the second quarter, Samsung expects the memory business to remain stable, but the overall profit may decline compared with last quarter, as the new crown epidemic will significantly affect the demand for several core products.

For component businesses, memory demand for servers and PCs is expected to remain strong as more people work from home, but the mobile market is likely to be weak. Revenue from OLED screens may be weaker as the smartphone market stalls.

Sales and profits in the packaged products business, including smartphones and TVs, are expected to fall sharply as the outbreak affects demand and causes the closure of stores and factories around the world. To solve this problem, Samsung will take advantage of its global production flexibility and supply network and strengthen its online sales capabilities.

In the second half of this year, the uncertainty caused by the new crown outbreak will continue, because the duration and impact of the outbreak are not clear. Samsung plans to focus on optimizing resource allocation in the short term, and at the same time continue to strengthen its technological leadership and develop innovative package products.

For memory business, Samsung plans to respond to market changes through flexible investment and portfolio adjustment. For OLED, it will actively meet the needs of new product release and expand its presence in new applications such as foldable displays.

The mobile communication business aims to strengthen its product portfolio by introducing new high-end models and expanding 5g models for the mass market. The network business will focus on developing technology and improving global competitiveness to strengthen 5g business.

For the consumer electronics sector, under the current risk of economic uncertainty, Samsung will pay close attention to the market situation and will continue to work to minimize the negative impact by investing in effective marketing and promotion activities tailored to each region and optimizing its logistics.

Tesla’s acquisition of deepscale, a computer vision start-up, began to pay off, with the acquisition of a full team to start offering new patents to the EV manufacturer.

At the end of 2019, it was reported that Tesla bought DeepScale, a start-up company in the Gulf of San Francisco. The company focused on developing “deep neural network” for autopilot, and the amount of the purchase was not disclosed. Deepscale focuses on the deep learning system of computing energy saving, which is also the focus area of Tesla’s attention. Tesla decided to design its own computer chip to drive the automatic driving software. There is speculation that Tesla acquired the team to accelerate the development of its machine learning.

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Now Tesla has released a new patent called “systems and methods for training machine models with extended data”, and we see the results of this acquisition. The inventor of the patent includes three members of deepscale, namely Matthew Cooper, Paras Jain and hasimran Singh Sidhu.

The DeepScale team currently working under Tesla is trying to apply the system to training neural networks with data from several different sensor observations, such as simultaneous interpreting of eight cameras in the Autopilot sensor array of the Tesla driver assistance system.

Tesla described the difficulty of this situation in the patent application: “in a typical machine learning application, data can be expanded in many ways to avoid over fitting the feature model of the capture device used to obtain training data. For example, in simultaneous interpreting of typical image sets for training computer models, images may represent objects of many different capturing environments, which have different sensor characteristics associated with the captured object. For example, such an image can be captured by different sensor characteristics, such as different scales, focal length, lens type, preprocessing or post-processing, software environment, sensor array hardware and so on. These sensors may also differ in different external parameters, such as the position and orientation of the imaging sensor relative to the environment in which the image was captured. All these different types of sensor characteristics will lead to different forms of captured images in the image set, making it more difficult to train the computer model correctly. “

In response, the Tesla team summarized their solutions to this problem:

The first part is used to train and predict a set of parameters of computer model. The embodiment can include: 1) identifying an image captured by a group of cameras, the image is attached to one or more image collection systems; 2) identifying a training output of the image for each image in the image collection; 3) generating an enhanced image for one or more images in the group through the following specific steps: maintaining the camera attributes of the image The image manipulation function modifies the image to generate an enhanced image for the group of images, and associates the enhanced training image with the training output of the image; 4) the parameters of the training prediction computer model predict the training output based on the image training set including the image and the group of enhanced images.

The second part includes a non transient computer storage medium system with one or more processors and storage instructions. When the instructions are executed by one or more processors, the processors will perform related operations, including: 1) identifying the image set captured by a group of cameras and attached to one or more image acquisition systems at the same time; 2) for the image set For each image in the combination, recognize the training output of the image; 3) for one or more images in the group, generate an enhanced image for a group of images through the following steps: generate an enhanced image for the group of images by modifying the image processing function that maintains the camera attributes of the image, and associate the enhanced training image with the training output of the image; 4) train Predict the parameter set of computer model to predict the training output based on image training set, including image and enhanced image set.

The third part includes a non transient computer-readable medium with instructions for the processor to execute, which enables the processor to: 1) recognize a group of images captured by a group of cameras and attached to one or more image acquisition systems at the same time; 2) recognize the training output of each image in the group; 3) recognize the group of images For one or more images in an image, an enhanced image is generated for a group of images by modifying the image operation function of maintaining the camera attributes of the image to generate an enhanced image for a group of images, and associating the enhanced training image with the training output of the image; 4) training the computer model to learn image training based on the image including the image and the group of enhanced images Set to predict training output.

As previously reported, Tesla is experiencing “a major fundamental rewrite of Tesla autopilot.”. As part of the rewrite, CEO Elon Musk said: “neural networks are absorbing more and more problems.”

The new patent will also include a tagging system, which musk says will change the rules of the game: a car enters a scene with eight cameras, draws a road, and then you can mark that road in 3D. This new method of training machine learning system with multiple cameras, like Tesla’s autopilot, can be an autopilot update with additional data.