Computer Science

What actually happens is much weirder, and may help us understand more about quantum mechanics

A research team led by Lingnan University's School of Interdisciplinary Studies Assistant Professor Xiao Hang recently devised a “crystal language” that enables artificial intelligence (AI) to create new materials with desired properties (Figure 1). This groundbreaking discovery holds the potential for creating the next revolutionary semiconductor material that makes solar energy abundant and affordable, and opens possibilities for the development of novel superconductors that effortlessly transport clean electricity with zero losses. The findings of this breakthrough research have been published in the prestigious academic journal Nature Communications, which is also the first time Lingnan University scholar published a lead-author paper on the esteemed Nature series.

Researchers have developed a high-speed, high-sensitivity terahertz-wave detector operating at room temperature, paving the way for advancements in the development of next generation 6G/7G technology.

- Professor Sung-hoon Lim’s team at DGIST has developed a metric learning technology that increases 3D object estimation performance, a key self-driving technology - Commercialization of self-driving is expected due to the reduced self-driving framework installation cost

- Professor Sunghoon Im's team at DGIST has developed depth-sensing technology that can be reliably used in diverse environments by analyzing the relationship between the neural network structure and distance sensing in autonomous driving. - The new technology is expected to contribute to the development of a wide range of AI fields, such as autonomous driving, which must be performed safely in various urban settings and weather conditions

A research team at Osaka Metropolitan University has fabricated a gallium nitride (GaN) transistor using diamond, which of all natural materials has the highest thermal conductivity on earth, as a substrate, and they succeeded in increasing heat dissipation by more than two times compared with conventional transistors. The transistor is expected to be useful not only in the fields of 5G communication base stations, weather radar, and satellite communications, but also in microwave heating and plasma processing.

Prof Joe Qin, President and Wai Kee Kau Chair Professor of Data Science of Lingnan University, and Prof Sam Kwong Tak-wu, Associate Vice-President (Strategic Research) and Chair Professor of Computational Intelligence, have been elected Fellows of the Hong Kong Academy of Engineering Sciences (HKAES) for the year 2023. This recognition highlights their exceptional contributions and expertise in the field of engineering.

Lingnan University and Manchester University co-hosted a two-day China and Higher Education Conference 2023 on 13-14 December, exploring the theme "New Spaces and Places in Education: Technology and Innovation in Chinese Higher Education". The conference brought together distinguished representatives from the global academic community. The keynote address by Prof S. Joe Qin, the President of Lingnan University, at this event was particularly noteworthy, adding a touch of brilliance to the entire proceedings.

Prof S. Joe Qin, President and Wai Kee Kau Chair Professor of Data Science of Lingnan University, has been elected a Member of the European Academy of Sciences and Arts (EASA) in the Technical and Environmental Sciences class, making him the first scholar from Lingnan to receive this prestigious honour. Prof Klaus Mainzer, President of the European Academy of Sciences and Arts, extended his congratulations to President Qin. This recognition follows Prof Qin's election as a Fellow of the National Academy of Inventors, marking another distinguished membership in a globally renowned scientific academy.

Researchers at Tohoku University and the University of California, Santa Barbara have shown a proof-of-concept of an energy-efficient computer compatible with current AI.

The Distinguished Scholars Seminar Series organised by Lingnan University’s School of Graduate Studies presented its fourth session on 7 December “Creating a better future: harnessing AI for social and environmental responsibility” was delivered by Prof Sam Kwong Tak-wu, Chair Professor of Computational Intelligence and Associate Vice-President (Strategic Research) at Lingnan University. Prof Kwong was recently on the “Highly Cited Researchers 2023” list and in the seminar, he analysed how the rapid rise of artificial intelligence and robots in recent years will affect human work and daily life.

Lingnan University held the opening ceremony for Lingnan@WestKowloon and Lingnan Arts Biennale 2023 Digital Art Exhibition “Digital Matter” at M+ Tower of West Kowloon Cultural District (WKCD) today (7 December). The unveiling ceremony of it was officiated by Council Chairman Mr Andrew Yao Cho-fai, Council Treasurer Ms Katherine Cheung Marn-kay, Chairman of the Court Dr Patrick Wong Chi-kwong, and Prof S. Joe Qin, President and Wai Kee Kau Chair Professor of Data Science of Lingnan University. They marked the launch of the Lingnan@WestKowloon by presiding over the switching-on ceremony.

On 14 November 2023, Lingnan University and the Shenzhen Technology and Innovation Commission signed an agreement at the 2023 Assembly of Shenzhen Virtual University Park, announcing that Lingnan University will officially become a member of the Shenzhen Virtual University Park.

They say two heads are better than one. But in the world of fish, it appears two fins are better than one. Researchers have produced a theoretical model that demonstrates the underlying mechanisms behind how fish will synchronize their fin movements to ride each other’s vortices, thereby saving energy.

- A demonstration was held on September 21 at the Goheung Drone Center in Goheung-gun, Jeollanam-do - This anti-drone radar, developed only with domestic technology, has the world’s highest level of technology... K-defense proudly making its inroads in the world

- Professor Park’s research team at DGIST successfully develops AI for pathological image analysis that can accurately segment overlapping cell nuclei using only rough labels - Improved efficiency in diagnosing diseases and predicting the prognosis of patients by analyzing the microenvironment of pathological images... published in MICCAI, a top journal in the field of artificial intelligence

- Professor Sung-hoon Im’s research team at DGIST has developed automated search technology that can find an optimal deep learning network architecture by learning the relationship between tasks. - It is expected to greatly contribute to advances in AI technology, such as autonomous driving, which simultaneously performs multiple tasks, including object detection, lane recognition, and distance estimation.

- Successfully developed federated learning AI, which can enable large-scale model learning and work without violating privacy with only a single model transfer - Collaborated with Stanford University on a model for generating high-quality brain MRI images... Published 2 papers in MICCAI

Osaka University and NEC Corporation are jointly developing a data infrastructure called RED-ONION to accelerate open science by reducing the burden on researchers to record the research process, share research data and publish them on the data aggregation platform. They are currently engaged in the R & D of three elemental technologies: (1) Technology to integrate a data aggregation platform and data publication platform, (2) Technology to generate research process information on supercomputers, and (3) A high-speed data transmission function. These research results will be showcased at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC23) scheduled to be held from November 12 to 17, 2023 in Denver, Colorado.

- DGIST developed an image sensor technology that automatically analyzes phenomena in cardiac muscle cells, such as calcium dynamics and impulse patterns, based on multi-mode images combining holograms and calcium. - This technology can be used to examine characteristics of calcium dynamics and cellular motility depending on drugs inserted, thus being a core technology for new drug development customized for patients.

- Professor Park’s research team at DGIST has successfully developed domain-adaptive AI specialized in medical images - Contributes to saving cost and time in developing deep learning models for multi-center medical image analysis

- The developed image translation model can yield accurate classification results despite the use of biased data for the training of a deep learning model. - This solution is expected to be applied in various fields, such as self-driving, content creation, and medicine.

Graphene is often referred to as a wonder material for its advantageous qualities. But its application in quantum computers, while promising, is stymied by the challenge of getting accurate measurements of quantum bit states with existing techniques. Now, Tohoku University researchers have developed design guidelines that enable radio-frequency reflectometry to achieve high-speed electrical readouts of graphene nanodevices.

The latest ‘large language model’ artificial intelligence system, GPT-4, could aid chemistry researchers, but limitations reveal the need for improvements.

Researchers from Osaka University have developed a data-driven AI algorithm for controlling the heating and cooling of an office building. The system does not require ambient sensors or specific knowledge of the building’s rooms. During heating operations, the system was able to achieve energy savings of up to 30%, which can represent significant reductions to cost and environmental impact.

Scientists have revealed that the outer part of our brain (the cortex) is skilled at managing all the info it gets from the outside world thanks to special groups of nerve connections called modules, which work together but also independently.

The physical laws of everyday water flow were established two centuries ago. However, scientists today struggle to simulate disrupted water flow virtually, e.g., when a hand or object alters its flow. A research team from Tohoku University has harnessed the power of deep reinforcement learning to replicate the flow of water when disturbed. The replication allowed for recreating water flow in real time based on only a small amount of data, opening up the possibility for virtual reality interactions involving water.

- Research team led by Professor Sanghyun Park at DGIST developed a deep-learning technology capable of accurately classifying the brain waves of a subject using only a small amount of data - Contributes significantly to improving the utilization of deep-learning models, which require a large amount of data from subjects to classify brain waves - Research findings have been published in IEEE Transactions on Neural Networks and Learning Systems, a world-renowned academic journal in the AI field

Researchers have explored the efficacy of combining solar panels on rooftops with electric vehicles acting as storage batteries in Paris and its surrounding areas, finding some encouraging results.

To address the lack of suitable training data for deep-learning semantic segmentation models in urban landscaping, researchers from Osaka University developed a method that generates a training dataset without the need for real images or a model of an existing city. The method, which is based on procedural modelling and image-to-image techniques, enables segmentation models to achieve comparable performance under some conditions at a fraction of the cost of real dataset generation.