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KAIST-KT AI & SW Research Center to Open
KAIST and KT will team up to advance AI technology by co-founding the “AI and SW Research Center.” Last month, President Kwang Hyung Lee and KT CEO Hyeon-Mo Ku signed the agreement to launch the center in Daejeon by the end of the year.
The KAIST-KT AI and SW Research Center will focus on exploring original technologies and industry AI that will incorporate KAIST’s excellent R&D capabilities and KT’s future AI-based business portfolio. The center will be located at the KT’s Research Center in Daejeon.
The two sides selected 15 futuristic projects for developing original technologies in the fields of sound, vision, health, and humanistic AI. In addition, the center plans to develop an AI model that can perceive and reply to precise and complex information-based situations through human conversation and detection, sound, images, and sensing.
To lay the groundwork for next-generation markets, the center will work on five industrial AI projects in the fields of media, bio, and health. Both KAIST and KT aim to lead digital innovation and changes in lifestyles by developing a next-generation AI model to follow GPT-3 (Generative Pre-Training 3) and strengthen the global competitiveness of AI technologies.
Furthermore, KT will provide infrastructure including space, equipment, and manpower to KAIST students hoping to form a start-up. A KT accelerator for start-up cultivation and investment will also help KAIST students via a start-up mentoring program. It will also run scholarship and internship programs for students who stand out during joint research projects.
President Lee said, “KT is an excellent AI R&D partner dealing with differentiated data from diverse sectors. Through the AI core technology lab, I look forward to seeing innovative technologies that will be meaningful not only for academia, but also for industry.”
2021.06.01 View 4071 -
Research Day Highlights the Most Impactful Technologies of the Year
Technology Converting Full HD Image to 4-Times Higher UHD Via Deep Learning Cited as the Research of the Year
The technology converting a full HD image into a four-times higher UHD image in real time via AI deep learning was recognized as the Research of the Year. Professor Munchurl Kim from the School of Electrical Engineering who developed the technology won the Research of the Year Grand Prize during the 2021 KAIST Research Day ceremony on May 25. Professor Kim was lauded for conducting creative research on machine learning and deep learning-based image processing.
KAIST’s Research Day recognizes the most notable research outcomes of the year, while creating opportunities for researchers to immerse themselves into interdisciplinary research projects with their peers. The ceremony was broadcast online due to Covid-19 and announced the Ten R&D Achievement of the Year that are expected to make a significant impact.
To celebrate the award, Professor Kim gave a lecture on “Computational Imaging through Deep Learning for the Acquisition of High-Quality Images.” Focusing on the fact that advancements in artificial intelligence technology can show superior performance when used to convert low-quality videos to higher quality, he introduced some of the AI technologies that are currently being applied in the field of image restoration and quality improvement.
Professors Eui-Cheol Shin from the Graduate School of Medical Science and Engineering and In-Cheol Park from the School of Electrical Engineering each received Research Awards, and Professor Junyong Noh from the Graduate School of Culture Technology was selected for the Innovation Award. Professors Dong Ki Yoon from the Department of Chemistry and Hyungki Kim from the Department of Mechanical Engineering were awarded the Interdisciplinary Award as a team for their joint research.
Meanwhile, out of KAIST’s ten most notable R&D achievements, those from the field of natural and biological sciences included research on rare earth element-platinum nanoparticle catalysts by Professor Ryong Ryoo from the Department of Chemistry, real-time observations of the locational changes in all of the atoms in a molecule by Professor Hyotcherl Ihee from the Department of Chemistry, and an investigation on memory retention mechanisms after synapse removal from an astrocyte by Professor Won-Suk Chung from the Department of Biological Sciences.
Awardees from the engineering field were a wearable robot for paraplegics with the world’s best functionality and walking speed by Professor Kyoungchul Kong from the Department of Mechanical Engineering, fair machine learning by Professor Changho Suh from the School of Electrical Engineering, and a generative adversarial networks processing unit (GANPU), an AI semiconductor that can learn from even mobiles by processing multiple and deep networks by Professor Hoi-Jun Yoo from the School of Electrical Engineering.
Others selected as part of the ten research studies were the development of epigenetic reprogramming technology in tumour by Professor Pilnam Kim from the Department of Bio and Brain Engineering, the development of an original technology for reverse cell aging by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering, a heterogeneous metal element catalyst for atmospheric purification by Professor Hyunjoo Lee from the Department of Chemical and Biomolecular Engineering, and the Mobile Clinic Module (MCM): a negative pressure ward for epidemic hospitals by Professor Taek-jin Nam (reported at the Wall Street Journal) from the Department of Industrial Design.
2021.05.31 View 12435 -
Identification of How Chemotherapy Drug Works Could Deliver Personalized Cancer Treatment
The chemotherapy drug decitabine is commonly used to treat patients with blood cancers, but its response rate is somewhat low. Researchers have now identified why this is the case, opening the door to more personalized cancer therapies for those with these types of cancers, and perhaps further afield.
Researchers have identified the genetic and molecular mechanisms within cells that make the chemotherapy drug decitabine—used to treat patients with myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) —work for some patients but not others. The findings should assist clinicians in developing more patient-specific treatment strategies.
The findings were published in the Proceedings of the National Academies of Science on March 30.
The chemotherapy drug decitabine, also known by its brand name Dacogen, works by modifying our DNA that in turn switches on genes that stop the cancer cells from growing and replicating. However, decitabine’s response rate is somewhat low (showing improvement in just 30-35% of patients), which leaves something of a mystery as to why it works well for some patients but not for others. To find out why this happens, researchers from the KAIST investigated the molecular mediators that are involved with regulating the effects of the drug.
Decitabine works to activate the production of endogenous retroviruses (ERVs), which in turn induces an immune response. ERVs are viruses that long ago inserted dormant copies of themselves into the human genome. Decitabine in essence, ‘reactivates’ these viral elements and produces double-stranded RNAs (dsRNAs) that the immune system views as a foreign body.
“However, the mechanisms involved in this process, in particular how production and transport of these ERV dsRNAs were regulated within the cell were understudied,” said corresponding author Yoosik Kim, professor in the Department of Chemical and Biomolecular Engineering at KAIST.
“So to explain why decitabine works in some patients but not others, we investigated what these molecular mechanisms were,” added Kim.
To do so, the researchers used image-based RNA interference (RNAi) screening. This is a relatively new technique in which specific sequences within a genome are knocked out of action or “downregulated.” Large-scale screening, which can be performed in cultured cells or within live organisms, works to investigate the function of different genes. The KAIST researchers collaborated with the Institut Pasteur Korea to analyze the effect of downregulating genes that recognize ERV dsRNAs and could be involved in the cellular response to decitabine.
From these initial screening results, they performed an even more detailed downregulation screening analysis. Through the screening, they were able to identify two particular gene sequences involved in the production of an RNA-binding protein called Staufen1 and the production of a strand of RNA that does not in turn produce any proteins called TINCR that play a key regulatory role in response to the drug. Staufen1 binds directly to dsRNAs and stabilizes them in concert with the TINCR.
If a patient is not producing sufficient Staufen1 and TINCR, then the dsRNA viral mimics quickly degrade before the immune system can spot them. And, crucially for cancer therapy, this means that patients with lower expression (activation) of these sequences will show inferior response to decitabine. Indeed, the researchers confirmed that MDS/AML patients with low Staufen1 and TINCR expression did not benefit from decitabine therapy.
“We can now isolate patients who will not benefit from the therapy and direct them to a different type of therapy,” said first author Yongsuk Ku. “This serves as an important step toward developing a patient-specific treatment cancer strategy.”
As the researchers used patient samples taken from bone marrow, the next step will be to try to develop a testing method that can identify the problem from just blood samples, which are much easier to acquire from patients.
The team plans to investigate if the analysis can be extended to patients with solid tumors in addition to those with blood cancers.
-Profile
Professor Yoosik Kim
https://qcbio.kaist.ac.kr/
Department of Chemical and Biomolecular Engineering
KAIST
-Publication
Noncanonical immune response to the inhibition of DNA methylation by Staufen1 via stabilization of endogenous retrovirus RNAs, PNAS
2021.05.24 View 8187 -
KPC4IR Leads the Global Blockchain Standards Via Korea Innovation Studies
The Korea Policy Center for the Fourth Industrial Revolution (KPC4IR) at KAIST will play a leading role in the Global Standards Mapping Initiative (GSMI) 2.0 as the Chair of Working Group on South Korea at the Global Blockchain Business Council (GBBC). The GBBC, a Swiss-based non-profit consortium, established the GSMI to map blockchain technology ecosystem, established the GSMI to map blockchain and digital asset standards and regulation globally. The initial release of the GSMI mapped data and outputs from ons, 185 jurisdictions, nearly 400 industry groups, and over 30 technical standard-setting entities.
The GSMI Working Group on South Korea is the only group that will investigate the country-level innovation of blockchain and digital asset alongside six Korean blockchain associations: The GSMI Working Group on South Korea is the only group that will investigate the country-level innovation of blockchain and digital asset alongside six Korean blockchain associations: the Korea Blockchain Association, the Korea Society of Blockchain, Blockchain & Law, the Open Blockchain and DID Association, the Korea Blockchain Startup Association, and the Korea Blockchain Industry Promotion Association. Individual members also joined from the Inter-American Development Bank, Blockchain Labs, and GOPAX.
The GSMI Working Group on South Korea, chaired by KAIST, will leverage their experience in blockchain adoption to assist in setting global standards for the ecosystem. The Group will also highlight how South Korea can be a testbed for ITC adoption and open the door to a blockchain-ready world.
GSMI 2.0 is spearheaded by nine working groups chaired by institutions, such as the World Economic Forum and the GBBC, Ernst & Young, HM Revenue and Customs, Accenture, and Hyperledger - Linux Foundation. Each of the Working Groups will be supported by sixteen fellows from eight fellow program partners. KAIST student Yujin Bang is the South Korea Working Group fellow. The GBBC and the WEF already published the first volume of the GSMI in October 2020 in collaboration with world-leading institutions, including KAIST, MIT Media Lab, and Accenture.
Director of the KPC4IR Professor So Young Kim said, “The designation of KAIST is the result of continued collaborations with the WEF. The participation of this working group will help Korea’s global leadership with blockchain standards.”
2021.05.18 View 6040 -
Gut Hormone Triggers Craving for More Proteins
- Revelations from a fly study could improve our understanding of protein malnutrition in humans. -
A new study led by KAIST researchers using fruit flies reveals how protein deficiency in the diet triggers cross talk between the gut and brain to induce a desire to eat foods rich in proteins or essential amino acids. This finding reported in the May 5 issue of Nature can lead to a better understanding of malnutrition in humans.
“All organisms require a balanced intake of carbohydrates, proteins, and fats for their well being,” explained KAIST neuroscientist and professor Greg Seong-Bae Suh. “Taking in sufficient calories alone won’t do the job, as it can still lead to severe forms of malnutrition including kwashiorkor, if the diet does not include enough proteins,” he added.
Scientists already knew that inadequate protein intake in organisms causes a preferential choice of foods rich in proteins or essential amino acids but they didn’t know precisely how this happens. A group of researchers led by Professor Suh at KAIST and Professor Won-Jae Lee at Seoul National University (SNU) investigated this process in flies by examining the effects of different genes on food preference following protein deprivation.
The group found that protein deprivation triggered the release of a gut hormone called neuropeptide CNMamide (CNMa) from a specific population of enterocytes - the intestine lining cells. Until now, scientists have known that enterocytes release digestive enzymes into the intestine to help digest and absorb nutrients in the gut. “Our study showed that enterocytes have a more complex role than we previously thought,” said Professor Suh.
Enterocytes respond to protein deprivation by releasing CNMa that conveys the nutrient status in the gut to the CNMa receptors on nerve cells in the brain. This then triggers a desire to eat foods containing essential amino acids.
Interestingly, the KAIST-SNU team also found that the microbiome - Acetobacter bacteria - present in the gut produces amino acids that can compensate for mild protein deficit in the diet. This basal level of amino acids provided by the microbiome modifies CNMa release and tempers the flies’ compensatory desire to ingest more proteins.
The research team was able to further clarify two signalling pathways that respond to protein loss from the diet and ultimately produce the CNMa hormone in these specific enterocytes.
The team said that further studies are still needed to understand how CNMa communicates with its receptors in the brain, and whether this happens by directly activating nerve cells that link the gut to the brain or by indirectly activating the brain through blood circulation. Their research could provide insights into the understanding of similar process in mammals including humans.
“We chose to investigate a simple organism, the fly, which would make it easier for us to identify and characterize key nutrient sensors. Because all organisms have cravings for needed nutrients, the nutrient sensors and their pathways we identified in flies would also be relevant to those in mammals. We believe that this research will greatly advance our understanding of the causes of metabolic disease and eating-related disorders,” Professor Suh added.
This work was supported by the Samsung Science and Technology Foundation (SSTF) and the National Research Foundation (NRF) of Korea.
Publication:
Kim, B., et al. (2021) Response of the Drosophila microbiome– gut–brain axis to amino acid deficit. Nature. Available online at https://doi.org/10.1038/s41586-021-03522-2
Profile:
Greg Seong-Bae Suh, Ph.D
Associate Professor
seongbaesuh@kaist.ac.krLab of Neural Interoception
https://www.suhlab-neuralinteroception.kaist.ac.kr/Department of Biological Sciences
https://bio.kaist.ac.kr/
Korea Advanced Institute of Science and Technology (KAIST)
https:/kaist.ac.kr/en/
Daejeon 34141, Korea
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2021.05.17 View 6459 -
Dr. Won-Joon Lee from the ADD Wins the Jeong Hun Cho Award
Dr. Won-Joon Lee from the Agency for Defense Development (ADD) became the 17th Jeong Hun Cho Award recipient. KAIST PhD candidate Sok-Min Choi from the Department of Aerospace Engineering, Master’s-PhD combined course student Hyong-Won Choi from Korea University, and Chong-Ho Park from Kongju National University High School were also selected.
The award recognizes promising young scientists who makes significant achievements in the field of aerospace engineering in honor of Jeong Hun Cho, the former PhD candidate in the Department of Aerospace Engineering who died in a lab accident in May in 2003. Cho’s family endowed the award and scholarship to honor him. Three scholarship recipients from Cho’s alma mater, KAIST, Korea University, and Kongju National High School are selected every year.
Dr. Lee from the ADD has conducted research on shape design methods and radar absorbing structures for unmanned aerial vehicles, publishing more than 24 articles in SCI-level journals and 17 at academic conferences.
Dr. Lee was awarded 25 million KRW in prize money. The two students from KAIST and Korea University each received a 4 million KRW scholarship and Park received 3 million KRW.
2021.05.17 View 4134 -
KAIST Listed as Top 100 Global Innovator by Clarivate
KAIST was named as one of the Top 100 Global Innovators 2021 by Clarivate. Among the top 100, 42 US corporations, including Amazon, Apple, Google, and Facebook, and 29 Japanese corporations made the list. The list included four Korean corporations Samsung Electronics, LG Electronics, LS Electronics, and SK Telecommunications. KAIST, the only university listed as a global innovator, regained its place in the Top 100 Global Innovators this year after last being named in 2013. Industrywide, the electronics and semiconductor sectors took the majority of the top global innovators spots with 21 and 12 corporations respectively.
President Kwang Hyung Lee received the trophy from Clarivate Korea Regional Director Seongsik Ahn on May 12 at KAIST’s main campus. President Lee said, “We are glad that our continued innovation efforts are receiving worldwide recognition and will continue to strive for sustainable growth as a university that creates global value and impact.”
Every year since 2012, the Top 100 Global Innovators has identified companies and institutions at the pinnacle of the global innovation landscape by measuring the ideation culture that produces patents and puts them at the forefront. Clarivate tracks innovation based on four factors: 1. volume of patents 2. influence 3. Success and 4. globalization using patents, patents indices, and citation index solutions.
For measuring the patent volume, the Top 100 candidate must meet a threshold of 100 granted patents received in the past five years and more than 500 in the Derwent World Patents Index over any time period. Clarivate assesses the level of influence of the patented ideas by reviewing the number of external citations their inventions received over the past five years. For measuring success, they look at how successful each candidate has been getting their applications for patent protection approved by patent offices around the world over past five years. Globalization measures the investment levels of each candidate in their patent applications, a metric designed to assess both the importance of invention to the companies as well as the footprint of commercialization.
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2021.05.12 View 4539 -
Observing Individual Atoms in 3D Nanomaterials and Their Surfaces
Atoms are the basic building blocks for all materials. To tailor functional properties, it is essential to accurately determine their atomic structures. KAIST researchers observed the 3D atomic structure of a nanoparticle at the atom level via neural network-assisted atomic electron tomography.
Using a platinum nanoparticle as a model system, a research team led by Professor Yongsoo Yang demonstrated that an atomicity-based deep learning approach can reliably identify the 3D surface atomic structure with a precision of 15 picometers (only about 1/3 of a hydrogen atom’s radius). The atomic displacement, strain, and facet analysis revealed that the surface atomic structure and strain are related to both the shape of the nanoparticle and the particle-substrate interface.
Combined with quantum mechanical calculations such as density functional theory, the ability to precisely identify surface atomic structure will serve as a powerful key for understanding catalytic performance and oxidation effect.
“We solved the problem of determining the 3D surface atomic structure of nanomaterials in a reliable manner. It has been difficult to accurately measure the surface atomic structures due to the ‘missing wedge problem’ in electron tomography, which arises from geometrical limitations, allowing only part of a full tomographic angular range to be measured. We resolved the problem using a deep learning-based approach,” explained Professor Yang.
The missing wedge problem results in elongation and ringing artifacts, negatively affecting the accuracy of the atomic structure determined from the tomogram, especially for identifying the surface structures. The missing wedge problem has been the main roadblock for the precise determination of the 3D surface atomic structures of nanomaterials.
The team used atomic electron tomography (AET), which is basically a very high-resolution CT scan for nanomaterials using transmission electron microscopes. AET allows individual atom level 3D atomic structural determination.
“The main idea behind this deep learning-based approach is atomicity—the fact that all matter is composed of atoms. This means that true atomic resolution electron tomogram should only contain sharp 3D atomic potentials convolved with the electron beam profile,” said Professor Yang.
“A deep neural network can be trained using simulated tomograms that suffer from missing wedges as inputs, and the ground truth 3D atomic volumes as targets. The trained deep learning network effectively augments the imperfect tomograms and removes the artifacts resulting from the missing wedge problem.”
The precision of 3D atomic structure can be enhanced by nearly 70% by applying the deep learning-based augmentation. The accuracy of surface atom identification was also significantly improved.
Structure-property relationships of functional nanomaterials, especially the ones that strongly depend on the surface structures, such as catalytic properties for fuel-cell applications, can now be revealed at one of the most fundamental scales: the atomic scale.
Professor Yang concluded, “We would like to fully map out the 3D atomic structure with higher precision and better elemental specificity. And not being limited to atomic structures, we aim to measure the physical, chemical, and functional properties of nanomaterials at the 3D atomic scale by further advancing electron tomography techniques.”
This research, reported at Nature Communications, was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research M3I3 Project.
-Publication
Juhyeok Lee, Chaehwa Jeong & Yongsoo Yang
“Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography”
Nature Communications
-Profile
Professor Yongsoo Yang
Department of Physics
Multi-Dimensional Atomic Imaging Lab (MDAIL)
http://mdail.kaist.ac.kr
KAIST
2021.05.12 View 9450 -
The Educational ‘Metaverse’ Is Coming
The universities best equipped with digital infrastructure and savvy human resources will emerge as the new leaders − no matter where they are, says Kwang Hyung Lee
It goes without saying that the Covid-19 pandemic has taken a heavy toll on the education sector. Approximately 1.6 billion students from 192 countries, or 91 per cent of the world’s student population, have experienced educational disruptions.
As we all know, this disruption led to online education hastily emerging as an important new platform. However, approximately 29 percent of young people worldwide, about 364 million individuals, are not online. In many ways, the digital divide is now wider than ever.
We do, however, have an opportunity to ensure that the integration of emerging technologies is further accelerated and that online delivery becomes an integral component of education. This should, in theory, lead to more inclusive and creative pedagogical solutions.
The entire world has effectively taken part in an educational experiment, and at KAIST we were able to confirm that blended education worked effectively for our students. It made up for the long-standing pedagogical shortfalls of the one-way delivery of knowledge and made it possible to shift to a learner-centric model, giving us a great opportunity to unlock the creativity and collaborative minds of our students. Education tailored to students’ individual levels will not only help them accumulate knowledge but improve their ability to use it.
A recent survey in South Korea found that 96 per cent of Seoul citizens believed that the pandemic widened the existing learning gap, but 74 per cent said that schools should carry out a blended form of education using both remote and in-person classes. The feedback from KAIST students on our online classes gives us a glimpse into the new paths we need to take.
From last March, we offered 60 per cent real-time classes via Zoom and 40 per cent through our pre-recorded learning management system. Our students were satisfied with the real-time classes in which they could interact face to face with professors. The blended class format combining real-time and pre-recorded content received very satisfactory evaluations. The problem, however, came with lab classes via Zoom. Students expressed their dissatisfaction with the passive and indirect learning experiences.
Developing online tools or technologies that can enable scientific experiments, engineering prototyping and other hands-on activities remains a challenge. However, we can begin to address these issues using complementary technologies such as virtual reality, augmented reality, image recognition and eye-tracking technologies.
The barriers to access to these new experiences are both complex and pervasive, yet there are ways we can pull together to disrupt these barriers at a global level in the hope of fostering inclusive growth.
For instance, the virtual campus will become a reality at the Kenya-KAIST campus, which will open by September 2023 in the Konza Technopolis, 60km outside Nairobi.
There, we aim to go beyond online education by creating a “metaverse” that provides assistance for running classes and creates an immersive learning experience that runs the gamut of campus activities while utilising the latest digital technologies.
Following a feasibility study of the Kenya campus that took place five years ago, we planned to utilise Mooc courses created by KAIST professors. Using online content there will help mitigate the educational gap between the two institutions, plus it will reduce the need for many students and faculty to make the long commute from the capital to the campus. Although students are expected to live on campus, they will probably engage in other activities in Nairobi and want to take classes wherever they are.
Since it will take some time to select and recruit an excellent group of faculty members, we feel it will be more effective to use online lecture platforms to deliver standardised and qualified content.
It has been posited that the fast adoption of online education will affect international students’ enrolment in universities, which will lead to reductions in revenue. However, we expect that students will choose a university that offers more diverse and interactive metaverse experiences on top of academic and global experiences. The time has come to rebuild the curriculum and infrastructure for the world of the metaverse. We can’t go back to the way things were before.
Universities around the world are now on the same starting line. They need to innovate and pioneer new approaches and tools that can enable all sorts of campus activities online. They should carve out their own distinct metaverse that is viable for human interaction and diverse technological experiences that promote students’ creativity and collaborative minds.
The universities best equipped with digital infrastructure and savvy human resources will emerge as the new leaders − no matter where they are.
Successful education needs the full support of communities and equal access to opportunities. Technological breakthroughs must be used to benefit everyone. To this end, the private and public sectors need to collaborate to bring about inclusive learning opportunities and help shore up global resilience against this and any future pandemics. The hope is that such disruption will bring about new technology and knowledge that we can leverage to reshape the future of education.
ⓒ Source: Times Higher Education (THE)
2021.05.10 View 8126 -
Prof. Sang Yup Lee Elected as a Foreign Member of the Royal Society
Vice President for Research Distinguished Professor Sang Yup Lee was elected as a foreign member of the Royal Society in the UK. On May 6, the Society announced the list of distinguished new 52 fellows and 10 foreign members who achieved exceptional contributions to science. Professor Lee and Professor V. Narry Kim from Seoul National University are the first foreign members ever elected from Korea.
The Royal Society, established in 1660, is one of the most prestigious national science academies and a fellowship of 1,600 of the world’s most eminent scientists. From Newton to Darwin, Einstein, Hawking, and beyond, pioneers and paragons in their fields are elected by their peers. To date, there are 280 Nobel prize winners among the fellows.
Distinguished Professor Lee from the Department of Chemical and Biomolecular Engineering at KAIST is one of the Highly Cited Researchers (HCRs) who pioneered systems metabolic engineering and developed various micro-organisms for producing a wide range of fuels, chemicals, materials, and natural compounds.
His seminal scholarship and research career have already been recognized worldwide. He is the first Korean ever elected into the National Academy of Inventors (NAI) in the US and one of 13 scholars elected as an International Member of both the National Academy of Sciences (NAS) and the National Academy of Engineering (NAE) in the US. With this fellowship, he added one more accolade of being the first non-US and British Commonwealth scientist elected into the three most prestigious science academies: the NAS, the NAE, and the Royal Society.
2021.05.07 View 8836 -
Professor Byungha Shin Named Scientist of the Month
Professor Byungha Shin from the Department of Materials Science and Engineering won the Scientist of the Month Award presented by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF) on May 4. Professor Shin was recognized for his research in the field of next-generation perovskite solar cells and received 10 million won in prize money.
To achieve ‘carbon neutrality,’ which many countries across the globe including Korea hope to realize, the efficiency of converting renewable energies to electricity must be improved. Solar cells convert solar energy to electricity. Since single solar cells show lower efficiency, the development of ‘tandem solar cells’ that connect two or more cells together has been popular in recent years.
However, although ‘perovskite’ received attention as a next-generation material for tandem solar cells, it is sensitive to the external environment including light and moisture, making it difficult to maintain stability.
Professor Shin discovered that, theoretically, adding certain anion additives to perovskite solar cells would allow the control of the electrical and structural properties of the two-dimensional stabilization layer that forms inside the film. He confirmed this through high-resolution transmission electron microscopy. Controlling the amount of anions in the additives allowed the preservation of over 80% of the initial stability even after 1000 hours of continuous exposure to sunlight.
Based on this discovery, Professor Shin combined silicon with solar cells to create a tandem solar cell with 26.7% energy convergence efficiency. Considering that the highest-efficiency tandem solar cell in existence showed 29.5% efficiency, this figure is quite high. Professor Shin’s perovskite solar cell is also combinable with the CIGS (Cu(In,Ga)Se2) thin-film solar cell composed of copper (Cu), indium (In), gallium (Ga), and selenium (Se2).
Professor Shin’s research results were published in the online edition of the journal Science in April of last year.
“This research is meaningful for having suggested a direction for solar cell material stabilization using additives,” said Professor Shin. “I look forward to this technique being applied to a wide range of photoelectrical devices including solar cells, LEDs, and photodetectors,” he added.
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2021.05.07 View 7859 -
T-GPS Processes a Graph with Trillion Edges on a Single Computer
Trillion-scale graph processing simulation on a single computer presents a new concept of graph processing
A KAIST research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. Named as T-GPS (Trillion-scale Graph Processing Simulation) by the developer Professor Min-Soo Kim from the School of Computing at KAIST, it can process a graph with one trillion edges using a single computer.
Graphs are widely used to represent and analyze real-world objects in many domains such as social networks, business intelligence, biology, and neuroscience. As the number of graph applications increases rapidly, developing and testing new graph algorithms is becoming more important than ever before. Nowadays, many industrial applications require a graph algorithm to process a large-scale graph (e.g., one trillion edges). So, when developing and testing graph algorithms such for a large-scale graph, a synthetic graph is usually used instead of a real graph. This is because sharing and utilizing large-scale real graphs is very limited due to their being proprietary or being practically impossible to collect.
Conventionally, developing and testing graph algorithms is done via the following two-step approach: generating and storing a graph and executing an algorithm on the graph using a graph processing engine.
The first step generates a synthetic graph and stores it on disks. The synthetic graph is usually generated by either parameter-based generation methods or graph upscaling methods. The former extracts a small number of parameters that can capture some properties of a given real graph and generates the synthetic graph with the parameters. The latter upscales a given real graph to a larger one so as to preserve the properties of the original real graph as much as possible.
The second step loads the stored graph into the main memory of the graph processing engine such as Apache GraphX and executes a given graph algorithm on the engine. Since the size of the graph is too large to fit in the main memory of a single computer, the graph engine typically runs on a cluster of several tens or hundreds of computers. Therefore, the cost of the conventional two-step approach is very high.
The research team solved the problem of the conventional two-step approach. It does not generate and store a large-scale synthetic graph. Instead, it just loads the initial small real graph into main memory. Then, T-GPS processes a graph algorithm on the small real graph as if the large-scale synthetic graph that should be generated from the real graph exists in main memory. After the algorithm is done, T-GPS returns the exactly same result as the conventional two-step approach.
The key idea of T-GPS is generating only the part of the synthetic graph that the algorithm needs to access on the fly and modifying the graph processing engine to recognize the part generated on the fly as the part of the synthetic graph actually generated.
The research team showed that T-GPS can process a graph of 1 trillion edges using a single computer, while the conventional two-step approach can only process of a graph of 1 billion edges using a cluster of eleven computers of the same specification. Thus, T-GPS outperforms the conventional approach by 10,000 times in terms of computing resources. The team also showed that the speed of processing an algorithm in T-GPS is up to 43 times faster than the conventional approach. This is because T-GPS has no network communication overhead, while the conventional approach has a lot of communication overhead among computers.
Professor Kim believes that this work will have a large impact on the IT industry where almost every area utilizes graph data, adding, “T-GPS can significantly increase both the scale and efficiency of developing a new graph algorithm.”
This work was supported by the National Research Foundation (NRF) of Korea and Institute of Information & communications Technology Planning & Evaluation (IITP).
Publication:
Park, H., et al. (2021) “Trillion-scale Graph Processing Simulation based on Top-Down Graph Upscaling,” Presented at the IEEE ICDE 2021 (April 19-22, 2021, Chania, Greece)
Profile:
Min-Soo Kim
Associate Professor
minsoo.k@kaist.ac.kr
http://infolab.kaist.ac.kr
School of Computing
KAIST
2021.05.06 View 6467