News on China's scientific and technological development.

Watch out Elon Musk.

China-based Geely Automobile launches LEO satellites​

Nuying Huang, Taipei; Adam Hwang, DIGITIMESMonday 13 June 2022


Credit: Geely

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Future space battles.
A research team in China said that an anti-satellite system has mastered the art of deception in a simulated space battle.

In the experiment, the AI commanded three small satellites to approach and capture a high-value target, repeating the exercise thousands of times.

Eventually the targeted satellite learned to detect the incoming threat and fired up powerful thrusters to evade the pursuit.

But it was then lured into a trap after the AI ordered the three hunters to veer off their original trajectory, as if giving up the pursuit.

One of the hunting satellites then suddenly changed course and deployed a capturing device from a distance of less than 10 metres (33 feet).

Chinese students’ dream device defeats Japan’s most powerful supercomputer in world contest ​

• DepGraph Supernode, started as a training project at Huazhong University of Science and Technology in Wuhan, was nearly twice as fast as nearest competitor
• Secrecy of Chinese authorities regarding their large computers makes it difficult to accurately rank supercomputers from around the world, according to experts

A small computer developed by Chinese students outperformed Japan’s most powerful machine in solving a major complex data problem related to artificial intelligence, according to the latest global ranking.​

Supercomputer Fugaku in Japan has nearly 4 million CPU cores, making it the second-largest computer ever built.​

DepGraph Supernode, which was started as a “training project” by graduate students at Huazhong University of Science and Technology in Wuhan, has 128 cores.​

But the DepGraph was nearly twice as fast as Fugaku in solving a single source shortest path (SSSP) problem, a difficult graph problem affecting the performance of artificial intelligence in a wide range of sectors, according to the annual Graph500 ranking released by the International Supercomputing Conference early this month.​

“It felt like a dream,” first-year graduate student Shen Qiange said, according to a report by China Science Daily on Monday.​

The average age of Shen and her teammates was 24.​

Mathematicians often used graphs to describe relations. The simplest graph can contain just two dots with a line between them.​

More complex graphs have been applied in many areas, such as financial markets with a large number of listed companies, global social media platforms or war games.​

AI can detect hidden relations or discover a pattern of evolution in a sophisticated, constantly changing graph.​

But the training and learning process usually involves a large number of calculations. Even some seemingly easy tasks, such as finding the shortest path between two dots, poses a huge challenge to computers.​

The Wuhan team said they discovered a bottleneck issue that could severely affect computers on this type of job.​

A supercomputer uses many CPU – central processing unit – cores to execute many calculation tasks simultaneously, according to a paper the team submitted to the IEEE Symposium on High-Performance Computer Architecture last year.​

But when handling a graph-related problem, the calculation process in a core often depends on the outcome of another.​

This dependency disrupted the calculation process, the paper said. Most of the time the cores had to either wait or re-do calculations with new results from another core.​

The DepGraph machine solved the problem with a new structure and software that could take the performance of each core to the limit by reducing the chaos caused by dependency, the team said.​

“Don’t underestimate the ability of students,” said Zhang Yu, associate professor of computer science and adviser to the project team.​

“This is the first time a single computer defeated a cluster of computers in graph calculation,” he added.​

Professor Jin Hai, a mentor to the students, said the young researchers had worked closely with China’s hi-tech companies. Their biggest inspirations came from the industry.​

“The problem definitely originated from the industry. The research meets the most urgent needs of our country,” he was quoted by the China Science Daily as saying.​

Zhao Jin, a PhD candidate and team leader, said they had to report their progress to Chinese tech giant Huawei Technologies every two weeks.​

“Master’s and doctoral students can get access to the topics of the national key research and development plan. This opens our eyes instantly and integrates us more closely with the industry,” he said.​

But Zhao said they also enjoyed a high degree of freedom.​

“We find our research topic by ourselves. The supervisors do not tell the students what to do, but support us in the direction we are interested in. This is very stimulating,” he added.​

Fugaku recorded top performance in breadth-first search (BFS), another major graph problem with an algorithm that was simpler but required more raw calculation resources to solve, according to the Graph500.​

Sunway TaihuLight, a Chinese supercomputer built in 2015, ranked second in that category, with the DepGraph ranking ninth among the giant machines.​

China has built some of the world’s largest computers, including some exascale machines that could rival the US Department of Energy’s Frontier, currently the fastest machine in open record.​

Chinese researchers have conducted unprecedented experiments such as the world’s largest training for AI on these new machines, according to openly available information.​

But the Chinese authorities have kept their performance secret.​

The reliability of international ranking on supercomputers has been affected by China’s absence, according to industrial experts.​

Strangelove said:

The average age of Shen and her teammates was 24.​

Click to expand...

The most painful part about the computer field is being a world-renown and respected professor in your prime leading the nation's top efforts and then having your ass handed to you by some college kid without any idea how it happened.

Strangelove said:

Professor Jin Hai, a mentor to the students, said the young researchers had worked closely with China’s hi-tech companies. Their biggest inspirations came from the industry.

Click to expand...

This is some groundbreaking stuff. On a side note, I was confused for some time because I kept reading "Professor Jai Hind".

China made a breakthrough on recycling spent nuclear fuel and improving the safety of generating nuclear power.

China’s ‘Particle Beam Cannon’ Is a Nuclear-Power Breakthrough​

It promises to recycle spent nuclear fuel, making it cheaper and less dangerous—and moving Beijing toward energy independence.

Click to expand...

China's supercomputer can use AI to quicken drug discovery



The Tianhe-2 supercomputer in south China's Guangdong Province. /CFP

Using artificial intelligence (AI) and one of the world's fastest supercomputers, Chinese scientists are engineering otherwise unknown chemicals that can be clinically used in the future.

The Tianhe-2 supercomputer in south China's Guangdong Province, ranking among the global top 10 fastest computers in the TOP 500 listing published this month, has been used as a platform for drug discovery. Now, AI-based algorithms make the machine even smarter.

Scientists from Sun Yat-sen University and Beijing-based AI startup Galixir, along with those from the Georgia Institute of Technology and the Massachusetts Institute of Technology, reported a practical deep-learning toolkit to predict the biosynthetic pathways for natural products (NPs) or NP-like compounds in Tianhe-2.

Natural products are the primary source of clinical drug discovery. More than 60 percent of FDA-approved small molecule drugs in the United States are NPs or their derivatives.

Over 300,000 NPs have been recorded to date, but owing to the complex production know-how, only one-tenth have been developed as a substrate or product, with the computer-aided screening urgently needed.

In a recent study published in Nature Communications, the researchers presented a tool called BioNavi-NP to propose NP biosynthetic pathways from simple building blocks in an optimal fashion, which requires no already-known biochemical rules.

Firstly, a single-step bio-retrosynthesis prediction model is trained to generate candidate precursors for a target NP. The full data-driven model achieves a prediction accuracy 1.7 times more precise than the previous rule-based model, according to the study.

Then, an automatic retro-biosynthesis route planning system efficiently samples plausible biosynthetic pathways.

The study reveals that the toolkit can successfully identify biosynthetic pathways for 90.2 percent of 368 test compounds.

Also, the researchers combined an existing enzyme prediction tool to provide a user-friendly, open-to-public web server that can predict biosynthetic pathways. It can also score the biological feasibility of those pathways based on the estimated preference of species and enzymes.

Inputting any relevant NP molecules into the online toolkit, one can obtain multiple predicted ways to synthesize them in a few minutes.

The quick results are only made possible by Tianhe-2's strong parallel computing capability and its customized GPU resources, which help shorten the training and testing time from more than two weeks to one day. China's supercomputer Tianhe-2 has been widely used to promote research in health and medicine.

New carbon form has good potential​

By ZHANG ZHIHAO | CHINA DAILY | Updated: 2022-06-17 09:57

Chinese scientists have synthesized a new form of carbon known as monolayer polymeric fullerene, which has exhibited good crystallinity and stability that would make it a useful material candidate for electronics, catalysts and quantum computing, according to a study published in the journal Nature on Wednesday.

Fullerene, sometimes referred to as "buckyballs", is a cage-like structure made of 60 connected carbon atoms that resembles the shape of a hollow soccer ball.

Despite its complex form, this allotrope of carbon is found in nature and space, and has been the subject of intense research, especially in materials science, electronics and nanotechnology, since its discovery in 1985.

The new material is essentially a sheet of connected fullerenes, which scientists have been trying to synthesize for decades, said Zheng Jian, a researcher from the Institute of Chemistry of the Chinese Academy of Sciences who led the research team that made the breakthrough.

Carbon is the basis of life and is one of the most common and studied elements in the world. A study of pure carbon would not seem exciting to a layperson, but it is a vibrant field that has yielded many rewards and applications.

The scientists who discovered fullerene won the Nobel Prize in chemistry in 1996 for their work. The 2010 Nobel Prize in physics was awarded to scientists who discovered graphene, a single layer of carbon atoms arranged in a honeycomb lattice.

Fullerene has been used in the medical field in the design of highperformance contrast agents for X-ray and magnetic resonance imaging. Graphene is used in next generation biomedicine, coatings, electronics, sensors and energy technologies.

"Our work is significant because it adds a new member to the carbon allotrope family, joining the likes of diamond, graphite, graphene and carbon nanotubes," Zheng said.

"It also opened up a new research field in two-dimensional carbon materials, and the synthesis technique we developed, which doesn't require complex reactions and can work in atmospheric pressure, could provide a unique perspective in exploring new carbon materials," he added.

The new carbon allotrope has shown to be a good semiconductor material, and is incredibly stable, even at a temperature as high as 326.85 C, he said.

Therefore, the material has good application potential in optics, electronics, superconducting, quantum computing, information storage and catalysts, but more research is needed to probe its industrial practicality, he said.