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A Mathematical Model Shows High Viral Transmissions Reduce the Progression Rates for Severe Covid-19
The model suggests a clue as to when a pandemic will turn into an endemic
A mathematical model demonstrated that high transmission rates among highly vaccinated populations of COVID-19 ultimately reduce the numbers of severe cases. This model suggests a clue as to when this pandemic will turn into an endemic.
With the future of the pandemic remaining uncertain, a research team of mathematicians and medical scientists analyzed a mathematical model that may predict how the changing transmission rate of COVID-19 would affect the settlement process of the virus as a mild respiratory virus.
The team led by Professor Jae Kyoung Kim from the Department of Mathematical Science and Professor Eui-Cheol Shin from the Graduate School of Medical Science and Engineering used a new approach by dividing the human immune responses to SARS-CoV-2 into a shorter-term neutralizing antibody response and a longer-term T-cell immune response, and applying them each to a mathematical model. Additionally, the analysis was based on the fact that although breakthrough infection may occur frequently, the immune response of the patient will be boosted after recovery from each breakthrough infection.
The results showed that in an environment with a high vaccination rate, although COVID-19 cases may rise temporarily when the transmission rate increases, the ratio of critical cases would ultimately decline, thereby decreasing the total number of critical cases and in fact settling COVID-19 as a mild respiratory disease more quickly.
Conditions in which the number of cases may spike include relaxing social distancing measures or the rise of variants with higher transmission rates like the Omicron variant. This research did not take the less virulent characteristic of the Omicron variant into account but focused on the results of its high transmission rate, thereby predicting what may happen in the process of the endemic transition of COVID-19.
The research team pointed out the limitations of their mathematical model, such as the lack of consideration for age or patients with underlying diseases, and explained that the results of this study must be applied with care when compared against high-risk groups. Additionally, as medical systems may collapse when the number of cases rises sharply, this study must be interpreted with prudence and applied accordingly. The research team therefore emphasized that for policies that encourage a step-wise return to normality to succeed, the sustainable maintenance of public health systems is indispensable.
Professor Kim said, “We have drawn a counter-intuitive conclusion amid the unpredictable pandemic through an adequate mathematical model,” asserting the importance of applying mathematical models to medical research.
Professor Shin said, “Although the Omicron variant has become the dominant strain and the number of cases is rising rapidly in South Korea, it is important to use scientific approaches to predict the future and apply them to policies rather than fearing the current situation.”
The results of the research were published on medRxiv.org on February 11, under the title “Increasing viral transmission paradoxically reduces progression rates to severe COVID-19 during endemic transition.”
This research was funded by the Institute of Basic Science, the Korea Health Industry Development Institute, and the National Research Foundation of Korea.
-PublicationHyukpyo Hong, Ji Yun Noh, Hyojung Lee, Sunhwa Choi, Boseung Choi, Jae Kyung Kim, Eui-Cheol Shin, “Increasing viral transmission paradoxically reduces progression rates to
severe COVID-19 during endemic transition,” medRxiv, February 9, 2022 (doi.org/10.1101/2022.02.09.22270633)
-ProfileProfessor Jae Kyung KimDepartment of Mathematical SciencesKAIST
Professor Eui-Cheol ShinGraduate School of Medical Science and EngineeringKAIST
2022.02.22 View 13091 -
Five Projects Ranked in the Top 100 for National R&D Excellence
Five KAIST research projects were selected as the 2021 Top 100 for National R&D Excellence by the Ministry of Science and ICT and the Korea Institute of Science & Technology Evaluation and Planning.
The five projects are:-The development of E. coli that proliferates with only formic acid and carbon dioxide by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering
-An original reverse aging technology that restores an old human skin cell into a younger one by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering-The development of next-generation high-efficiency perovskite-silicon tandem solar cells by Professor Byungha Shin from the Department of Materials Science and Engineering-Research on the effects of ultrafine dust in the atmosphere has on energy consumption by Professor Jiyong Eom from the School of Business and Technology Management-Research on a molecular trigger that controls the phase transformation of bio materials by Professor Myungchul Kim from the Department of Bio and Brain Engineering
Started in 2006, an Evaluation Committee composed of experts in industries, universities, and research institutes has made the preliminary selections of the most outstanding research projects based on their significance as a scientific and technological development and their socioeconomic effects. The finalists went through an open public evaluation. The final 100 studies are from six fields: 18 from mechanics & materials, 26 from biology & marine sciences, 19 from ICT & electronics, 10 from interdisciplinary research, and nine from natural science and infrastructure.
The selected 100 studies will receive a certificate and an award plaque from the minister of MSIT as well as additional points for business and institutional evaluations according to appropriate regulations, and the selected researchers will be strongly recommended as candidates for national meritorious awards.
In particular, to help the 100 selected research projects become more accessible for the general public, their main contents will be provided in a free e-book ‘The Top 100 for National R&D Excellence of 2021’ that will be available from online booksellers.
2022.02.17 View 13043 -
Research Finds Digital Music Streaming Consumption Dropped as a Result of Covid-19 and Lockdowns
Decline in human mobility has stunning consequences for content streaming
The Covid-19 pandemic and lockdowns significantly reduced the consumption of audio music streaming in many countries as people turned to video platforms. On average, audio music consumption decreased by 12.5% after the World Health Organization’s (WHO) pandemic declaration in March 2020.
Music streaming services were an unlikely area hit hard by the Covid-19 pandemic. New research in Marketing Science found that the drop in people’s mobility during the pandemic significantly reduced the consumption of audio music streaming. Instead, people turned more to video platforms.
“On average, audio music consumption decreased by more than 12% after the World Health Organization’s (WHO) pandemic declaration on March 11, 2020. As a result, during the pandemic, Spotify lost 838 million dollars of revenue in the first three quarters of 2020,” said Jaeung Sim, a PhD candidate in management engineering at KAIST and one of the authors of the research study on this phenomenon. “Our results showed that human mobility plays a much larger role in the audio consumption of music than previously thought.”
The study, “Frontiers: Virus Shook the Streaming Star: Estimating the Covid-19 Impact on Music Consumption,” conducted by Sim and Professor Daegon Cho of KAIST, Youngdeok Hwang of City University of New York, and Rahul Telang of Carnegie Mellon University, looked at online music streaming data for top songs for two years in 60 countries, as well as Covid-19 cases, lockdown statistics, and daily mobility data, to determine the nature of the changes.
The study showed how the pandemic adversely impacted music streaming services despite the common expectation that the pandemic would universally benefit online medias platforms. This implies that the substantially changing media consumption environment can place streaming music in fiercer competition with other media forms that offer more dynamic and vivid experiences to consumers.
The researchers found that music consumption through video platforms was positively associated with the severity of Covid-19, lockdown policies, and time spent at home.
-PublicationJaeung Sim, Daegon Cho, Youngdeok Hwang, and Rahul Telang,“Frontiers: Virus Shook the Streaming Star: Estimating the Covid-19 Impact on Music Consumption,” November 30 in Marketing Science online (doi.org/10.1287/mksc.2021.1321)
-Profile Professor Daegon ChoGraduate School of Information and Media ManagementCollege of BusinessKAIST
2022.02.15 View 12124 -
Label-Free Multiplexed Microtomography of Endogenous Subcellular Dynamics Using Deep Learning
AI-based holographic microscopy allows molecular imaging without introducing exogenous labeling agents
A research team upgraded the 3D microtomography observing dynamics of label-free live cells in multiplexed fluorescence imaging. The AI-powered 3D holotomographic microscopy extracts various molecular information from live unlabeled biological cells in real time without exogenous labeling or staining agents.
Professor YongKeum Park’s team and the startup Tomocube encoded 3D refractive index tomograms using the refractive index as a means of measurement. Then they decoded the information with a deep learning-based model that infers multiple 3D fluorescence tomograms from the refractive index measurements of the corresponding subcellular targets, thereby achieving multiplexed micro tomography. This study was reported in Nature Cell Biology online on December 7, 2021.
Fluorescence microscopy is the most widely used optical microscopy technique due to its high biochemical specificity. However, it needs to genetically manipulate or to stain cells with fluorescent labels in order to express fluorescent proteins. These labeling processes inevitably affect the intrinsic physiology of cells. It also has challenges in long-term measuring due to photobleaching and phototoxicity. The overlapped spectra of multiplexed fluorescence signals also hinder the viewing of various structures at the same time. More critically, it took several hours to observe the cells after preparing them.
3D holographic microscopy, also known as holotomography, is providing new ways to quantitatively image live cells without pretreatments such as staining. Holotomography can accurately and quickly measure the morphological and structural information of cells, but only provides limited biochemical and molecular information.
The 'AI microscope' created in this process takes advantage of the features of both holographic microscopy and fluorescence microscopy. That is, a specific image from a fluorescence microscope can be obtained without a fluorescent label. Therefore, the microscope can observe many types of cellular structures in their natural state in 3D and at the same time as fast as one millisecond, and long-term measurements over several days are also possible.
The Tomocube-KAIST team showed that fluorescence images can be directly and precisely predicted from holotomographic images in various cells and conditions. Using the quantitative relationship between the spatial distribution of the refractive index found by AI and the major structures in cells, it was possible to decipher the spatial distribution of the refractive index. And surprisingly, it confirmed that this relationship is constant regardless of cell type.
Professor Park said, “We were able to develop a new concept microscope that combines the advantages of several microscopes with the multidisciplinary research of AI, optics, and biology. It will be immediately applicable for new types of cells not included in the existing data and is expected to be widely applicable for various biological and medical research.”
When comparing the molecular image information extracted by AI with the molecular image information physically obtained by fluorescence staining in 3D space, it showed a 97% or more conformity, which is a level that is difficult to distinguish with the naked eye.
“Compared to the sub-60% accuracy of the fluorescence information extracted from the model developed by the Google AI team, it showed significantly higher performance,” Professor Park added.
This work was supported by the KAIST Up program, the BK21+ program, Tomocube, the National Research Foundation of Korea, and the Ministry of Science and ICT, and the Ministry of Health & Welfare.
-Publication
Hyun-seok Min, Won-Do Heo, YongKeun Park, et al. “Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning,” Nature Cell Biology (doi.org/10.1038/s41556-021-00802-x) published online December 07 2021.
-Profile
Professor YongKeun Park
Biomedical Optics Laboratory
Department of Physics
KAIST
2022.02.09 View 13919 -
Thermal Superconductor Lab Becomes the 7th Cross-Generation Collaborative Lab
The Thermal Superconductor Lab led by Senior Professor Sung Jin Kim from the Department of Mechanical Engineering will team up with Junior Professor Youngsuk Nam to develop next-generation superconductors. The two professor team was selected as the 7th Cross-Generation Collaborative Lab last week and will sustain the academic legacy of Professor Kim’s three decades of research on superconductors. The team will continue to develop thin, next-generation superconductors that carry super thermal conductivity using phase transition control technology and thin film packaging.
Thin-filmed, next-generation superconductors can be used in various high-temperature flexible electronic devices. The superconductors built inside of the semiconductor device packages will also be used for managing the low-powered but high-performance temperatures of semiconductor and electronic equipment.
Professor Kim said, “I am very pleased that my research, know-how, and knowledge from over 30 years of work will continue through the Cross-Generation Collaborative Lab system with Professor Nam. We will spare no effort to advance superconductor technology and play a part in KAIST leading global technology fields.” Junior Professor Nam also stressed that the team is excited to continue its research on crucial technology for managing the temperatures of semiconductors and other electronic equipment.
KAIST started this innovative research system in 2018, and in 2021 it established the steering committee to select new labs based on: originality, differentiation, and excellence; academic, social, economic impact; the urgency of cross-generation research; the senior professor’s academic excellence and international reputation; and the senior professor’s research vision. Selected labs receive 500 million KRW in research funding over five years.
2022.01.27 View 8173 -
Eco-Friendly Micro-Supercapacitors Using Fallen Leaves
Green micro-supercapacitors on a single leaf could easily be applied in wearable electronics, smart houses, and IoTs
A KAIST research team has developed graphene-inorganic-hybrid micro-supercapacitors made of fallen leaves using femtosecond laser direct writing. The rapid development of wearable electronics requires breakthrough innovations in flexible energy storage devices in which micro-supercapacitors have drawn a great deal of interest due to their high power density, long lifetimes, and short charging times. Recently, there has been an enormous increase in waste batteries owing to the growing demand and the shortened replacement cycle in consumer electronics. The safety and environmental issues involved in the collection, recycling, and processing of such waste batteries are creating a number of challenges.
Forests cover about 30 percent of the Earth’s surface and produce a huge amount of fallen leaves. This naturally occurring biomass comes in large quantities and is completely biodegradable, which makes it an attractive sustainable resource. Nevertheless, if the fallen leaves are left neglected instead of being used efficiently, they can contribute to fire hazards, air pollution, and global warming.
To solve both problems at once, a research team led by Professor Young-Jin Kim from the Department of Mechanical Engineering and Dr. Hana Yoon from the Korea Institute of Energy Research developed a novel technology that can create 3D porous graphene microelectrodes with high electrical conductivity by irradiating femtosecond laser pulses on the leaves in ambient air. This one-step fabrication does not require any additional materials or pre-treatment.
They showed that this technique could quickly and easily produce porous graphene electrodes at a low price, and demonstrated potential applications by fabricating graphene micro-supercapacitors to power an LED and an electronic watch. These results open up a new possibility for the mass production of flexible and green graphene-based electronic devices.
Professor Young-Jin Kim said, “Leaves create forest biomass that comes in unmanageable quantities, so using them for next-generation energy storage devices makes it possible for us to reuse waste resources, thereby establishing a virtuous cycle.”
This research was published in Advanced Functional Materials last month and was sponsored by the Ministry of Agriculture Food and Rural Affairs, the Korea Forest Service, and the Korea Institute of Energy Research.
-Publication
Truong-Son Dinh Le, Yeong A. Lee, Han Ku Nam, Kyu Yeon Jang, Dongwook Yang, Byunggi Kim, Kanghoon Yim, Seung Woo Kim, Hana Yoon, and Young-jin Kim, “Green Flexible Graphene-Inorganic-Hybrid Micro-Supercapacitors Made of Fallen Leaves Enabled by Ultrafast Laser Pulses," December 05, 2021, Advanced Functional Materials (doi.org/10.1002/adfm.202107768)
-ProfileProfessor Young-Jin KimUltra-Precision Metrology and Manufacturing (UPM2) LaboratoryDepartment of Mechanical EngineeringKAIST
2022.01.27 View 15096 -
AI Light-Field Camera Reads 3D Facial Expressions
Machine-learned, light-field camera reads facial expressions from high-contrast illumination invariant 3D facial images
A joint research team led by Professors Ki-Hun Jeong and Doheon Lee from the KAIST Department of Bio and Brain Engineering reported the development of a technique for facial expression detection by merging near-infrared light-field camera techniques with artificial intelligence (AI) technology.
Unlike a conventional camera, the light-field camera contains micro-lens arrays in front of the image sensor, which makes the camera small enough to fit into a smart phone, while allowing it to acquire the spatial and directional information of the light with a single shot. The technique has received attention as it can reconstruct images in a variety of ways including multi-views, refocusing, and 3D image acquisition, giving rise to many potential applications.
However, the optical crosstalk between shadows caused by external light sources in the environment and the micro-lens has limited existing light-field cameras from being able to provide accurate image contrast and 3D reconstruction.
The joint research team applied a vertical-cavity surface-emitting laser (VCSEL) in the near-IR range to stabilize the accuracy of 3D image reconstruction that previously depended on environmental light. When an external light source is shone on a face at 0-, 30-, and 60-degree angles, the light field camera reduces 54% of image reconstruction errors. Additionally, by inserting a light-absorbing layer for visible and near-IR wavelengths between the micro-lens arrays, the team could minimize optical crosstalk while increasing the image contrast by 2.1 times.
Through this technique, the team could overcome the limitations of existing light-field cameras and was able to develop their NIR-based light-field camera (NIR-LFC), optimized for the 3D image reconstruction of facial expressions. Using the NIR-LFC, the team acquired high-quality 3D reconstruction images of facial expressions expressing various emotions regardless of the lighting conditions of the surrounding environment.
The facial expressions in the acquired 3D images were distinguished through machine learning with an average of 85% accuracy – a statistically significant figure compared to when 2D images were used. Furthermore, by calculating the interdependency of distance information that varies with facial expression in 3D images, the team could identify the information a light-field camera utilizes to distinguish human expressions.
Professor Ki-Hun Jeong said, “The sub-miniature light-field camera developed by the research team has the potential to become the new platform to quantitatively analyze the facial expressions and emotions of humans.” To highlight the significance of this research, he added, “It could be applied in various fields including mobile healthcare, field diagnosis, social cognition, and human-machine interactions.”
This research was published in Advanced Intelligent Systems online on December 16, under the title, “Machine-Learned Light-field Camera that Reads Facial Expression from High-Contrast and Illumination Invariant 3D Facial Images.” This research was funded by the Ministry of Science and ICT and the Ministry of Trade, Industry and Energy.
-Publication“Machine-learned light-field camera that reads fascial expression from high-contrast and illumination invariant 3D facial images,” Sang-In Bae, Sangyeon Lee, Jae-Myeong Kwon, Hyun-Kyung Kim. Kyung-Won Jang, Doheon Lee, Ki-Hun Jeong, Advanced Intelligent Systems, December 16, 2021 (doi.org/10.1002/aisy.202100182)
ProfileProfessor Ki-Hun JeongBiophotonic LaboratoryDepartment of Bio and Brain EngineeringKAIST
Professor Doheon LeeDepartment of Bio and Brain EngineeringKAIST
2022.01.21 View 15091 -
Perigee-KAIST Rocket Research Center Launches Scientific Rocket
Undergraduate startup Perigree Aerospace develops suborbital rocket called Blue Whale 0.1
On December 29, Perigee Aerospace, an undergraduate startup, launched a test rocket with a length of 3.2 m, a diameter of 19 cm, and a weight of 51 kg, using ethanol and liquid oxygen as fuel. The launch took place off Jeju Island. It was aimed at building experience and checking the combustion of a liquid propulsion engine and the performance of pre-set flight and trajectory, communication, and navigation devices. It was also one of the projects marking the 50th anniversary of KAIST in 2021.
However, after flying for several seconds, the rocket lost its track due to a gust of wind that activated the rocket’s automatic flight suspension system. "At the moment the rocket took off, there was a much stronger gust than expected," Dong-Yoon Shin, CEO of Perigee said. "The wind sent it flying off course and the automatic flight suspension system stopped its engine." However, Shin was not disappointed, saying the launch, which was conducted in collaboration with Perigee-KAIST Rocket Research Center provided a good experience.
"Some people say that Blue Whale 0.1 is like a toy because of its small size. Of course, it's much smaller than the rockets I’ve dreamed of, but like other rockets, it has all the technology needed for launch," said Shin, who established his company in 2018 as a KAIST aerospace engineering student to develop small liquid-propellant orbital rockets. Perigee Aerospace aims to develop the world’s lightest launch vehicle using high-powered engines, with a goal of leading the global market for small launch vehicles in the new space generation.
Perigee-KAIST Rocket Research Center was founded in 2019 for the research and development of rocket propellants and has been testing the combustion of rocket engines of various sizes in their liquid propellant rocket combustion lab located on the KAIST Munji Campus.
The research center initiated the 50th anniversary rocket launch project in late April of last year, finished the examination of their preliminary design in late May, and secured a tentative launching site through the KAIST-Jejudo agreement in early July.
The ethanol engine combustion was tested in late July, and an examination meeting regarding the detailed design that took place in late August was followed by two months of static firing tests of the assembled rocket in October and November.
This was a very meaningful trial in which a domestic private enterprise founded by a college student collaborated with a university to successfully develop and launch a technically challenging liquid propellant rocket.
Shin's near-term goal is to launch a two-stage orbital rocket that uses liquid methane as fuel and weighs 1.8 tons. To secure competitiveness in the small projectile market, KAIST and Perigee Aerospace have set up a joint research center to test various rocket engine sizes and develop the world's lightest projectile using a high-performance engine.
Professor Jae-Hung Han, head of the Department of Aerospace Engineering, said, “The scientific rocket system secured through the launch of the celebratory rocket will be utilized for design and system-oriented education, and for carrying out various scientific missions.” He added, “It is very rare both domestically and globally that a scientific rocket designed by the initiatives of a department should be incorporated as part of a regular aerospace system design curriculum. This will be an exemplary case we can boast about to the rest of the world.”
Perigee Aerospace will improve the technology they have developed through the course of this project to develop subminiature vehicles they may use to launch small satellites into the low Earth orbit.
Shin said, “I am happy just with the fact that we have participated in a rocket project to celebrate the 50th anniversary of KAIST, and I would like to thank the engineers at my company and members of the KAIST Department of Aerospace Engineering.” He added, “I’m looking forward to the day that we develop a space launch vehicle that can deliver satellites even higher.”
2022.01.14 View 11158 -
Team KAIST Makes Its Presence Felt in the Self-Driving Tech Industry
Team KAIST finishes 4th at the inaugural CES Autonomous Racing Competition
Team KAIST led by Professor Hyunchul Shim and Unmanned Systems Research Group (USRG) placed fourth in an autonomous race car competition in Las Vegas last week, making its presence felt in the self-driving automotive tech industry.
Team KAIST, beat its first competitor, Auburn University, with speeds of up to 131 mph at the Autonomous Challenge at CES held at the Las Vegas Motor Speedway. However, the team failed to advance to the final round when it lost to PoliMOVE, comprised of the Polytechnic University of Milan and the University of Alabama, the final winner of the $150,000 USD race.
A total of eight teams competed in the self-driving race. The race was conducted as a single elimination tournament consisting of multiple rounds of matches. Two cars took turns playing the role of defender and attacker, and each car attempted to outpace the other until one of them was unable to complete the mission.
Each team designed the algorithm to control its racecar, the Dallara-built AV-21, which can reach a speed of up to 173 mph, and make it safely drive around the track at high speeds without crashing into the other.
The event is the CES version of the Indy Autonomous Challenge, a competition that took place for the first time in October last year to encourage university students from around the world to develop complicated software for autonomous driving and advance relevant technologies. Team KAIST placed 4th at the Indy Autonomous Challenge, which qualified it to participate in this race.
“The technical level of the CES race is much higher than last October’s and we had a very tough race. We advanced to the semifinals for two consecutive races. I think our autonomous vehicle technology is proving itself to the world,” said Professor Shim.
Professor Shim’s research group has been working on the development of autonomous aerial and ground vehicles for the past 12 years. A self-driving car developed by the lab was certified by the South Korean government to run on public roads.
The vehicle the team used cost more than 1 million USD to build. Many of the other teams had to repair their vehicle more than once due to accidents and had to spend a lot on repairs. “We are the only one who did not have any accidents, and this is a testament to our technological prowess,” said Professor Shim.
He said the financial funding to purchase pricy parts and equipment for the racecar is always a challenge given the very tight research budget and absence of corporate sponsorships.
However, Professor Shim and his research group plan to participate in the next race in September and in the 2023 CES race.
“I think we need more systemic and proactive research and support systems to earn better results but there is nothing better than the group of passionate students who are taking part in this project with us,” Shim added.
2022.01.12 View 13401 -
KAIST and KNUA to Collaborate on Culture Technology
Distinguished Visiting Scholar Soprano Sumi Jo Accompanied by AI pianist ‘VirtuosoNet’ during the Special Concert at KAIST
KAIST will expand the convergence of arts education and culture technology research in collaboration with the Korea National University of Arts (KNUA), the nation’s top arts university. KAIST President Kwang Hyung Lee signed an MOU with President Daejin Kim of the Korea National University of Art on January 6 at KAIST’s Daejeon campus for collaborations in arts education and research.
KAIST and KNUA will expand educational programs such as student exchanges and co-credit programs. The two universities will team up for cooperation focusing on research centers and academic conferences for the creation of culture technology and convergence arts.
Minister of Culture, Sports, and Tourism Hee Hwang also attended the ceremony. Minister Hwang said that the Ministry will invest 132 billion KRW in R&D for developing metaverse and content technologies. He added that this collaboration will be a very meaningful turning point for creating a new culture combining high-level technologies.
President Kim also expressed his expectations saying, “The collaboration of our two universities will generate a huge synergistic impact for nurturing talents and the creation of convergence arts.
President Lee said that the collaboration with KNUA will take KAIST another step forward as it aims to foster well-rounded talents. “We look forward to proactive collaborative research that will expand the new chapter of convergence arts and future stage performances.”
Right after the signing ceremony, world renowned soprano Sumi Jo, who was named a Distinguished Visiting Scholar, took the KAIST auditorium stage for a special concert. AI pianist ‘VirtuosoNet’, developed by Professor Juhan Nam at the Graduate School of Culture Technology, made its debut at the concert by playing Mozart’s Turkish March arranged by Arcardi Volrodos. VirtuosoNet also accompanied Soprano Jo on one of her songs.
The concert by Sumi Jo and AI pianist VirtuosoNet heralds what KAIST is pursuing for education and research in culture technology. The Graduate School of Culture Technology plans to conduct research on future culture industries combined with technologies for the metaverse. The Sumi Jo Performing Arts Research Center will conduct research on performing technologies together with virtual artists. Head of the Graduate School of Culture Technology Woontack Woo said that KAIST will expand the sphere of the culture industry including tourism in collaboration with KNUA by incorporating technology into arts.
2022.01.10 View 9206 -
AI Weather Forecasting Research Center Opens
The Kim Jaechul Graduate School of AI in collaboration with the National Institute of Meteorological Sciences (NIMS) under the National Meteorological Administration launched the AI Weather Forecasting Research Center last month.
The KAIST AI Weather Forecasting Research Center headed by Professor Seyoung Yoon was established with funding from from the AlphaWeather Development Research Project of the National Institute of Meteorological Sciences. KAIST was finally selected asas the project facilitator.
AlphaWeather is an AI system that utilizes and analyzes approximately approximately 150,000 ,000 pieces of weather information per hour to help weather forecasters produce accurate weather forecasts. The research center is composed of three research teams with the following goals: (a) developdevelop AI technology for precipitation nowcasting, (b) developdevelop AI technology for accelerating physical process-based numerical models, and (c) develop dAI technology for supporting weather forecasters. The teams consist of 15 staff member members from NIMS and 61 researchers from the Kim Jaechul Graduate School of AI at KAIST.
The research center is developing an AI algorithm for precipitation nowcasting (with up to six hours of lead time), which uses satellite images, radar reflectivity, and data collected from weather stations. It is also developing an AI algorithm for correcting biases in the prediction results from multiple numerical models. Finally, it is Finally, it is developing AI technology that supports weather forecasters by standardizing and automating repetitive manual processes. After verification, the the results obtained will be used by by the Korean National Weather Service as a next-generation forecasting/special-reporting system intelligence engine from 2026.
2022.01.10 View 8242 -
Face Detection in Untrained Deep Neural Networks
A KAIST team shows that primitive visual selectivity of faces can arise spontaneously in completely untrained deep neural networks
Researchers have found that higher visual cognitive functions can arise spontaneously in untrained neural networks. A KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering has shown that visual selectivity of facial images can arise even in completely untrained deep neural networks.
This new finding has provided revelatory insights into mechanisms underlying the development of cognitive functions in both biological and artificial neural networks, also making a significant impact on our understanding of the origin of early brain functions before sensory experiences.
The study published in Nature Communications on December 16 demonstrates that neuronal activities selective to facial images are observed in randomly initialized deep neural networks in the complete absence of learning, and that they show the characteristics of those observed in biological brains.
The ability to identify and recognize faces is a crucial function for social behavior, and this ability is thought to originate from neuronal tuning at the single or multi-neuronal level. Neurons that selectively respond to faces are observed in young animals of various species, and this raises intense debate whether face-selective neurons can arise innately in the brain or if they require visual experience.
Using a model neural network that captures properties of the ventral stream of the visual cortex, the research team found that face-selectivity can emerge spontaneously from random feedforward wirings in untrained deep neural networks. The team showed that the character of this innate face-selectivity is comparable to that observed with face-selective neurons in the brain, and that this spontaneous neuronal tuning for faces enables the network to perform face detection tasks.
These results imply a possible scenario in which the random feedforward connections that develop in early, untrained networks may be sufficient for initializing primitive visual cognitive functions.
Professor Paik said, “Our findings suggest that innate cognitive functions can emerge spontaneously from the statistical complexity embedded in the hierarchical feedforward projection circuitry, even in the complete absence of learning”.
He continued, “Our results provide a broad conceptual advance as well as advanced insight into the mechanisms underlying the development of innate functions in both biological and artificial neural networks, which may unravel the mystery of the generation and evolution of intelligence.” This work was supported by the National Research Foundation of Korea (NRF) and by the KAIST singularity research project.
-PublicationSeungdae Baek, Min Song, Jaeson Jang, Gwangsu Kim, and Se-Bum Baik, “Face detection in untrained deep neural network,” Nature Communications 12, 7328 on Dec.16, 2021
(https://doi.org/10.1038/s41467-021-27606-9)
-ProfileProfessor Se-Bum PaikVisual System and Neural Network LaboratoryProgram of Brain and Cognitive EngineeringDepartment of Bio and Brain EngineeringCollege of EngineeringKAIST
2021.12.21 View 12387