Science
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KAIST Proposes a New Way to Circumvent a Long-time Frustration in Neural Computing
The human brain begins learning through spontaneous random activities even before it receives sensory information from the external world. The technology developed by the KAIST research team enables much faster and more accurate learning when exposed to actual data by pre-learning random information in a brain-mimicking artificial neural network, and is expected to be a breakthrough in the development of brain-based artificial intelligence and neuromorphic computing technology in the future.
KAIST (President Kwang-Hyung Lee) announced on the 23rd of October that Professor Se-Bum Paik 's research team in the Department of Brain Cognitive Sciences solved the weight transport problem*, a long-standing challenge in neural network learning, and through this, explained the principles that enable resource-efficient learning in biological brain neural networks.
*Weight transport problem: This is the biggest obstacle to the development of artificial intelligence that mimics the biological brain. It is the fundamental reason why large-scale memory and computational work are required in the learning of general artificial neural networks, unlike biological brains.
Over the past several decades, the development of artificial intelligence has been based on error backpropagation learning proposed by Geoffery Hinton, who won the Nobel Prize in Physics this year. However, error backpropagation learning was thought to be impossible in biological brains because it requires the unrealistic assumption that individual neurons must know all the connected information across multiple layers in order to calculate the error signal for learning.
< Figure 1. Illustration depicting the method of random noise training and its effects >
This difficult problem, called the weight transport problem, was raised by Francis Crick, who won the Nobel Prize in Physiology or Medicine for the discovery of the structure of DNA, after the error backpropagation learning was proposed by Hinton in 1986. Since then, it has been considered the reason why the operating principles of natural neural networks and artificial neural networks will forever be fundamentally different.
At the borderline of artificial intelligence and neuroscience, researchers including Hinton have continued to attempt to create biologically plausible models that can implement the learning principles of the brain by solving the weight transport problem.
In 2016, a joint research team from Oxford University and DeepMind in the UK first proposed the concept of error backpropagation learning being possible without weight transport, drawing attention from the academic world. However, biologically plausible error backpropagation learning without weight transport was inefficient, with slow learning speeds and low accuracy, making it difficult to apply in reality.
KAIST research team noted that the biological brain begins learning through internal spontaneous random neural activity even before experiencing external sensory experiences. To mimic this, the research team pre-trained a biologically plausible neural network without weight transport with meaningless random information (random noise).
As a result, they showed that the symmetry of the forward and backward neural cell connections of the neural network, which is an essential condition for error backpropagation learning, can be created. In other words, learning without weight transport is possible through random pre-training.
< Figure 2. Illustration depicting the meta-learning effect of random noise training >
The research team revealed that learning random information before learning actual data has the property of meta-learning, which is ‘learning how to learn.’ It was shown that neural networks that pre-learned random noise perform much faster and more accurate learning when exposed to actual data, and can achieve high learning efficiency without weight transport.
< Figure 3. Illustration depicting research on understanding the brain's operating principles through artificial neural networks >
Professor Se-Bum Paik said, “It breaks the conventional understanding of existing machine learning that only data learning is important, and provides a new perspective that focuses on the neuroscience principles of creating appropriate conditions before learning,” and added, “It is significant in that it solves important problems in artificial neural network learning through clues from developmental neuroscience, and at the same time provides insight into the brain’s learning principles through artificial neural network models.”
This study, in which Jeonghwan Cheon, a Master’s candidate of KAIST Department of Brain and Cognitive Sciences participated as the first author and Professor Sang Wan Lee of the same department as a co-author, will be presented at the 38th Neural Information Processing Systems (NeurIPS), the world's top artificial intelligence conference, to be held in Vancouver, Canada from December 10 to 15, 2024. (Paper title: Pretraining with random noise for fast and robust learning without weight transport)
This study was conducted with the support of the National Research Foundation of Korea's Basic Research Program in Science and Engineering, the Information and Communications Technology Planning and Evaluation Institute's Talent Development Program, and the KAIST Singularity Professor Program.
2024.10.23 View 977 -
Revolutionary 'scLENS' Unveiled to Decode Complex Single-Cell Genomic Data
Unlocking biological information from complex single-cell genomic data has just become easier and more precise, thanks to the innovative 'scLENS' tool developed by the Biomedical Mathematics Group within the IBS Center for Mathematical and Computational Sciences led by Chief Investigator Jae Kyoung Kim, who is also a professor at KAIST. This new finding represents a significant leap forward in the field of single-cell transcriptomics.
Single-cell genomic analysis is an advanced technique that measures gene expression at the individual cell level, revealing cellular changes and interactions that are not observable with traditional genomic analysis methods. When applied to cancer tissues, this analysis can delineate the composition of diverse cell types within a tumor, providing insights into how cancer progresses and identifying key genes involved during each stage of progression.
Despite the immense potential of single-cell genomic analysis, handling the vast amount of data that it generates has always been challenging. The amount of data covers the expression of tens of thousands of genes across hundreds to thousands of individual cells. This not only results in large datasets but also introduces noise-related distortions, which arise in part due to current measurement limitations.
< Figure 1. Overview of scLENS (single-cell Low-dimensional embedding using the effective Noise Subtract) >
(Left) Current dimensionality reduction methods for scRNA-seq data involve conventional data preprocessing steps, such as log normalization, followed by manual selection of signals from the scaled data. However, this study reveals that the high levels of sparsity and variability in scRNA-seq data can lead to signal distortion during the data preprocessing, compromising the accuracy of downstream analyses.
(Right) To address this issue, the researchers integrated L2 normalization into the conventional preprocessing pipeline, effectively mitigating signal distortion. Moreover, they developed a novel signal detection algorithm that eliminates the need for user intervention by leveraging random matrix theory-based noise filtering and signal robustness testing. By incorporating these techniques, scLENS enables accurate and automated analysis of scRNA-seq data, overcoming the limitations of existing dimensionality reduction methods.
Corresponding author Jae Kyoung Kim highlighted, “There has been a remarkable advancement in experimental technologies for analyzing single-cell transcriptomes over the past decade. However, due to limitations in data analysis methods, there has been a struggle to fully utilize valuable data obtained through extensive cost and time."
Researchers have developed numerous analysis methods over the years to discern biological signals from this noise. However, the accuracy of these methods has been less than satisfactory. A critical issue is that determining signal and noise thresholds often depends on subjective decisions from the users.
The newly developed scLENS tool harnesses Random Matrix Theory and Signal robustness test to automatically differentiate signals from noise without relying on subjective user input.
First author Hyun Kim stated, "Previously, users had to arbitrarily decide the threshold for signal and noise, which compromised the reproducibility of analysis results and introduced subjectivity. scLENS eliminates this problem by automatically detecting signals using only the inherent structure of the data."
During the development of scLENS, researchers identified the fundamental reasons for inaccuracies in existing analysis methods. They found that commonly used data preprocessing methods distort both biological signals and noise. The new preprocessing approach that scLENS offers is free from such distortions.
By resolving issues related to noise threshold determined by subjective user choice and signal distortion in conventional data preprocessing, scLENS significantly outperforms existing methods in accuracy. Additionally, scLENS automates the laborious process of signal dimension selection, allowing researchers to extract biological signals conveniently and automatically.
CI Kim added, "scLENS solves major issues in single-cell transcriptome data analysis, substantially improving the accuracy and efficiency throughout the analysis process. This is a prime example of how fundamental mathematical theories can drive innovation in life sciences research, allowing researchers to more quickly and accurately answer biological questions and uncover secrets of life that were previously hidden."
This research was published in the international journal 'Nature Communications' on April 27.
Terminology
* Single-cell RNA sequencing (scRNA-seq): A technique used to measure gene expression levels in individual cells, providing insights into cell heterogeneity and rare cell types.
* Dimensionality reduction: A method to reduce the number of features or variables in a dataset while preserving the most important information, making data analysis more manageable and interpretable.
* Random matrix theory: A mathematical framework used to model and analyze the properties of large, random matrices, which can be applied to filter out noise in high-dimensional data.
* Signal robustness test: Among the signals, this test selects signals that are robust to the slight perturbation in data because real biological signals should be invariant for such slight modification in the data.
2024.05.09 View 2708 -
North Korea and Beyond: AI-Powered Satellite Analysis Reveals the Unseen Economic Landscape of Underdeveloped Nations
- A joint research team in computer science, economics, and geography has developed an artificial intelligence (AI) technology to measure grid-level economic development within six-square-kilometer regions.
- This AI technology is applicable in regions with limited statistical data (e.g., North Korea), supporting international efforts to propose policies for economic growth and poverty reduction in underdeveloped countries.
- The research team plans to make this technology freely available for use to contribute to the United Nations' Sustainable Development Goals (SDGs).
The United Nations reports that more than 700 million people are in extreme poverty, earning less than two dollars a day. However, an accurate assessment of poverty remains a global challenge. For example, 53 countries have not conducted agricultural surveys in the past 15 years, and 17 countries have not published a population census. To fill this data gap, new technologies are being explored to estimate poverty using alternative sources such as street views, aerial photos, and satellite images.
The paper published in Nature Communications demonstrates how artificial intelligence (AI) can help analyze economic conditions from daytime satellite imagery. This new technology can even apply to the least developed countries - such as North Korea - that do not have reliable statistical data for typical machine learning training.
The researchers used Sentinel-2 satellite images from the European Space Agency (ESA) that are publicly available. They split these images into small six-square-kilometer grids. At this zoom level, visual information such as buildings, roads, and greenery can be used to quantify economic indicators. As a result, the team obtained the first ever fine-grained economic map of regions like North Korea. The same algorithm was applied to other underdeveloped countries in Asia: North Korea, Nepal, Laos, Myanmar, Bangladesh, and Cambodia (see Image 1).
The key feature of their research model is the "human-machine collaborative approach," which lets researchers combine human input with AI predictions for areas with scarce data. In this research, ten human experts compared satellite images and judged the economic conditions in the area, with the AI learning from this human data and giving economic scores to each image. The results showed that the Human-AI collaborative approach outperformed machine-only learning algorithms.
< Image 1. Nightlight satellite images of North Korea (Top-left: Background photo provided by NASA's Earth Observatory). South Korea appears brightly lit compared to North Korea, which is mostly dark except for Pyongyang. In contrast, the model developed by the research team uses daytime satellite imagery to predict more detailed economic predictions for North Korea (top-right) and five Asian countries (Bottom: Background photo from Google Earth). >
The research was led by an interdisciplinary team of computer scientists, economists, and a geographer from KAIST & IBS (Donghyun Ahn, Meeyoung Cha, Jihee Kim), Sogang University (Hyunjoo Yang), HKUST (Sangyoon Park), and NUS (Jeasurk Yang). Dr Charles Axelsson, Associate Editor at Nature Communications, handled this paper during the peer review process at the journal.
The research team found that the scores showed a strong correlation with traditional socio-economic metrics such as population density, employment, and number of businesses. This demonstrates the wide applicability and scalability of the approach, particularly in data-scarce countries. Furthermore, the model's strength lies in its ability to detect annual changes in economic conditions at a more detailed geospatial level without using any survey data (see Image 2).
< Image 2. Differences in satellite imagery and economic scores in North Korea between 2016 and 2019. Significant development was found in the Wonsan Kalma area (top), one of the tourist development zones, but no changes were observed in the Wiwon Industrial Development Zone (bottom). (Background photo: Sentinel-2 satellite imagery provided by the European Space Agency (ESA)). >
This model would be especially valuable for rapidly monitoring the progress of Sustainable Development Goals such as reducing poverty and promoting more equitable and sustainable growth on an international scale. The model can also be adapted to measure various social and environmental indicators. For example, it can be trained to identify regions with high vulnerability to climate change and disasters to provide timely guidance on disaster relief efforts.
As an example, the researchers explored how North Korea changed before and after the United Nations sanctions against the country. By applying the model to satellite images of North Korea both in 2016 and in 2019, the researchers discovered three key trends in the country's economic development between 2016 and 2019. First, economic growth in North Korea became more concentrated in Pyongyang and major cities, exacerbating the urban-rural divide. Second, satellite imagery revealed significant changes in areas designated for tourism and economic development, such as new building construction and other meaningful alterations. Third, traditional industrial and export development zones showed relatively minor changes.
Meeyoung Cha, a data scientist in the team explained, "This is an important interdisciplinary effort to address global challenges like poverty. We plan to apply our AI algorithm to other international issues, such as monitoring carbon emissions, disaster damage detection, and the impact of climate change."
An economist on the research team, Jihee Kim, commented that this approach would enable detailed examinations of economic conditions in the developing world at a low cost, reducing data disparities between developed and developing nations. She further emphasized that this is most essential because many public policies require economic measurements to achieve their goals, whether they are for growth, equality, or sustainability.
The research team has made the source code publicly available via GitHub and plans to continue improving the technology, applying it to new satellite images updated annually. The results of this study, with Ph.D. candidate Donghyun Ahn at KAIST and Ph.D. candidate Jeasurk Yang at NUS as joint first authors, were published in Nature Communications under the title "A human-machine collaborative approach measures economic development using satellite imagery."
< Photos of the main authors. 1. Donghyun Ahn, PhD candidate at KAIST School of Computing 2. Jeasurk Yang, PhD candidate at the Department of Geography of National University of Singapore 3. Meeyoung Cha, Professor of KAIST School of Computing and CI at IBS 4. Jihee Kim, Professor of KAIST School of Business and Technology Management 5. Sangyoon Park, Professor of the Division of Social Science at Hong Kong University of Science and Technology 6. Hyunjoo Yang, Professor of the Department of Economics at Sogang University >
2023.12.07 View 4637 -
KAIST researchers find sleep delays more prevalent in countries of particular culture than others
Sleep has a huge impact on health, well-being and productivity, but how long and how well people sleep these days has not been accurately reported. Previous research on how much and how well we sleep has mostly relied on self-reports or was confined within the data from the unnatural environments of the sleep laboratories.
So, the questions remained: Is the amount and quality of sleep purely a personal choice? Could they be independent from social factors such as culture and geography?
< From left to right, Sungkyu Park of Kangwon National University, South Korea; Assem Zhunis of KAIST and IBS, South Korea; Marios Constantinides of Nokia Bell Labs, UK; Luca Maria Aiello of the IT University of Copenhagen, Denmark; Daniele Quercia of Nokia Bell Labs and King's College London, UK; and Meeyoung Cha of IBS and KAIST, South Korea >
A new study led by researchers at Korea Advanced Institute of Science and Technology (KAIST) and Nokia Bell Labs in the United Kingdom investigated the cultural and individual factors that influence sleep. In contrast to previous studies that relied on surveys or controlled experiments at labs, the team used commercially available smartwatches for extensive data collection, analyzing 52 million logs collected over a four-year period from 30,082 individuals in 11 countries. These people wore Nokia smartwatches, which allowed the team to investigate country-specific sleep patterns based on the digital logs from the devices.
< Figure comparing survey and smartwatch logs on average sleep-time, wake-time, and sleep durations. Digital logs consistently recorded delayed hours of wake- and sleep-time, resulting in shorter sleep durations. >
Digital logs collected from the smartwatches revealed discrepancies in wake-up times and sleep-times, sometimes by tens of minutes to an hour, from the data previously collected from self-report assessments. The average sleep-time overall was calculated to be around midnight, and the average wake-up time was 7:42 AM. The team discovered, however, that individuals' sleep is heavily linked to their geographical location and cultural factors. While wake-up times were similar, sleep-time varied by country. Individuals in higher GDP countries had more records of delayed bedtime. Those in collectivist culture, compared to individualist culture, also showed more records of delayed bedtime. Among the studied countries, Japan had the shortest total sleep duration, averaging a duration of under 7 hours, while Finland had the longest, averaging 8 hours.
Researchers calculated essential sleep metrics used in clinical studies, such as sleep efficiency, sleep duration, and overslept hours on weekends, to analyze the extensive sleep patterns. Using Principal Component Analysis (PCA), they further condensed these metrics into two major sleep dimensions representing sleep quality and quantity. A cross-country comparison revealed that societal factors account for 55% of the variation in sleep quality and 63% of the variation in sleep quantity.
Countries with a higher individualism index (IDV), which placed greater emphasis on individual achievements and relationships, had significantly longer sleep durations, which could be attributed to such societies having a norm of going to bed early. Spain and Japan, on the other hand, had the bedtime scheduled at the latest hours despite having the highest collectivism scores (low IDV). The study also discovered a moderate relationship between a higher uncertainty avoidance index (UAI), which measures implementation of general laws and regulation in daily lives of regular citizens, and better sleep quality.
Researchers also investigated how physical activity can affect sleep quantity and quality to see if individuals can counterbalance cultural influences through personal interventions. They discovered that increasing daily activity can improve sleep quality in terms of shortened time needed in falling asleep and waking up. Individuals who exercise more, however, did not sleep longer. The effect of exercise differed by country, with more pronounced effects observed in some countries, such as the United States and Finland. Interestingly, in Japan, no obvious effect of exercise could be observed. These findings suggest that the relationship between daily activity and sleep may differ by country and that different exercise regimens may be more effective in different cultures.
This research published on the Scientific Reports by the international journal, Nature, sheds light on the influence of social factors on sleep. (Paper Title "Social dimensions impact individual sleep quantity and quality" Article number: 9681)
One of the co-authors, Daniele Quercia, commented: “Excessive work schedules, long working hours, and late bedtime in high-income countries and social engagement due to high collectivism may cause bedtimes to be delayed.”
Commenting on the research, the first author Shaun Sungkyu Park said, "While it is intriguing to see that a society can play a role in determining the quantity and quality of an individual's sleep with large-scale data, the significance of this study is that it quantitatively shows that even within the same culture (country), individual efforts such as daily exercise can have a positive impact on sleep quantity and quality."
"Sleep not only has a great impact on one’s well-being but it is also known to be associated with health issues such as obesity and dementia," said the lead author, Meeyoung Cha. "In order to ensure adequate sleep and improve sleep quality in an aging society, not only individual efforts but also a social support must be provided to work together," she said. The research team will contribute to the development of the high-tech sleep industry by making a code that easily calculates the sleep indicators developed in this study available free of charge, as well as providing the benchmark data for various types of sleep research to follow.
2023.07.07 View 4923 -
‘Urban Green Space Affects Citizens’ Happiness’
Study finds the relationship between green space, the economy, and happiness
A recent study revealed that as a city becomes more economically developed, its citizens’ happiness becomes more directly related to the area of urban green space.
A joint research project by Professor Meeyoung Cha of the School of Computing and her collaborators studied the relationship between green space and citizen happiness by analyzing big data from satellite images of 60 different countries.
Urban green space, including parks, gardens, and riversides not only provides aesthetic pleasure, but also positively affects our health by promoting physical activity and social interactions. Most of the previous research attempting to verify the correlation between urban green space and citizen happiness was based on few developed countries. Therefore, it was difficult to identify whether the positive effects of green space are global, or merely phenomena that depended on the economic state of the country. There have also been limitations in data collection, as it is difficult to visit each location or carry out investigations on a large scale based on aerial photographs.
The research team used data collected by Sentinel-2, a high-resolution satellite operated by the European Space Agency (ESA) to investigate 90 green spaces from 60 different countries around the world. The subjects of analysis were cities with the highest population densities (cities that contain at least 10% of the national population), and the images were obtained during the summer of each region for clarity. Images from the northern hemisphere were obtained between June and September of 2018, and those from the southern hemisphere were obtained between December of 2017 and February of 2018.
The areas of urban green space were then quantified and crossed with data from the World Happiness Report and GDP by country reported by the United Nations in 2018. Using these data, the relationships between green space, the economy, and citizen happiness were analyzed.
The results showed that in all cities, citizen happiness was positively correlated with the area of urban green space regardless of the country’s economic state. However, out of the 60 countries studied, the happiness index of the bottom 30 by GDP showed a stronger correlation with economic growth. In countries whose gross national income (GDP per capita) was higher than 38,000 USD, the area of green space acted as a more important factor affecting happiness than economic growth. Data from Seoul was analyzed to represent South Korea, and showed an increased happiness index with increased green areas compared to the past.
The authors point out their work has several policy-level implications. First, public green space should be made accessible to urban dwellers to enhance social support. If public safety in urban parks is not guaranteed, its positive role in social support and happiness may diminish. Also, the meaning of public safety may change; for example, ensuring biological safety will be a priority in keeping urban parks accessible during the COVID-19 pandemic.
Second, urban planning for public green space is needed for both developed and developing countries. As it is challenging or nearly impossible to secure land for green space after the area is developed, urban planning for parks and green space should be considered in developing economies where new cities and suburban areas are rapidly expanding.
Third, recent climate changes can present substantial difficulty in sustaining urban green space. Extreme events such as wildfires, floods, droughts, and cold waves could endanger urban forests while global warming could conversely accelerate tree growth in cities due to the urban heat island effect. Thus, more attention must be paid to predict climate changes and discovering their impact on the maintenance of urban green space.
“There has recently been an increase in the number of studies using big data from satellite images to solve social conundrums,” said Professor Cha. “The tool developed for this investigation can also be used to quantify the area of aquatic environments like lakes and the seaside, and it will now be possible to analyze the relationship between citizen happiness and aquatic environments in future studies,” she added.
Professor Woo Sung Jung from POSTECH and Professor Donghee Wohn from the New Jersey Institute of Technology also joined this research. It was reported in the online issue of EPJ Data Science on May 30.
-PublicationOh-Hyun Kwon, Inho Hong, Jeasurk Yang, Donghee Y. Wohn, Woo-Sung Jung, andMeeyoung Cha, 2021. Urban green space and happiness in developed countries. EPJ Data Science. DOI: https://doi.org/10.1140/epjds/s13688-021-00278-7
-ProfileProfessor Meeyoung ChaData Science Labhttps://ds.ibs.re.kr/
School of Computing
KAIST
2021.06.21 View 9330 -
Extremely Stable Perovskite Nanoparticles Films for Next-Generation Displays
Researchers have reported an extremely stable cross-linked perovskite nanoparticle that maintains a high photoluminescence quantum yield (PLQY) for 1.5 years in air and harsh liquid environments. This stable material’s design strategies, which addressed one of the most critical problems limiting their practical application, provide a breakthrough for the commercialization of perovskite nanoparticles in next-generation displays and bio-related applications.
According to the research team led by Professor Byeong-Soo Bae, their development can survive in severe environments such as water, various polar solvents, and high temperature with high humidity without additional encapsulation. This development is expected to enable perovskite nanoparticles to be applied to high color purity display applications as a practical color converting material. This result was published as the inside front cover article in Advanced Materials.
Perovskites, which consist of organics, metals, and halogen elements, have emerged as key elements in various optoelectronic applications. The power conversion efficiency of photovoltaic cells based on perovskites light absorbers has been rapidly increased. Perovskites are also great promise as a light emitter in display applications because of their low material cost, facile wavelength tunability, high (PLQY), very narrow emission band width, and wider color gamut than inorganic semiconducting nanocrystals and organic emitters. Thanks to these advantages, perovskites have been identified as a key color-converting material for next-generation high color-purity displays. In particular, perovskites are the only luminescence material that meets Rec. 2020 which is a new color standard in display industry.
However, perovskites are very unstable against heat, moisture, and light, which makes them almost impossible to use in practical applications. To solve these problems, many researchers have attempted to physically prevent perovskites from coming into contact with water molecules by passivating the perovskite grain and nanoparticle surfaces with organic ligands or inorganic shell materials, or by fabricating perovskite-polymer nanocomposites. These methods require complex processes and have limited stability in ambient air and water. Furthermore, stable perovskite nanoparticles in the various chemical environments and high temperatures with high humidity have not been reported yet.
The research team in collaboration with Seoul National University develops siloxane-encapsulated perovskite nanoparticle composite films. Here, perovskite nanoparticles are chemically crosslinked with thermally stable siloxane molecules, thereby significantly improving the stability of the perovskite nanoparticles without the need for any additional protecting layer.
Siloxane-encapsulated perovskite nanoparticle composite films exhibited a high PLQY (> 70%) value, which can be maintained over 600 days in water, various chemicals (alcohol, strong acidic and basic solutions), and high temperatures with high humidity (85℃/85%). The research team investigated the mechanisms impacting the chemical crosslinking and water molecule-induced stabilization of perovskite nanoparticles through various photo-physical analysis and density-functional theory calculation.
The research team confirmed that displays based on their siloxane-perovskite nanoparticle composite films exhibited higher PLQY and a wider color gamut than those of Cd-based quantum dots and demonstrated perfect color converting properties on commercial mobile phone screens. Unlike what was commonly believed in the halide perovskite field, the composite films showed excellent bio-compatibility because the siloxane matrix prevents the toxicity of Pb in perovskite nanoparticle.
By using this technology, the instability of perovskite materials, which is the biggest challenge for practical applications, is greatly improved through simple encapsulation method.
“Perovskite nanoparticle is the only photoluminescent material that can meet the next generation display color standard. Nevertheless, there has been reluctant to commercialize it due to its moisture vulnerability. The newly developed siloxane encapsulation technology will trigger more research on perovskite nanoparticles as color conversion materials and will accelerate early commercialization,” Professor Bae said.
This work was supported by the Wearable Platform Materials Technology Center (WMC) of the Engineering Research Center (ERC) Project, and the Leadership Research Program funded by the National Research Foundation of Korea.
-Publication:
Junho Jang, Young-Hoon Kim, Sunjoon Park, Dongsuk Yoo, Hyunjin Cho, Jinhyeong Jang, Han Beom Jeong, Hyunhwan Lee, Jong Min Yuk, Chan Beum Park, Duk Young Jeon, Yong-Hyun Kim, Byeong-Soo Bae, and Tae-Woo Lee. “Extremely Stable Luminescent Crosslinked Perovskite Nanoparticles under Harsh Environments over 1.5 Years” Advanced Materials, 2020, 2005255. https://doi.org/10.1002/adma.202005255.
Link to download the full-text paper:
https://onlinelibrary.wiley.com/doi/10.1002/adma.202005255
-Profile: Prof. Byeong-Soo Bae (Corresponding author)
bsbae@kaist.ac.kr
Lab. of Optical Materials & Coating
Department of Materials Science and Engineering
Korea Advanced Institute of Science and Technology (KAIST)
2020.12.29 View 11042 -
Research on the Million Follower Fallacy Receives the Test of Time Award
Professor Meeyoung Cha’s research investigating the correlation between the number of followers on social media and its influence was re-highlighted after 10 years of publication of the paper.
Saying that her research is still as relevant today as the day it was published 10 years ago, the Association for the Advancement of Artificial Intelligence (AAAI) presented Professor Cha from the School of Computing with the Test of Time Award during the 14th International Conference on Web and Social Media (ICWSM) held online June 8 through 11.
In her 2010 paper titled ‘Measuring User Influence in Twitter: The Million Follower Fallacy,’ Professor Cha proved that number of followers does not match the influential power. She investigated the data including 54,981,152 user accounts, 1,963,263,821 social links, and 1,755,925,520 Tweets, collected with 50 servers.
The research compares and illustrates the limitations of various methods used to measure the influence a user has on a social networking platform. These results provided new insights and interpretations to the influencer selection algorithm used to maximize the advertizing impact on big social networking platforms.
The research also looked at how long an influential user was active for, and whether the user could freely cross the borders between fields and be influential on different topics as well. By analyzing cases of who becomes an influencer when new events occur, it was shown that a person could quickly become an influencer using several key tactics, unlike what was previously claimed by the ‘accidental influential theory’.
Professor Cha explained, “At the time, data from social networking platforms did not receive much attention in computer science, but I remember those all-nighters I pulled to work on this project, fascinated by the fact that internet data could be used to solve difficult social science problems. I feel so grateful that my research has been endeared for such a long time.”
Professor Cha received both her undergraduate and graduate degrees from KAIST, and conducted this research during her postdoctoral course at the Max Planck Institute in Germany. She now also serves as a chief investigator of a data science group at the Institute for Basic Science (IBS).
(END)
2020.06.22 View 6259 -
A Global Campaign of ‘Facts before Rumors’ on COVID-19 Launched
- A KAIST data scientist group responds to facts and rumors on COVID-19 for global awareness of the pandemic. -
Like the novel coronavirus, rumors have no borders. The world is fighting to contain the pandemic, but we also have to deal with the appalling spread of an infodemic that is as contagious as the virus. This infodemic, a pandemic of false information, is bringing chaos and extreme fear to the general public.
Professor Meeyoung Cha’s group at the School of Computing started a global campaign called ‘Facts before Rumors,’ to prevent the spread of false information from crossing borders. She explained, “We saw many rumors that had already been fact-checked long before in China and South Korea now begin to circulate in other countries, sometimes leading to detrimental results. We launched an official campaign, Facts before Rumors, to deliver COVID-19-related facts to countries where the number of cases is now increasing.” She released the first set of facts on March 26 via her Twitter account @nekozzang.
Professor Cha, a data scientist who has focused on detecting global fake news, is now part of the COVID-19 AI Task Force at the Global Strategy Institute at KAIST. She is also leading the Data Science Group at the Institute for Basic Science (IBS) as Chief Investigator.
Her research group worked in collaboration with the College of Nursing at Ewha Woman’s University to identify 15 claims about COVID-19 that circulated on social networks (SNS) and among the general public. The team fact-checked these claims based on information from the WHO and CDCs of Korea and the US. The research group is now working on translating the list of claims into Portuguese, Spanish, Persian, Chinese, Amharic, Hindi, and Vietnamese. Delivering facts before rumors, the team says, will help contain the disease and prevent any harm caused by misinformation.
The pandemic, which spread in China and South Korea before arriving in Europe and the US, is now moving into South America, Africa, and Southeast Asia. “We would like to play a part in preventing the further spread of the disease with the provision of only scientifically vetted, truthful facts,” said the team.
For this campaign, Professor Cha’s team investigated more than 200 rumored claims on COVID-19 in China during the early days of the pandemic. These claims spread in different levels: while some were only relevant locally or in larger regions of China, others propagated in Asia and are now spreading to countries that are currently most affected by the disease.
For example, the false claim which publicized that ‘Fireworks can help tame the virus in the air’ only spread in China. Other claims such as ‘Eating garlic helps people overcome the disease’ or ‘Gargling with salt water prevents the contraction of the disease,’ spread around the world even after being proved groundless.
The team noted, however, that the times at which these claims propagate are different from one country to another. “This opens up an opportunity to debunk rumors in some countries, even before they start to emerge,” said Professor Cha.
Kun-Woo Kim, a master’s candidate in the Department of Industrial Design who joined this campaign and designed the Facts before Rumors chart also expressed his hope that this campaign will help reduce the number of victims. He added, “I am very grateful to our scientists who quickly responded to the Fact Check in these challenging times.”
2020.03.27 View 10775 -
COVID-19 Map Shows How the Global Pandemic Moves
- A School of Computing team facilitated the data from COVID-19 to show the global spread of the virus. -
The COVID-19 map made by KAIST data scientists shows where and how the virus is spreading from China, reportedly the epicenter of the disease.
Professor Meeyoung Cha from the School of Computing and her group facilitated data based on the number of confirmed cases from January 22 to March 22 to analyze the trends of this global epidemic. The statistics include the number of confirmed cases, recoveries, and deaths across major continents based on the number of confirmed case data during that period.
The moving dot on the map strikingly shows how the confirmed cases are moving across the globe. According to their statistics, the centroid of the disease starts from near Wuhan in China and moved to Korea, then through the European region via Italy and Iran.
The data is collected by a graduate student from the School of Computing, Geng Sun, who started the process during the time he was quarantined since coming back from his home in China. An undergraduate colleague of Geng's, Gabriel Camilo Lima who made the map, is now working remotely from his home in Brazil since all undergraduate students were required to move out of the dormitory last week. The university closed all undergraduate housing and advised the undergraduate students to go back home in a preventive measure to stop the virus from spreading across the campus.
Gabriel said he calculated the centroid of all confirmed cases up to a given day. He explained, “I weighed each coordinate by the number of cases in that region and country and calculated an approximate center of gravity.”
“The Earth is round, so the shortest path from Asia to Europe is often through Russia. In early March, the center of gravity of new cases was moving from Asia to Europe. Therefore, the centroid is moving to the west and goes through Russia, even though Russia has not reported many cases,” he added.
Professor Cha, who is also responsible for the Data Science Group at the Institute for Basic Science (IBS) as the Chief Investigator, said their group will continue to update the map using public data at https://ds.ibs.re.kr/index.php/covid-19/.
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2020.03.27 View 10374 -
Three Professors Receive Han Sung Science Awards
Three KAIST professors swept the 2nd Han Sung Science Awards. Professor Bum-Ki Min from the Departments of Mechanical Engineering and Physics, Professor Sun-Kyu Han from the Department of Chemistry, and Professor Seung-Jae Lee from the Department of Biological Sciences won all three awards presented by the Han Sung Scholarship Foundation, which recognizes promising mid-career scientists in the fields of physics, chemistry, and biological sciences. The awards ceremony will take place on August 16 in Hwaseong.
Professor Min was declared as the winner of the physics field in recognition of his outstanding research activities including searching for new application areas for metamaterials and investigating their unexplored functionalities. The metamaterials with a high index of refraction developed by Professor Min’s research team have caught the attention of scientists worldwide, as they can help develop high-resolution imaging systems and ultra-small, hyper-sensitive optical devices.
The chemistry field winner, Professor Han, is the youngest awardee so far at 36 years of age. He is often described as one of the most promising next-generation Korean scientists in the field of the total synthesis of complex natural products. Given the fact that this field takes very long-term research, he is making unprecedented research achievements. He is focusing on convergent and flexible synthetic approaches that enable access to not only a single target but various natural products with structural and biosynthetic relevance as well as unnatural products with higher biological potency.
Professor Lee was recognized for his contributions to the advancement of biological sciences, especially in aging research. Professor Lee’s team is taking a novel approach by further investigating complex interactions between genetic and environmental factors that affect aging, and identifying genes that mediate the effects. The team has been conducting large-scale gene discovery efforts by employing RNA sequencing analysis, RNAi screening, and chemical mutagenesis screening. They are striving to determine the functional significance of candidate genes obtained from these experiments and mechanistically characterize these genes.
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2019.07.03 View 7899 -
On-chip Drug Screening for Identifying Antibiotic Interactions in Eight Hours
(from left: Seunggyu Kimand Professor Jessie Sungyun Jeon)
A KAIST research team developed a microfluidic-based drug screening chip that identifies synergistic interactions between two antibiotics in eight hours. This chip can be a cell-based drug screening platform for exploring critical pharmacological patterns of antibiotic interactions, along with potential applications in screening other cell-type agents and guidance for clinical therapies.
Antibiotic susceptibility testing, which determines types and doses of antibiotics that can effectively inhibit bacterial growth, has become more critical in recent years with the emergence of antibiotic-resistant pathogenic bacteria strains.
To overcome the antibiotic-resistant bacteria, combinatory therapy using two or more kinds of antibiotics has been gaining considerable attention. However, the major problem is that this therapy is not always effective; occasionally, unfavorable antibiotic pairs may worsen results, leading to suppressed antimicrobial effects. Therefore, combinatory testing is a crucial preliminary process to find suitable antibiotic pairs and their concentration range against unknown pathogens, but the conventional testing methods are inconvenient for concentration dilution and sample preparation, and they take more than 24 hours to produce the results.
To reduce time and enhance the efficiency of combinatory testing, Professor Jessie Sungyun Jeon from the Department of Mechanical Engineering, in collaboration with Professor Hyun Jung Chung from the Department of Biological Sciences, developed a high-throughput drug screening chip that generates 121 pairwise concentrations between two antibiotics.
The team utilized a microfluidic chip with a sample volume of a few tens of microliters. This chip enabled 121 pairwise concentrations of two antibiotics to be automatically formed in only 35 minutes.
They loaded a mixture of bacterial samples and agarose into the microchannel and injected reagents with or without antibiotics into the surrounding microchannel. The diffusion of antibiotic molecules from the channel with antibiotics to the one without antibiotics resulted in the formation of two orthogonal concentration gradients of the two antibiotics on the bacteria-trapping agarose gel.
The team observed the inhibition of bacterial growth by the antibiotic orthogonal gradients over six hours with a microscope, and confirmed different patterns of antibiotic pairs, classifying the interaction types into either synergy or antagonism.
Professor Jeon said, “The feasibility of microfluidic-based drug screening chips is promising, and we expect our microfluidic chip to be commercialized and utilized in near future.”
This study, led by Seunggyu Kim, was published in Lab on a Chip (10.1039/c8lc01406j) on March 21, 2019.
Figure 1. Back cover image for the “Lab on a Chip”.
Figure 2. Examples of testing results using the microfluidic chips developed in this research.
2019.04.18 View 29858 -
Flexible Drug Delivery Microdevice to Advance Precision Medicine
(Schematic view of flexible microdevice: The flexible drug delivery device for controlled release fabricated via inorganic laser lift off.)
A KAIST research team has developed a flexible drug delivery device with controlled release for personalized medicine, blazing the path toward theragnosis.
Theragnosis, an emerging medical technology, is gaining attention as key factor to advance precision medicine for its featuring simultaneous diagnosis and therapeutics. Theragnosis devices including smart contact lenses and microneedle patches integrate physiological data sensors and drug delivery devices. The controlled drug delivery boasts fewer side-effects, uniform therapeutic results, and minimal dosages compared to oral ingestion. Recently, some research groups conducted in-human applications of controlled-release bulky microchips for osteoporosis treatment. However they failed to demonstrate successful human-friendly flexible drug delivery systems for controlled release.
For this microdevice, the team under Professor Daesoo Kim from the Department of Biological Science and Professor Keon Jae Lee from the Department of Materials Science and Engineering, fabricated a device on a rigid substrate and transferred a 50 µm-thick active drug delivery layer to the flexible substrate via inorganic laser lift off. The fabricated device shows mechanical flexibility while maintaining the capability of precise administration of exact dosages at desired times. The core technology is to produce a freestanding gold capping layer directly on top of the microreservoir with the drugs inside, which had been regarded as impossible in conventional microfabrication.
The developed flexible drug delivery system can be applied to smart contact lenses or the brain disease treatments by implanting them into cramped and corrugated organs. In addition, when powered wirelessly, it will represent a novel platform for personalized medicine. The team already proved through animal experimentation that treatment for brain epilepsy made progress by releasing anti-epileptic medication through the device.
Professor Lee believes the flexible microdevice will further expand the applications of smart contact lenses, therapeutic treatments for brain disease, and subcutaneous implantations for daily healthcare system. This study “Flexible Wireless Powered Drug Delivery System for Targeted Administration on Cerebral Cortex” was described in the June online issue of Nano Energy.
(Photo: The flexible drug delivery device for contolled relase attached on a glass rod.)
2018.08.13 View 8195