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KAIST & LG U+ Team Up for Quantum Computing Solution for Ultra-Space 6G Satellite Networking
KAIST quantum computer scientists have optimized ultra-space 6G Low-Earth Orbit (LEO) satellite networking, finding the shortest path to transfer data from a city to another place via multi-satellite hops.
The research team led by Professor June-Koo Kevin Rhee and Professor Dongsu Han in partnership with LG U+ verified the possibility of ultra-performance and precision communication with satellite networks using D-Wave, the first commercialized quantum computer.
Satellite network optimization has remained challenging since the network needs to be reconfigured whenever satellites approach other satellites within the connection range in a three-dimensional space. Moreover, LEO satellites orbiting at 200~2000 km above the Earth change their positions dynamically, whereas Geo-Stationary Orbit (GSO) satellites do not change their positions. Thus, LEO satellite network optimization needs to be solved in real time.
The research groups formulated the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem and managed to solve the problem, incorporating the connectivity and link distance limits as the constraints.
The proposed optimization algorithm is reported to be much more efficient in terms of hop counts and path length than previously reported studies using classical solutions. These results verify that a satellite network can provide ultra-performance (over 1Gbps user-perceived speed), and ultra-precision (less than 5ms end-to-end latency) network services, which are comparable to terrestrial communication.
Once QUBO is applied, “ultra-space networking” is expected to be realized with 6G. Researchers said that an ultra-space network provides communication services for an object moving at up to 10 km altitude with an extreme speed (~ 1000 km/h). Optimized LEO satellite networks can provide 6G communication services to currently unavailable areas such as air flights and deserts.
Professor Rhee, who is also the CEO of Qunova Computing, noted, “Collaboration with LG U+ was meaningful as we were able to find an industrial application for a quantum computer. We look forward to more quantum application research on real problems such as in communications, drug and material discovery, logistics, and fintech industries.”
2022.06.17 View 9532 -
New Polymer Mesophase Structure Discovered
Bilayer-folded lamellar mesophase induced by random polymer sequence
Polymers, large molecules made up of repeating smaller molecules called monomers, are found in nearly everything we use in our day-to-day lives. Polymers can be natural or created synthetically. Natural polymers, also called biopolymers, include DNA, proteins, and materials like silk, gelatin, and collagen. Synthetic polymers make up many different kinds of materials, including plastic, that are used in constructing everything from toys to industrial fiber cables to brake pads.
As polymers are formed through a process called polymerization, the monomers are connected through a chain. As the chain develops, the structure of the polymer determines its unique physical and chemical properties. Researchers are continually studying polymers, how they form, how they are structured, and how they develop these unique properties. By understanding this information, scientists can develop new uses for polymers and create new materials that can be used in a wide variety of industries.
In a paper published in Nature Communications on May 4, researchers describe a new structure found in an aqueous solution of an amphiphilic copolymer, called a bilayer-folded lamellar mesophase, that has been discovered through a random copolymer sequence.
“A new mesophase is an important discovery as it shows a new way for molecules to self-organize,” said Professor Myungeun Seo at the Department of Chemistry at KAIST. “We were particularly thrilled to identify this bilayer-folded lamellar phase because pure bilayer membranes are difficult to fold thermodynamically.”
Researchers think that this mesophase structure comes from the sequence of the monomers within the copolymer. The way the different monomers arrange themselves in the chain that makes up a copolymer is important and can have implications for what the copolymer can do. Many copolymers are random, which means that their structure relies on how the monomers interact with each other. In this case, the interaction between the hydrophobic monomers associates the copolymer chains to conceal the hydrophobic domain from water. As the structure gets more complex, researchers have found that a visible order develops so that monomers can be matched up with the right pair.
“While we tend to think random means disorder, here we showed that a periodic order can spontaneously arise from the random copolymer sequence based on their collective behavior,” said Professor Seo. “We believe this comes from the sequence matching problem: finding a perfectly complementary pair for a long sequence is nearly impossible.”
This is what creates the unique structure of this newly discovered mesophase. The copolymer spontaneously folds and creates a multilamellar structure that is separated by water. A multilamellar structure refers to plate-like folds and the folded layers stack on top of each other. The resulting mesophase is birefringent, meaning light refracts through it, it is similar to liquid crystalline, and viscoelastic, which means that it is both viscous and elastic at the same time.
Looking ahead, researchers hope to learn more about this new mesophase and figure out how to control the outcome. Once more is understood about the mesophase and how it is formed, it’s possible that new mesophases could be discovered as more sequences are researched. “One of the obvious questions for us is how to control the folding frequency and adjust the folded height, which we are currently working to address. Ultimately, we want to understand how different multinary sequences can associate with another to create order and apply the knowledge to develop new materials,” said Professor Seo. The National Research Foundation, the Ministry of Education, and the Ministry of Science and ICT of Korea funded this research.
-PublicationMinjoong Shin, Hayeon Kim, Geonhyeong Park, Jongmin Park, Hyungju Ahn, Dong Ki Yoon, Eunji Lee, Myungeun Seo, “Bilayer-folded lamellar mesophase induced by random polymersequence,” May 4, 2022, Nature Communications (https://doi.org/10.1038/s41467-022-30122-z)
-ProfileProfessor Myungeun SeoMacromolecular Materials Chemistry Lab (https://nanopsg.kaist.ac.kr/)Department of ChemistryCollege of Natural SciencesKAIST
2022.06.17 View 9240 -
Professor Juho Kim’s Team Wins Best Paper Award at ACM CHI 2022
The research team led by Professor Juho Kim from the KAIST School of Computing won a Best Paper Award and an Honorable Mention Award at the Association for Computing Machinery Conference on Human Factors in Computing Systems (ACM CHI) held between April 30 and May 6.
ACM CHI is the world’s most recognized conference in the field of human computer interactions (HCI), and is ranked number one out of all HCI-related journals and conferences based on Google Scholar’s h-5 index. Best paper awards are given to works that rank in the top one percent, and honorable mention awards are given to the top five percent of the papers accepted by the conference.
Professor Juho Kim presented a total of seven papers at ACM CHI 2022, and tied for the largest number of papers. A total of 19 papers were affiliated with KAIST, putting it fifth out of all participating institutes and thereby proving KAIST’s competence in research.
One of Professor Kim’s research teams composed of Jeongyeon Kim (first author, MS graduate) from the School of Computing, MS candidate Yubin Choi from the School of Electrical Engineering, and Dr. Meng Xia (post-doctoral associate in the School of Computing, currently a post-doctoral associate at Carnegie Mellon University) received a best paper award for their paper, “Mobile-Friendly Content Design for MOOCs: Challenges, Requirements, and Design Opportunities”.
The study analyzed the difficulties experienced by learners watching video-based educational content in a mobile environment and suggests guidelines for solutions. The research team analyzed 134 survey responses and 21 interviews, and revealed that texts that are too small or overcrowded are what mainly brings down the legibility of video contents. Additionally, lighting, noise, and surrounding environments that change frequently are also important factors that may disturb a learning experience.
Based on these findings, the team analyzed the aptness of 41,722 frames from 101 video lectures for mobile environments, and confirmed that they generally show low levels of adequacy. For instance, in the case of text sizes, only 24.5% of the frames were shown to be adequate for learning in mobile environments. To overcome this issue, the research team suggested a guideline that may improve the legibility of video contents and help overcome the difficulties arising from mobile learning environments.
The importance of and dependency on video-based learning continue to rise, especially in the wake of the pandemic, and it is meaningful that this research suggested a means to analyze and tackle the difficulties of users that learn from the small screens of mobile devices. Furthermore, the paper also suggested technology that can solve problems related to video-based learning through human-AI collaborations, enhancing existing video lectures and improving learning experiences. This technology can be applied to various video-based platforms and content creation.
Meanwhile, a research team composed of Ph.D. candidate Tae Soo Kim (first author), MS candidate DaEun Choi, and Ph.D. candidate Yoonseo Choi from the School of Computing received an honorable mention award for their paper, “Stylette: styling the Web with Natural Language”.
The research team developed a novel interface technology that allows nonexperts who are unfamiliar with technical jargon to edit website features through speech. People often find it difficult to use or find the information they need from various websites due to accessibility issues, device-related constraints, inconvenient design, style preferences, etc. However, it is not easy for laymen to edit website features without expertise in programming or design, and most end up just putting up with the inconveniences. But what if the system could read the intentions of its users from their everyday language like “emphasize this part a little more”, or “I want a more modern design”, and edit the features automatically?
Based on this question, Professor Kim’s research team developed ‘Stylette’, a system in which AI analyses its users’ speech expressed in their natural language and automatically recommends a new style that best fits their intentions. The research team created a new system by putting together language AI, visual AI, and user interface technologies. On the linguistic side, a large-scale language model AI converts the intentions of the users expressed through their everyday language into adequate style elements. On the visual side, computer vision AI compares 1.7 million existing web design features and recommends a style adequate for the current website. In an experiment where 40 nonexperts were asked to edit a website design, the subjects that used this system showed double the success rate in a time span that was 35% shorter compared to the control group.
It is meaningful that this research proposed a practical case in which AI technology constructs intuitive interactions with users. The developed technology can be applied to existing design applications and web browsers in a plug-in format, and can be utilized to improve websites or for advertisements by collecting the natural intention data of users on a large scale.
2022.06.13 View 9110 -
Professor Iickho Song Publishes a Book on Probability and Random Variables in English
Professor Iickho Song from the School of Electrical Engineering has published a book on probability and random variables in English. This is the translated version of his book in Korean ‘Theory of Random Variables’, which was selected as an Excellent Book of Basic Sciences by the National Academy of Sciences and the Ministry of Education in 2020.
The book discusses diverse concepts, notions, and applications concerning probability and random variables, explaining basic concepts and results in a clearer and more complete manner.
Readers will also find unique results on the explicit general formula of joint moments and the expected values of nonlinear functions for normal random vectors. In addition, interesting applications for the step and impulse functions in discussions on random vectors are presented. Thanks to a wealth of examples and a total of 330 practice problems of varying difficulty, readers will have the opportunity to significantly expand their knowledge and skills. The book includes an extensive index, allowing readers to quickly and easily find what they are looking for. It also offers a valuable reference guide for experienced scholars and professionals, helping them review and refine their expertise.
Link: https://link.springer.com/book/10.1007/978-3-030-97679-8
2022.06.13 View 5114 -
Professor Jae-Woong Jeong Receives Hyonwoo KAIST Academic Award
Professor Jae-Woong Jeong from the School of Electrical Engineering was selected for the Hyonwoo KAIST Academic Award, funded by the HyonWoo Cultural Foundation (Chairman Soo-il Kwak, honorary professor at Seoul National University Business School).
The Hyonwoo KAIST Academic Award, presented for the first time in 2021, is an award newly founded by the donations of Chairman Soo-il Kwak of the HyonWoo Cultural Foundation, who aims to reward excellent KAIST scholars who have made outstanding academic achievements.
Every year, through the strict evaluations of the selection committee of the HyonWoo Cultural Foundation and the faculty reward recommendation board, KAIST will choose one faculty member that may represent the school with their excellent academic achievement, and reward them with a plaque and 100 million won.
Professor Jae-Woong Jeong, the winner of this year’s award, developed the first IoT-based wireless remote brain neural network control system to overcome brain diseases, and has been leading the field. The research was published in 2021 in Nature Biomedical Engineering, one of world’s best scientific journals, and has been recognized as a novel technology that suggested a new vision for the automation of brain research and disease treatment. This study, led by Professor Jeong’s research team, was part of the KAIST College of Engineering Global Initiative Interdisciplinary Research Project, and was jointly studied by Washington University School of Medicine through an international research collaboration. The technology was introduced more than 60 times through both domestic and international media, including Medical Xpress, MBC News, and Maeil Business News.
Professor Jeong has also developed a wirelessly chargeable soft machine for brain transplants, and the results were published in Nature Communications. He thereby opened a new paradigm for implantable semi-permanent devices for transplants, and is making unprecedented research achievements.
2022.06.13 View 8716 -
2022 KAIST Research Day Recognizes 10 Outstanding Researches
On May 31, the 2022 KAIST Research Day was held at the Jeong Geun-mo Conference Hall at KAIST’s main campus.
Since 2016, Research Day has been a yearly festival for researchers at KAIST. By introducing major research achievements and providing opportunities for information exchanges in R&D, it aims to create an atmosphere for mutual cooperation and communication amongst researchers, thereby vitalizing interdisciplinary research.
At this year’s event, 10 faculty members and their representative research achievements were rewarded. As the winner of the Grand Prize for Research, Professor Il-Doo Kim (Department of Materials Science and Engineering) gave a lecture on his topic, “Ultrasensitive flexible chemical sensor”.
With rising attention being given to environmental safety and healthcare, the importance of mobile sensors for trace amounts of molecules that can quickly raise hazard signals and allow early diagnosis from breath analysis have been brought to light. The lecture will break down ultrasensitive chemical sensor development cases, and introduced how gas sensor technologies developed at KAIST in particular are being applied at semiconductor and display fabrication plants for environmental and safety analyses and hazard prevention.
Professor Il-Doo Kim is a recognized researcher for his inventive achievements in the fields of respiratory gas sensor technology for early disease monitoring, and ordered nanofiber membranes for antiviral and fine dust filters.
Professor Kim has so far published 343 international research papers, received 56 journal covers, been awarded 230 domestic and international patents, and completed 12 technology transfers. He has also received a presidential award on the 51st invention day in 2016, Scientist of the Year Award selected by reporters in 2019, and has been named a fellow in the engineering division of the Korean Academy of Science and Technology in 2022.
Professor Kwang-Hyun Cho at the Department of Bio and Brain Engineering and Professor Doh Chang Lee at the Department of Chemical and Biomolecular Engineering were each awarded the Research Award, and Professor Dongsoo Han at the School of Computing received the Innovation Award.
Professors Buhm Soon Park at the Graduate School of Science and Technology Policy, Changick Kim at the School of Electrical Engineering and Hyun Jung Cho at the School of Digital Humanities and Computational Social Sciences received the Interdisciplinary Research Award as a team.
The passion and experiences of the awardees are to be introduced to undergraduate and graduate students as well as fellow researchers through a pre-recorded video lecture, while the lecture of the winner of the grand prize will be delivered on site.
Meanwhile, the top ten R&D achievements of KAIST selected excellent research outcomes from the natural and biological sciences including “Polariton-based PT symmetry laser that turns loss into gain” (Professor Yong-Hoon Cho at the Department of Physics), “Solution to the Riemann Problem including weak shock waves in 1-dimensional space” (Professor Moon-Jin Kang at the Department of Mathematical Sciences), and “Characterization of immune reaction in COVID-19 patients” (Professor Eui-Cheol Shin at the Graduate School of Medical Science and Engineering.)
Awardees from the engineering field included “Fluid surface stabilization technology using plasma jet” (Professor Wonho Choe at the Department of Nuclear and Quantum Engineering, “Visual recognition technology using event-based cameras” (Professor Kuk-Jin Yoon at theDepartment of Mechanical Engineering, “Artificial sensory system development through neural signal mimicry” (Professor Seongjun Park at the Department of Bio and Brain Engineering, “Mott transition material-based ultrahigh speed, low-power, and deformation-resistant true random number generator” (Professor Kyung Min Kim at the Department of Materials Science and Engineering, “Investment service design based on Aline: ESG” (Professor Sangsu Lee at the Department of Industrial Design), “Structural color printing technology without chemical colorings” (Professor Shin-Hyun Kim at the Department of Chemical and Biomolecular Engineering), and “Differentiable transient optical transfer simulation” (Professor Minhyuk Kim at the School of Computing)
To encourage the participation of members of KAIST, all parts of the ceremony will be broadcast live through YouTube in both English and Korean.” He added, “Offline audiences will congratulate the awardees at Fusion Hall in the KI Building and gain research ideas.”
2022.06.10 View 9219 -
Neuromorphic Memory Device Simulates Neurons and Synapses
Simultaneous emulation of neuronal and synaptic properties promotes the development of brain-like artificial intelligence
Researchers have reported a nano-sized neuromorphic memory device that emulates neurons and synapses simultaneously in a unit cell, another step toward completing the goal of neuromorphic computing designed to rigorously mimic the human brain with semiconductor devices.
Neuromorphic computing aims to realize artificial intelligence (AI) by mimicking the mechanisms of neurons and synapses that make up the human brain. Inspired by the cognitive functions of the human brain that current computers cannot provide, neuromorphic devices have been widely investigated. However, current Complementary Metal-Oxide Semiconductor (CMOS)-based neuromorphic circuits simply connect artificial neurons and synapses without synergistic interactions, and the concomitant implementation of neurons and synapses still remains a challenge. To address these issues, a research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering implemented the biological working mechanisms of humans by introducing the neuron-synapse interactions in a single memory cell, rather than the conventional approach of electrically connecting artificial neuronal and synaptic devices.
Similar to commercial graphics cards, the artificial synaptic devices previously studied often used to accelerate parallel computations, which shows clear differences from the operational mechanisms of the human brain. The research team implemented the synergistic interactions between neurons and synapses in the neuromorphic memory device, emulating the mechanisms of the biological neural network. In addition, the developed neuromorphic device can replace complex CMOS neuron circuits with a single device, providing high scalability and cost efficiency.
The human brain consists of a complex network of 100 billion neurons and 100 trillion synapses. The functions and structures of neurons and synapses can flexibly change according to the external stimuli, adapting to the surrounding environment. The research team developed a neuromorphic device in which short-term and long-term memories coexist using volatile and non-volatile memory devices that mimic the characteristics of neurons and synapses, respectively. A threshold switch device is used as volatile memory and phase-change memory is used as a non-volatile device. Two thin-film devices are integrated without intermediate electrodes, implementing the functional adaptability of neurons and synapses in the neuromorphic memory.
Professor Keon Jae Lee explained, "Neurons and synapses interact with each other to establish cognitive functions such as memory and learning, so simulating both is an essential element for brain-inspired artificial intelligence. The developed neuromorphic memory device also mimics the retraining effect that allows quick learning of the forgotten information by implementing a positive feedback effect between neurons and synapses.”
This result entitled “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse” was published in the May 19, 2022 issue of Nature Communications.
-Publication:Sang Hyun Sung, Tae Jin Kim, Hyera Shin, Tae Hong Im, and Keon Jae Lee (2022) “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse,” Nature Communications May 19, 2022 (DOI: 10.1038/s41467-022-30432-2)
-Profile:Professor Keon Jae Leehttp://fand.kaist.ac.kr
Department of Materials Science and EngineeringKAIST
2022.05.20 View 14424 -
Energy-Efficient AI Hardware Technology Via a Brain-Inspired Stashing System
Researchers demonstrate neuromodulation-inspired stashing system for the energy-efficient learning of a spiking neural network using a self-rectifying memristor array
Researchers have proposed a novel system inspired by the neuromodulation of the brain, referred to as a ‘stashing system,’ that requires less energy consumption. The research group led by Professor Kyung Min Kim from the Department of Materials Science and Engineering has developed a technology that can efficiently handle mathematical operations for artificial intelligence by imitating the continuous changes in the topology of the neural network according to the situation. The human brain changes its neural topology in real time, learning to store or recall memories as needed. The research group presented a new artificial intelligence learning method that directly implements these neural coordination circuit configurations.
Research on artificial intelligence is becoming very active, and the development of artificial intelligence-based electronic devices and product releases are accelerating, especially in the Fourth Industrial Revolution age. To implement artificial intelligence in electronic devices, customized hardware development should also be supported. However most electronic devices for artificial intelligence require high power consumption and highly integrated memory arrays for large-scale tasks. It has been challenging to solve these power consumption and integration limitations, and efforts have been made to find out how the human brain solves problems.
To prove the efficiency of the developed technology, the research group created artificial neural network hardware equipped with a self-rectifying synaptic array and algorithm called a ‘stashing system’ that was developed to conduct artificial intelligence learning. As a result, it was able to reduce energy by 37% within the stashing system without any accuracy degradation. This result proves that emulating the neuromodulation in humans is possible.
Professor Kim said, "In this study, we implemented the learning method of the human brain with only a simple circuit composition and through this we were able to reduce the energy needed by nearly 40 percent.”
This neuromodulation-inspired stashing system that mimics the brain’s neural activity is compatible with existing electronic devices and commercialized semiconductor hardware. It is expected to be used in the design of next-generation semiconductor chips for artificial intelligence.
This study was published in Advanced Functional Materials in March 2022 and supported by KAIST, the National Research Foundation of Korea, the National NanoFab Center, and SK Hynix.
-Publication:
Woon Hyung Cheong, Jae Bum Jeon†, Jae Hyun In, Geunyoung Kim, Hanchan Song, Janho An, Juseong Park, Young Seok Kim, Cheol Seong Hwang, and Kyung Min Kim (2022)
“Demonstration of Neuromodulation-inspired Stashing System for Energy-efficient Learning of Spiking Neural Network using a Self-Rectifying Memristor Array,” Advanced FunctionalMaterials March 31, 2022 (DOI: 10.1002/adfm.202200337)
-Profile:
Professor Kyung Min Kimhttp://semi.kaist.ac.kr https://scholar.google.com/citations?user=BGw8yDYAAAAJ&hl=ko
Department of Materials Science and EngineeringKAIST
2022.05.18 View 11692 -
Sumi Jo Performing Arts Research Center Opens
Distinguished visiting scholar soprano Sumi Jo gave a special lecture on May 13 at the KAIST auditorium. During the lecture, she talked about new technologies that will be introduced for future performing art stages while sharing some of the challenges she experienced before reaching to the stardom of the world stage. She also joined the KAIST student choral club ‘Chorus’ to perform the KAIST school song.
Professor Jo also opened the Sumi Jo Performing Arts Research Center on the same day along with President Kwang Hyung Lee and faculty members from the Graduate School of Culture Technology. The center will conduct AI and metaverse-based performing art technologies such as performer modeling via AI playing and motion creation, interactions between virtual and human players via sound analysis and motion recognition, as well as virtual stage and performing center modeling. The center will also carry out extensive stage production research applied to media convergence technologies.
Professor Juhan Nam, who heads the research center, said that the center is seeking collaborations with other universities such as Seoul National University and the Korea National University of Arts as well as top performing artists at home and abroad. He looks forward to the center growing into a collaborative center for future performing arts.
Professor Jo added that she will spare no effort to offer her experience and advice for the center’s future-forward performing arts research projects.
2022.05.16 View 7079 -
Professor Hyo-Sang Shin at Cranfield University Named the 18th Jeong Hun Cho Awardee
Professor Hyo-Sang Shin at Cranfield University in the UK was named the 18th Jeong Hun Cho Award recipient. PhD candidate Kyu-Sob Kim from the Department of Aerospace Engineering at KAIST, Master’s candidate from Korea University Kon-Hee Chang, Jae-Woo Chang from Kongju National University High School were also selected.
Professor Shin, a PhD graduate from the KAIST Department of Aerospace Engineering in 2016 works at Cranfield University. Professor Shin, whose main research focus covers guidance, navigation, and control, conducts research on information-based control. He has published 66 articles in SCI journals and presented approximately 70 papers at academic conference with more than 12 patent registrations. He is known for his expertise in areas related to unmanned aerospace systems and urban aero traffic automation. Professor Shin is participating in various aerospace engineering development projects run by the UK government.
The award recognizes promising young scientists who have made significant achievements in the field of aerospace engineering in honor of Jeong Hun Cho, the former PhD candidate in KAIST’s Department of Aerospace Engineering. Cho died in a lab accident in May 2003. Cho’s family endowed the award and scholarship to honor him and a recipient from each of his three alma maters (Kongju National High School, Korea University, and KAIST) are selected every year.
Professor Shin was awarded 25 million KRW in prize money. KAIST student Kim and Korea University student Chang received four million KRW while Kongju National University High School student Chang received three million KRW.
2022.05.16 View 7214 -
Professor Sang Su Lee’s Team Wins Seven iF Design Awards 2022
Professor Sang Su Lee from the Department of Industrial Design and his team’s five apps made in collaboration with NH Investment and Securities won iF Design Awards in the fields of UI, UX, service design, product design, and communication. These apps are now offered as NH Investment and Securities mobile applications.
The iF Design Awards recognize top quality creativity in product design, communication, packaging, service design and concepts, and architecture and interior design, as well as user experience (UX) and interface for digital media (UI).
In the field of UI, ‘Gretell’ is a mobile stock investment app service designed by Lee and his team to support investors struggling to learn about investing by archiving personalized information. Gretell provides investment information including news and reports. Users learn, evaluate, and leave comments. This shows both quantitative and qualitative indications, leading to rational decision-making. Other user’s comments are shared to reduce confirmation bias. Through this process, Gretell helps users who are impulsive or easily swayed by others’ opinions to grow as independent investors.
‘Bright’ is another app created by Lee’s team. It helps people exercise their rights as shareholders. As the need to exercise shareholders’ rights increases, many people are frustrated that investors with a small number of shares don’t have a lot of power. Bright provides a space for shareholders to share their opinions and brings people together so that individuals can be more proactive as shareholders. The Integrated Power of Attorney System (IPAS) expands the chances for shareholders to exercise their rights and allows users to submit proposals that can be communicated during the general meeting. Bright fosters influential shareholders, responsible companies, and a healthy society.
For communications, ‘Rewind’ is a stock information services app that visualizes past stock charts through sentiment analysis. Existing services focus on numbers, while Rewind takes a qualitative approach. Rewind analyzes public sentiment toward each event by collecting opinions on social media and then visualizes them chronologically along with the stock chart. Rewind allows users to review stock market movements and record their thoughts. Users can gain their own insights into current events in the stock market and make wiser investment decisions. The intuitive color gradient design provides a pleasant and simplified information experience.
In the area of interfaces for digital media and service design, ‘Groo’ is a green bond investing service app that helps users participate in green investment though investing in green bonds that support green projects for environmental improvement. Not restricted to trading bonds, Groo joins users in the holistic experience of green investing, from taking an interest in environmental issues to confirming the impact of the investment.
Next, ‘Modu’ is a story-based empathy expression training game for children with intellectual disabilities. Modu was developed to support emotion recognition and empathy behavior training in children with mild intellectual disabilities (MID) and borderline intellectual functioning (BIF).
Finally, the diving VR device for neutral buoyancy training, ‘Blow-yancy’, also made winner’s list. The device mimics scuba diving training without having to go into the water, therefore beginner divers are able getting feeling of diving while remaining perfectly safe and not harming any corals. It is expected that the device will be able to help protect at-risk underwater ecosystems.
2022.05.10 View 9097 -
Machine Learning-Based Algorithm to Speed up DNA Sequencing
The algorithm presents the first full-fledged, short-read alignment software that leverages learned indices for solving the exact match search problem for efficient seeding
The human genome consists of a complete set of DNA, which is about 6.4 billion letters long. Because of its size, reading the whole genome sequence at once is challenging. So scientists use DNA sequencers to produce hundreds of millions of DNA sequence fragments, or short reads, up to 300 letters long. Then the DNA sequencer assembles all the short reads like a giant jigsaw puzzle to reconstruct the entire genome sequence. Even with very fast computers, this job can take hours to complete.
A research team at KAIST has achieved up to 3.45x faster speeds by developing the first short-read alignment software that uses a recent advance in machine-learning called a learned index.
The research team reported their findings on March 7, 2022 in the journal Bioinformatics. The software has been released as open source and can be found on github (https://github.com/kaist-ina/BWA-MEME).
Next-generation sequencing (NGS) is a state-of-the-art DNA sequencing method. Projects are underway with the goal of producing genome sequencing at population scale. Modern NGS hardware is capable of generating billions of short reads in a single run. Then the short reads have to be aligned with the reference DNA sequence. With large-scale DNA sequencing operations running hundreds of next-generation sequences, the need for an efficient short read alignment tool has become even more critical. Accelerating the DNA sequence alignment would be a step toward achieving the goal of population-scale sequencing. However, existing algorithms are limited in their performance because of their frequent memory accesses.
BWA-MEM2 is a popular short-read alignment software package currently used to sequence the DNA. However, it has its limitations. The state-of-the-art alignment has two phases – seeding and extending. During the seeding phase, searches find exact matches of short reads in the reference DNA sequence. During the extending phase, the short reads from the seeding phase are extended. In the current process, bottlenecks occur in the seeding phase. Finding the exact matches slows the process.
The researchers set out to solve the problem of accelerating the DNA sequence alignment. To speed the process, they applied machine learning techniques to create an algorithmic improvement. Their algorithm, BWA-MEME (BWA-MEM emulated) leverages learned indices to solve the exact match search problem. The original software compared one character at a time for an exact match search. The team’s new algorithm achieves up to 3.45x faster speeds in seeding throughput over BWA-MEM2 by reducing the number of instructions by 4.60x and memory accesses by 8.77x. “Through this study, it has been shown that full genome big data analysis can be performed faster and less costly than conventional methods by applying machine learning technology,” said Professor Dongsu Han from the School of Electrical Engineering at KAIST.
The researchers’ ultimate goal was to develop efficient software that scientists from academia and industry could use on a daily basis for analyzing big data in genomics. “With the recent advances in artificial intelligence and machine learning, we see so many opportunities for designing better software for genomic data analysis. The potential is there for accelerating existing analysis as well as enabling new types of analysis, and our goal is to develop such software,” added Han.
Whole genome sequencing has traditionally been used for discovering genomic mutations and identifying the root causes of diseases, which leads to the discovery and development of new drugs and cures. There could be many potential applications. Whole genome sequencing is used not only for research, but also for clinical purposes. “The science and technology for analyzing genomic data is making rapid progress to make it more accessible for scientists and patients. This will enhance our understanding about diseases and develop a better cure for patients of various diseases.”
The research was funded by the National Research Foundation of the Korean government’s Ministry of Science and ICT.
-PublicationYoungmok Jung, Dongsu Han, “BWA-MEME:BWA-MEM emulated with a machine learning approach,” Bioinformatics, Volume 38, Issue 9, May 2022
(https://doi.org/10.1093/bioinformatics/btac137)
-ProfileProfessor Dongsu HanSchool of Electrical EngineeringKAIST
2022.05.10 View 9840