Japan’s Subaru Telescope, ATERUI II supercomputer join forces to reveal a clear Universe

Japanese astronomers have developed a new artificial intelligence (AI) technique to remove noise in astronomical data due to random variations in galaxy shapes. After extensive training and testing on large mock data created by supercomputer simulations, they then applied this new tool to actual data from Japan’s Subaru Telescope and found that the mass distribution derived from using this method is consistent with the currently accepted models of the Universe. This is a powerful new tool for analyzing big data from current and planned astronomy surveys. Artist’s visualization of this research. Using AI driven data analysis to peel back the noise and find the actual shape of the Universe. (Credit: The Institute of Statistical Mathematics)

Wide area survey data can be used to study the large-scale structure of the Universe through measurements of gravitational lensing patterns. In gravitational lensing, the gravity of a foreground object, like a cluster of galaxies, can distort the image of a background object, such as a more distant galaxy. Some examples of gravitational lensing are obvious, such as the “Eye of Horus”. The large-scale structure, consisting mostly of mysterious “dark” matter, can distort the shapes of distant galaxies as well, but the expected lensing effect is subtle. Averaging over many galaxies in an area is required to create a map of foreground dark matter distributions.

But this technique of looking at many galaxy images runs into a problem; some galaxies are just innately a little funny-looking. It is difficult to distinguish between a galaxy image distorted by gravitational lensing and a galaxy that is actually distorted. This is referred to as shape noise and is one of the limiting factors in research studying the large-scale structure of the Universe.

To compensate for shape noise, a team of Japanese astronomers first used ATERUI II, one of the world’s most powerful supercomputers and dedicated to astronomy, to generate 25,000 mock galaxy catalogs based on real data from the Subaru Telescope. They then added realist noise to these perfectly known artificial data sets and trained an AI to statistically recover the lensing dark matter from the mock data.

After training, the AI was able to recover previously unobservable fine details, helping to improve our understanding of the cosmic dark matter. Then using this AI on real data covering 21 square degrees of the sky, the team found a distribution of foreground mass consistent with the standard cosmological model.

“This research shows the benefits of combining different types of research: observations, simulations, and AI data analysis.” comments Masato Shirasaki, the leader of the team, “In this era of big data, we need to step across traditional boundaries between specialties and use all available tools to understand the data. If we can do this, it will open new fields in astronomy and other sciences.”

Spanish-German team's computational model simulates movements of hominids via water routes

Scientists from the interdisciplinary research center “The Role of Culture in Early Expansions of Humans” (ROCEEH), funded by the Heidelberg Academy of Sciences and based at the Senckenberg Research Institute and Natural History Museum Frankfurt, modeled for the first time together with a Spanish-German team, the movements of our early ancestors under the inclusion of waterways. The model, presented in the scientific journal “PLOS ONE,” allows the configuration of behavioral scenarios that illustrate different biological and cultural stages of water crossing by hominids. It was developed in the agent-based modeling laboratory of ROCEEH in Frankfurt, Germany. The newly developed model makes it possible for the first time to simulate the water crossing of hominids on a small scale. In their simulation, the researchers sent one thousand individuals (red dots) on their “journey.” Graphic: Senckenberg

According to the “Out-of-Africa” theory, the genus Homo first appeared in Africa about 2.8 million years ago before spreading from there across the entire world. “However, it is often difficult to understand in detail how these movements took place. As a rule, there are only very large-scale models for the migration routes of our early ancestors,” explains Ericson Hölzchen, lead author of the ROCEEH study at the Senckenberg Research Institute and Natural History Museum in Frankfurt, and he continues, “What is certain is that the hominids had to cross bodies of water of different sizes on their migration – but whether and how they were able to do so, without the use of modern maritime technology, has not yet been conclusively clarified. Yet, this is essential for the discussion of potential migration routes.” 

Hölzchen and a Spanish-German team have now closed this gap: A new model they developed – in the “agent-based modeling” laboratory of ROCEEH under the leadership of Dr. Christine Hertler in Frankfurt – makes it possible for the first time to simulate the water crossing of hominids on a small scale. In their simulation, the researchers sent one thousand individuals on their “journey” and equipped them with different, adaptable abilities as well as 45,000 energy units. “Our model hominids have different means of negotiating water barriers: directed swimming, paddling, drifting, or on a raft. In addition, other parameters – such as the width of the water barrier, the water temperature, or the current – can also be adjusted in the simulation,” adds Hölzchen.

The various factors can then be used to derive a “crossing success rate” (CSR), i.e., the probability that the crossing will succeed or fail. “By applying the CSR, we can use small-scale movement decisions to compare different behavioral scenarios and their effects on crossing success,” adds the bioinformatician from Frankfurt.

The researchers show that in two of the modeled scenarios – by directed swimming or with the aid of a raft – the early representatives of the genus Homo were able with a high probability of success to cross-straits up to 15 kilometers wide, such as the Strait of Gibraltar, or wide rivers, such as the Ganges.

"Accordingly, expansion across water barriers is not unlikely and should also be considered in larger-scale models,” Hölzchen adds in summary, and he provides an outlook, “In the future, our model may serve as a template for expansion scenarios involving other natural barriers such as mountains or deserts. Thus, we will be able to gain an increased understanding of how our ancestors have spread from the ‘cradle of mankind’!"

Computer scientist Jin wins Germany's highest international research award Humboldt Professorship for artificial intelligence at Bielefeld University

Bielefeld University has been awarded its second Alexander von Humboldt Professorship. This time, it goes to the computer scientist Professor Dr. Yaochu Jin. He is one of the world's leading experts on evolutionary algorithms--a form of artificial intelligence (AI) that optimizes its own capabilities. In autumn 2021, Jin will move from the University of Surrey (UK) to Bielefeld University. The Humboldt Professorship enables researchers who have previously been working abroad to take up a professorship at a German university where they can conduct pioneering research. It is the most highly endowed international research award in the country. Yaochu Jin will receive 3.5 million euros in prize money over a period of five years. It was announced today (01.07.2021) that a total of six new Humboldt Professorships have been selected. Jin is one of three award winners who will be honored with the award for their research on AI. Professor Dr. Yaochu Jin will start conducting research as a Humboldt Professor at Bielefeld University in October 2021. As part of the research award, he will receive 3.5 million euros. Photo: Pei An  CREDIT Photo: Pei An

'We are delighted that the Alexander von Humboldt Foundation is honoring the university's previous research achievements on human-centered artificial intelligence with this award and ensuring that Yaochu Jin will enrich our research in this field with his expertise,' says Professor Dr. Gerhard Sagerer, rector of Bielefeld University. Yaochu Jin will take up his professorship in the Faculty of Technology. 'He will play a prominent role in developing new research networks and, in particular, in networking the Faculty of Technology and the Medicine School OWL. This can include, for example, developing self-learning systems for medical analyses and thus creating innovations for personalized medicine,' Gerhard Sagerer explains.

Evolutionary algorithms as the key to future technologies

Yaochu Jin's research can be used not only for medical services but also for numerous other applications such as solving industrially significant problems. These may range from the interaction between robots to the design of vehicles.

All these applications are based on evolutionary algorithms that use principles of natural evolution to solve technical problems. One use of Jin's research results is to apply such principles to multi-objective optimization--for example, to achieve not only accurate but also robust and energy-efficient solutions in the field of swarm robotics and engineering design.

Connected to Bielefeld University for more than a decade

At Bielefeld University's Faculty of Technology, Yaochu Jin will establish a new working group 'Nature-Inspired Computing and Engineering'. He and his team will also be setting up a research laboratory equipped with hardware for morphogenetic robotics. In this form of robotics, shaped decisively by Jin, who is viewed as one of the pioneers in the field of swarm robotics, robots evolve in self-organized ways. The morphogenetic self-organization mechanism, inspired by biological morphogenesis, enables robots to self-organize as a group in order to solve a problem that a single robot would not be able to cope with alone.

'I am looking forward to continuing my work at Bielefeld University in the future and helping to further develop the Bielefeld approach to cognitive interaction technology,' says Yaochu Jin. He has been associated with the East Westphalian university for a long time. In recent years, he has consulted several Bielefeld doctoral students with their projects. Before that, from 2007 to 2010, he was a lead investigator at the graduate school of the university's Research Institute for Cognition and Robotics (CoR-Lab) that was run as a partnership between Bielefeld University and the Honda Research Institute Europe (Offenbach). 'During that time, I experienced how to open scientists in Bielefeld are, to cooperative and interdisciplinary research. In addition, there is continuous collaboration with external companies and research institutions. These provide a perfect environment for creating technical solutions that actually work in practice.'

Innovations in problem-solving for complex scenarios

'One aspect that played a special role when nominating Yaochu Jin for the Humboldt Professorship is that he complements the research profile of the Faculty of Technology, particularly through his focus on the multi-objective optimization of AI systems,' explains Professor Dr Barbara Hammer, chair of the Machine Learning research group. Hammer was instrumental in preparing Yaochu Jin's nomination. 'Expertise in multi-objective optimization is essential for efficient problem solving in complex scenarios. It is thus not only highly relevant in various fields such as modular robotics, medicine, and bioinformatics, but also in economics and machine learning,' says Hammer, who has collaborated with Jin several times in the past. For example, both are active members of the IEEE Computational Intelligence Society, an international professional association. 'I am especially looking forward to doing research with Yaochu Jin on innovations in federated learning and online machine learning.'

Professor Dr.-Ing. Yaochu Jin, born in China, began his academic career at Zhejiang University in China, where he worked as an associate professor following his doctorate in 1996. After research stays at the Ruhr University Bochum and the State University of New Jersey, USA, he conducted research at Honda R&D Europe and Honda Research Institute Europe, both in Offenbach, Germany, from 1999 to 2010. In 2010, he moved to the University of Surrey, United Kingdom, as a professor, and became one of 14 Distinguished Chair Professors at Surrey in 2019. He was also a Finland Distinguished Professor at the University of Jyväskylä, Finland for three years. Jin's research has been recognized with numerous awards. For example, he has received five Outstanding Paper Awards from the IEEE Computational Intelligence Society and has been appointed an IEEE Distinguished Lecturer several times. In November 2015, he was elevated to an IEEE Fellow for contributions to evolutionary optimization.

Research award helps to attract top international researchers

The Alexander von Humboldt Professorship has been offered since 2008. It is the most highly endowed research award in Germany--it grants 5 million euros for academics doing experimental and 3.5 million euros for those doing theoretical research. The award is granted by the Alexander von Humboldt Foundation and funded by the Federal Ministry of Education and Research. With the Humboldt Professorship, the Foundation wants to enable German universities to raise their own profile in the global competition. It gives universities the opportunity to offer top researchers internationally competitive conditions. At the same time, the award includes an obligation to offer the new Humboldt Professors a long-term perspective for their research in Germany.

The first Humboldt Professorship at Bielefeld University was awarded to the mathematician Professor Dr William Crawley-Boevey in 2016. He is considered a luminary in his field--the representation theory of algebras. He moved to Bielefeld from the University of Leeds (UK).