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Ultrathin but Fully Packaged High-Resolution Camera
- Biologically inspired ultrathin arrayed camera captures super-resolution images. - The unique structures of biological vision systems in nature inspired scientists to design ultracompact imaging systems. A research group led by Professor Ki-Hun Jeong have made an ultracompact camera that captures high-contrast and high-resolution images. Fully packaged with micro-optical elements such as inverted micro-lenses, multilayered pinhole arrays, and gap spacers on the image sensor, the camera boasts a total track length of 740 μm and a field of view of 73°. Inspired by the eye structures of the paper wasp species Xenos peckii, the research team completely suppressed optical noise between micro-lenses while reducing camera thickness. The camera has successfully demonstrated high-contrast clear array images acquired from tiny micro lenses. To further enhance the image quality of the captured image, the team combined the arrayed images into one image through super-resolution imaging. An insect’s compound eye has superior visual characteristics, such as a wide viewing angle, high motion sensitivity, and a large depth of field while maintaining a small volume of visual structure with a small focal length. Among them, the eyes of Xenos peckii and an endoparasite found on paper wasps have hundreds of photoreceptors in a single lens unlike conventional compound eyes. In particular, the eye structures of an adult Xenos peckii exhibit hundreds of photoreceptors on an individual eyelet and offer engineering inspiration for ultrathin cameras or imaging applications because they have higher visual acuity than other compound eyes. For instance, Xenos peckii’s eye-inspired cameras provide a 50 times higher spatial resolution than those based on arthropod eyes. In addition, the effective image resolution of the Xenos peckii’s eye can be further improved using the image overlaps between neighboring eyelets. This unique structure offers higher visual resolution than other insect eyes. The team achieved high-contrast and super-resolution imaging through a novel arrayed design of micro-optical elements comprising multilayered aperture arrays and inverted micro-lens arrays directly stacked over an image sensor. This optical component was integrated with a complementary metal oxide semiconductor image sensor. This is first demonstration of super-resolution imaging which acquires a single integrated image with high contrast and high resolving power reconstructed from high-contrast array images. It is expected that this ultrathin arrayed camera can be applied for further developing mobile devices, advanced surveillance vehicles, and endoscopes. Professor Jeong said, “This research has led to technological advances in imaging technology. We will continue to strive to make significant impacts on multidisciplinary research projects in the fields of microtechnology and nanotechnology, seeking inspiration from natural photonic structures.” This work was featured in Light Science & Applications last month and was supported by the National Research Foundation (NRF) of and the Ministry of Health and Welfare (MOHW) of Korea. Image credit: Professor Ki-Hun Jeong, KAIST Image usage restrictions: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only. Publication: Kisoo Kim, Kyung-Won Jang, Jae-Kwan Ryu, and Ki-Hun Jeong. (2020) “Biologically inspired ultrathin arrayed camera for high-contrast and high-resolution imaging”. Light Science & Applications. Volume 9. Article 28. Available online at https://doi.org/10.1038/s41377-020-0261-8 Profile: Ki-Hun Jeong Professor email@example.com http://biophotonics.kaist.ac.kr/ Department of Bio and Brain Engineering KAIST Profile: Kisoo Kim Ph.D. Candidate firstname.lastname@example.org http://biophotonics.kaist.ac.kr/ Department of Bio and Brain Engineering KAIST (END)
Wearable Strain Sensor Using Light Transmittance Helps Measure Physical Signals Better
KAIST researchers have developed a novel wearable strain sensor based on the modulation of optical transmittance of a carbon nanotube (CNT)-embedded elastomer. The sensor is capable of sensitive, stable, and continuous measurement of physical signals. This technology, featured in the March 4th issue of ACS Applied Materials & Interfaces as a front cover article, shows great potential for the detection of subtle human motions and the real-time monitoring of body postures for healthcare applications. A wearable strain sensor must have high sensitivity, flexibility, and stretchability, as well as low cost. Those used especially for health monitoring should also be tied to long-term solid performance, and be environmentally stable. Various stretchable strain sensors based on piezo-resistive and capacitive principles have been developed to meet all these requirements. Conventional piezo-resistive strain sensors using functional nanomaterials, including CNTs as the most common example, have shown high sensitivity and great sensing performance. However, they suffer from poor long-term stability and linearity, as well as considerable signal hysteresis. As an alternative, piezo-capacitive strain sensors with better stability, lower hysteresis, and higher stretchability have been suggested. But due to the fact that piezo-capacitive strain sensors exhibit limited sensitivity and strong electromagnetic interference caused by the conductive objects in the surrounding environment, these conventional stretchable strain sensors are still facing limitations that are yet to be resolved. A KAIST research team led by Professor Inkyu Park from the Department of Mechanical Engineering suggested that an optical-type stretchable strain sensor can be a good alternative to resolve the limitations of conventional piezo-resistive and piezo-capacitive strain sensors, because they have high stability and are less affected by environmental disturbances. The team then introduced an optical wearable strain sensor based on the light transmittance changes of a CNT-embedded elastomer, which further addresses the low sensitivity problem of conventional optical stretchable strain sensors. In order to achieve a large dynamic range for the sensor, Professor Park and his researchers chose Ecoflex as an elastomeric substrate with good mechanical durability, flexibility, and attachability on human skin, and the new optical wearable strain sensor developed by the research group actually shows a wide dynamic range of 0 to 400%. In addition, the researchers propagated the microcracks under tensile strain within the film of multi-walled CNTs embedded in the Ecoflex substrate, changing the optical transmittance of the film. By doing so, it was possible for them to develop a wearable strain sensor having a sensitivity 10 times higher than conventional optical stretchable strain sensors. The proposed sensor has also passed the durability test with excellent results. The sensor’s response after 13,000 sets of cyclic loading was stable without any noticeable drift. This suggests that the sensor response can be used without degradation, even if the sensor is repeatedly used for a long time and in various environmental conditions. Using the developed sensor, the research team could measure the finger bending motion and used it for robot control. They also developed a three-axes sensor array for body posture monitoring. The sensor was able to monitor human motions with small strains such as a pulse near the carotid artery and muscle movement around the mouth during pronunciation. Professor Park said, “In this study, our group developed a new wearable strain sensor platform that overcomes many limitations of previously developed resistive, capacitive, and optical-type stretchable strain sensors. Our sensor could be widely used in a variety of fields including soft robotics, wearable electronics, electronic skin, healthcare, and even entertainment.” This work was supported by the National Research Foundation (NRF) of Korea. Publication: Jimin Gu, Donguk Kwon, Junseong Ahn, and Inkyu Park. (2020) “Wearable Strain sensors Using Light Transmittance Change of Carbon Nanotube-Embedded Elastomers with Microcracks” ACS Applied Materials & Interfaces. Volume 12. Issue 9. Available online at https://doi.org/10.1021/acsami.9b18069 Profile: Inkyu Park Professor email@example.com http://mintlab1.kaist.ac.kr Micro/Nano Transducers Laboratory (MINT Lab) Department of Mechanical Engineering (ME)Korea Advanced Institute of Science and Technology (KAIST) Profile: Jimin Gu Ph.D. Candidate firstname.lastname@example.org http://mintlab1.kaist.ac.kr MINT Lab KAIST ME (END)
3D Hierarchically Porous Nanostructured Catalyst Helps Efficiently Reduce CO2
- This new catalyst will bring CO2 one step closer to serving as a sustainable energy source. - KAIST researchers developed a three-dimensional (3D) hierarchically porous nanostructured catalyst with carbon dioxide (CO2) to carbon monoxide (CO) conversion rate up to 3.96 times higher than that of conventional nanoporous gold catalysts. This new catalyst helps overcome the existing limitations of the mass transport that has been a major cause of decreases in the CO2 conversion rate, holding a strong promise for the large-scale and cost-effective electrochemical conversion of CO2 into useful chemicals. As CO2 emissions increase and fossil fuels deplete globally, reducing and converting CO2 to clean energy electrochemically has attracted a great deal of attention as a promising technology. Especially due to the fact that the CO2 reduction reaction occurs competitively with hydrogen evolution reactions (HER) at similar redox potentials, the development of an efficient electrocatalyst for selective and robust CO2 reduction reactions has remained a key technological issue. Gold (Au) is one of the most commonly used catalysts in CO2 reduction reactions, but the high cost and scarcity of Au pose obstacles for mass commercial applications. The development of nanostructures has been extensively studied as a potential approach to improving the selectivity for target products and maximizing the number of active stable sites, thus enhancing the energy efficiency. However, the nanopores of the previously reported complex nanostructures were easily blocked by gaseous CO bubbles during aqueous reactions. The CO bubbles hindered mass transport of the reactants through the electrolyte, resulting in low CO2 conversion rates. In the study published in the Proceedings of the National Academy of Sciences of the USA (PNAS) on March 4, a research group at KAIST led by Professor Seokwoo Jeon and Professor Jihun Oh from the Department of Materials Science and Engineering designed a 3D hierarchically porous Au nanostructure with two different sizes of macropores and nanopores. The team used proximity-field nanopatterning (PnP) and electroplating techniques that are effective for fabricating the 3D well-ordered nanostructures. The proposed nanostructure, comprised of interconnected macroporous channels 200 to 300 nanometers (nm) wide and 10 nm nanopores, induces efficient mass transport through the interconnected macroporous channels as well as high selectivity by producing highly active stable sites from numerous nanopores. As a result, its electrodes show a high CO selectivity of 85.8% at a low overpotential of 0.264 V and efficient mass activity that is up to 3.96 times higher than that of de-alloyed nanoporous Au electrodes. “These results are expected to solve the problem of mass transfer in the field of similar electrochemical reactions and can be applied to a wide range of green energy applications for the efficient utilization of electrocatalysts,” said the researchers. This work was supported by the National Research Foundation (NRF) of Korea. Image credit: Professor Seokwoo Jeon and Professor Jihun Oh, KAIST Image usage restrictions: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only. Publication: Hyun et al. (2020) Hierarchically porous Au nanostructures with interconnected channels for efficient mass transport in electrocatalytic CO2 reduction. Proceedings of the National Academy of Sciences of the USA (PNAS). Available online at https://doi.org/10.1073/pnas.1918837117 Profile: Seokwoo Jeon, PhD Professor email@example.com http://fdml.kaist.ac.kr Department of Materials Science and Engineering (MSE) https://www.kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST)Daejeon, Republic of Korea Profile: Jihun Oh, PhD Associate Professor firstname.lastname@example.org http://les.kaist.ac.kr Department of Materials Science and Engineering (MSE) Department of Energy, Environment, Water and Sustainability (EEWS) KAIST Profile: Gayea Hyun PhD Candidate email@example.com http://fdml.kaist.ac.kr Flexible Devices and Metamaterials Laboratory (FDML) Department of Materials Science and Engineering (MSE) KAIST Profile: Jun Tae Song, PhD Assistant Professor firstname.lastname@example.org http://www.cstf.kyushu-u.ac.jp/~ishihara-lab/ Department of Applied Chemistry https://www.kyushu-u.ac.jp Kyushu UniversityFukuoka, Japan (END)
Professor Minsoo Rhu Recognized as Facebook Research Scholar
Professor Minsoo Rhu from the School of Electrical Engineering was selected as the recipient of the Systems for Machine Learning Research Awards presented by Facebook. Facebook launched the award last year with the goal of funding impactful solutions in the areas of developer tookits, compilers and code generation, system architecture, memory technologies, and machine learning accelerator support. A total of 167 scholars from 100 universities representing 26 countries submitted research proposals, and Facebook selected final 10 scholars. Professor Rhu made the list with his research topic ‘A Near-Memory Processing Architecture for Training Recommendation Systems.’ He will receive 5,000 USD in research funds at the award ceremony which will take place during this year’s AI Systems Faculty Summit at the Facebook headquarters in Menlo Park, California. Professor Rhu’s submission was based on research on ‘Memory-Centric Deep Learning System Architecture’ that he carried out for three years under the auspices of Samsung Science and Technology Foundation from 2017. It was an academic-industrial cooperation research project in which leading domestic companies like Samsung Electronics and SK Hynix collaborated to make a foray into the global memory-centric smart system semiconductor market. Professor Rhu who joined KAIST in 2018 has led various systems research projects to accelerate the AI computing technology while working at NVIDIA headquarters from 2014. (END)
What Fuels a “Domino Effect” in Cancer Drug Resistance?
KAIST researchers have identified mechanisms that relay prior acquired resistance to the first-line chemotherapy to the second-line targeted therapy, fueling a “domino effect” in cancer drug resistance. Their study featured in the February 7 edition of Science Advances suggests a new strategy for improving the second-line setting of cancer treatment for patients who showed resistance to anti-cancer drugs. Resistance to cancer drugs is often managed in the clinic by chemotherapy and targeted therapy. Unlike chemotherapy that works by repressing fast-proliferating cells, targeted therapy blocks a single oncogenic pathway to halt tumor growth. In many cases, targeted therapy is engaged as a maintenance therapy or employed in the second-line after front-line chemotherapy. A team of researchers led by Professor Yoosik Kim from the Department of Chemical and Biomolecular Engineering and the KAIST Institute for Health Science and Technology (KIHST) has discovered an unexpected resistance signature that occurs between chemotherapy and targeted therapy. The team further identified a set of integrated mechanisms that promotes this kind of sequential therapy resistance. “There have been multiple clinical accounts reflecting that targeted therapies tend to be least successful in patients who have exhausted all standard treatments,” said the first author of the paper Mark Borris D. Aldonza. He continued, “These accounts ignited our hypothesis that failed responses to some chemotherapies might speed up the evolution of resistance to other drugs, particularly those with specific targets.” Aldonza and his colleagues extracted large amounts of drug-resistance information from the open-source database the Genomics of Drug Sensitivity in Cancer (GDSC), which contains thousands of drug response data entries from various human cancer cell lines. Their big data analysis revealed that cancer cell lines resistant to chemotherapies classified as anti-mitotic drugs (AMDs), toxins that inhibit overacting cell division, are also resistant to a class of targeted therapies called epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). In all of the cancer types analyzed, more than 84 percent of those resistant to AMDs, representatively ‘paclitaxel’, were also resistant to at least nine EGFR-TKIs. In lung, pancreatic, and breast cancers where paclitaxel is often used as a first-line, standard-of-care regimen, greater than 92 percent showed resistance to EGFR-TKIs. Professor Kim said, “It is surprising to see that such collateral resistance can occur specifically between two chemically different classes of drugs.” To figure out how failed responses to paclitaxel leads to resistance to EGFR-TKIs, the team validated co-resistance signatures that they found in the database by generating and analyzing a subset of slow-doubling, paclitaxel-resistant cancer models called ‘persisters’. The results demonstrated that paclitaxel-resistant cancers remodel their stress response by first becoming more stem cell-like, evolving the ability to self-renew to adapt to more stressful conditions like drug exposures. More surprisingly, when the researchers characterized the metabolic state of the cells, EGFR-TKI persisters derived from paclitaxel-resistant cancer cells showed high dependencies to energy-producing processes such as glycolysis and glutaminolysis. “We found that, without an energy stimulus like glucose, these cells transform to becoming more senescent, a characteristic of cells that have arrested cell division. However, this senescence is controlled by stem cell factors, which the paclitaxel-resistant cancers use to escape from this arrested state given a favorable condition to re-grow,” said Aldonza. Professor Kim explained, “Before this research, there was no reason to expect that acquiring the cancer stem cell phenotype that dramatically leads to a cascade of changes in cellular states affecting metabolism and cell death is linked with drug-specific sequential resistance between two classes of therapies.” He added, “The expansion of our work to other working models of drug resistance in a much more clinically-relevant setting, perhaps in clinical trials, will take on increasing importance, as sequential treatment strategies will continue to be adapted to various forms of anti-cancer therapy regimens.” This study was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF-2016R1C1B2009886), and the KAIST Future Systems Healthcare Project (KAISTHEALTHCARE42) funded by the Korean Ministry of Science and ICT (MSIT). Undergraduate student Aldonza participated in this research project and presented the findings as the lead author as part of the Undergraduate Research Participation (URP) Program at KAIST. < Figure 1. Schematic overview of the study. > < Figure 2. Big data analysis revealing co-resistance signatures between classes of anti-cancer drugs. > Publication: Aldonza et al. (2020) Prior acquired resistance to paclitaxel relays diverse EGFR-targeted therapy persistence mechanisms. Science Advances, Vol. 6, No. 6, eaav7416. Available online at http://dx.doi.org/10.1126/sciadv.aav7416 Profile: Prof. Yoosik Kim, MA, PhD email@example.com https://qcbio.kaist.ac.kr/ Assistant Professor Bio Network Analysis Laboratory Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon, Republic of Korea Profile: Mark Borris D. Aldonza firstname.lastname@example.org Undergraduate Student Department of Biological Sciences Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon, Republic of Korea (END)
Cancer cell reversion may offer a new approach to colorectal cancer treatment
A novel approach to reverse the progression of healthy cells to malignant ones may offer a more effective way to eradicate colorectal cancer cells with far fewer side effects, according to a team of researchers based in South Korea. Colorectal cancer, or cancer of the colon, is the third most common cancer in men and the second most common in women worldwide. South Korea has the second highest incident rate of colorectal cancer in the world, topped only by Hungary, according to the World Cancer Research Fund. Their results were published as a featured cover article on January 2 in Molecular Cancer Research, a journal of the American Association for Cancer Research. Led by Kwang-Hyun Cho, a professor and associate vice president of research at KAIST , the researchers used a computational framework to analyze healthy colon cells and colorectal cancer cells. They found that some master regulator proteins involved in cellular replication helped healthy colon cells mature, or differentiate into their specific cell type, and remain healthy. One particular protein, called SETDB1, suppressed the helpful proteins, forcing new cells to remain in a state of immaturity with the potential to become cancerous. “This suggests that differentiated cells have an inherent resistance mechanism against malignant transformation and indicates that cellular reprogramming is indispensable for malignancy,” said Cho. “We speculated that malignant properties might be eradicated if the tissue-specific gene expression is reinstated — if we repress SETDB1 and allow the colon cells to mature and differentiate as they would normally.” Image credit: Kwang-Hyun Cho, KAIST Image restriction: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only. Using human-derived cells, Cho and his team targeted the tissue-specific gene expression programs identified in their computational analysis. These are the blueprints for the proteins that eventually help immature cells differentiate into tissue-specific cell types, such as colon cells. When a person has a genetic mutation, or has exposure to certain environmental factors, this process can go awry, leading to an overexpression of unhelpful proteins, such as SEDTB1. The researchers specifically reduced the amount of SEDTB1 in these tissue-specific gene expression programs, which allowed the cells to mature and fully differentiate into colon cells. “Our experiment also shows that SETDB1 depletion combined with cytotoxic drugs might be potentially beneficial to anticancer treatment,” Cho said. Cytotoxic drugs are often used for cancer treatment because the type of medicine contains chemicals that are toxic to cancer cells which can prevent them from replicating or growing. He noted that this combination could be more effective in treating cancer by transforming the cancer cell state into a less malignant or resistant state. He eventually pursues a cancer reversion therapy alone instead of conventional cytotoxic drug therapy since the cancer reversion therapy can provide a much less painful experience for patients with cancer who often have severe side effects from treatments intended to kill off cancerous cells, such as chemotherapy. The researchers plan to continue studying how to return cancer cells to healthier states, with the ultimate goal of translating their work to therapeutic treatment for patients with colorectal cancer. “I think our study of cancer reversion would eventually change the current medical practice of treating cancer toward the direction of keeping the patient’s quality of life while minimizing the side effects of current anti-cancer therapies,” Cho said. ### This work was funded by KAIST and the National Research Foundation of Korea grants funded by the Korean government, the Ministry of Science and Information and Communication Technology. Other authors include Soobeom Lee, Chae Young Hwang and Dongsan Kim, all of whom are affiliated with the Laboratory for Systems Biology and Bio-Inspired Engineering in the Department of Bio and Brain Engineering at KAIST; Chansu Lee and Sung Noh Hong, both with the Department of Medicine, and Seok-Hyung Kim of the Department of Pathology in the Samsung Medical Center at the Sungkyunkwan University School of Medicine. -Profile Professor Kwang-Hyun Cho email@example.com http://sbie.kaist.ac.kr/ Department of Bio and Brain Engineering KAIST https://www.kaist.ac.kr
A System Controlling Road Active Noise to Hit the Road
The research team led by Professor Youngjin Park of the Department of Mechanical Engineering has developed a road noise active noise control (RANC) system to be commercialized in partnership with Hyundai Motor Group. On December 11, Hyundai Motor Group announced the successful development of the RANC system, which significantly reduces the road noise flowing into cars. The carmaker has completed the domestic and American patent applications for the location of sensors and the signal selection method, the core technology of RANC. RANC is a technology for reducing road noise during driving. This system consists of an acceleration sensor, digital signal processor (the control computer to analyze sound signals), microphone, amplifier, and audio system. To make the system as simple as possible, the audio system utilizes the original audio system embedded in the car instead of a separate system. The acceleration sensor first calculates the vibration from the road into the car. The location of the sensor is important for accurately identifying the vibration path. The research team was able to find the optimal sensor location through a number of tests. The System Dynamics and Applied Control Laboratory of Professor Park researched ways to significantly reduce road noise with Hyundai Motor Group for four years from 1993 as a G7 national project and published the results in international journals. In 2002, the researchers published an article titled “Noise Quietens Driving” in Nature, where they announced the first success in reducing road noise in actual cars. The achievement did not lead to commercialization, however, due to the lack of auxiliary technologies at the time, digital amplifiers and DSP for cars for example, and pricing issues. Since 2013, Professor Park’s research team has participated in one technology transfer and eight university-industry projects. Based on these efforts, the team was able to successfully develop the RANC system with domestic technology in partnership with Hyundai’s NVH Research Lab (Research Fellow, Dr. Gangdeok Lee; Ph.D. in aviation engineering, 1996), Optomech (Founder, Professor Gyeongsu Kim; Ph.D. in mechanical engineering, 1999), ARE (CEO Hyeonseok Kim; Ph.D. in mechanical engineering, 1998), WeAcom, and BurnYoung. Professor Park’s team led the project by performing theory-based research during the commercialization stage in collaboration with Hyundai Motor Group. For the commercialization of the RANC system, Hyundai Motor Group is planning to collaborate with the global car audio company Harman to increase the degree of completion and apply the RANC system to the GV 80, the first SUV model of the Genesis brand. “I am very delighted as an engineer to see the research I worked on from my early days at KAIST be commercialized after 20 years,” noted Professor Park. “I am thrilled to make a contribution to such commercialization with my students in my lab.”
New Liquid Metal Wearable Pressure Sensor Created for Health Monitoring Applications
Soft pressure sensors have received significant research attention in a variety of fields, including soft robotics, electronic skin, and wearable electronics. Wearable soft pressure sensors have great potential for the real-time health monitoring and for the early diagnosis of diseases. A KAIST research team led by Professor Inkyu Park from the Department of Mechanical Engineering developed a highly sensitive wearable pressure sensor for health monitoring applications. This work was reported in Advanced Healthcare Materials on November 21 as a front cover article. This technology is capable of sensitive, precise, and continuous measurement of physiological and physical signals and shows great potential for health monitoring applications and the early diagnosis of diseases. A soft pressure sensor is required to have high compliance, high sensitivity, low cost, long-term performance stability, and environmental stability in order to be employed for continuous health monitoring. Conventional solid-state soft pressure sensors using functional materials including carbon nanotubes and graphene have showed great sensing performance. However, these sensors suffer from limited stretchability, signal drifting, and long-term instability due to the distance between the stretchable substrate and the functional materials. To overcome these issues, liquid-state electronics using liquid metal have been introduced for various wearable applications. Of these materials, Galinstan, a eutectic metal alloy of gallium, indium, and tin, has great mechanical and electrical properties that can be employed in wearable applications. But today’s liquid metal-based pressure sensors have low-pressure sensitivity, limiting their applicability for health monitoring devices. The research team developed a 3D-printed rigid microbump array-integrated, liquid metal-based soft pressure sensor. With the help of 3D printing, the integration of a rigid microbump array and the master mold for a liquid metal microchannel could be achieved simultaneously, reducing the complexity of the manufacturing process. Through the integration of the rigid microbump and the microchannel, the new pressure sensor has an extremely low detection limit and enhanced pressure sensitivity compared to previously reported liquid metal-based pressure sensors. The proposed sensor also has a negligible signal drift over 10,000 cycles of pressure, bending, and stretching and exhibited excellent stability when subjected to various environmental conditions. These performance outcomes make it an excellent sensor for various health monitoring devices. First, the research team demonstrated a wearable wristband device that can continuously monitor one’s pulse during exercise and be employed in a noninvasive cuffless BP monitoring system based on PTT calculations. Then, they introduced a wireless wearable heel pressure monitoring system that integrates three 3D-BLiPS with a wireless communication module. Professor Park said, “It was possible to measure health indicators including pulse and blood pressure continuously as well as pressure of body parts using our proposed soft pressure sensor. We expect it to be used in health care applications, such as the prevention and the monitoring of the pressure-driven diseases such as pressure ulcers in the near future. There will be more opportunities for future research including a whole-body pressure monitoring system related to other physical parameters.” This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT. < Figure 1. The front cover image of Advanced Healthcare Materials, Volume 8, Issue 22. > < Figure 2. Highly sensitive liquid metal-based soft pressure sensor integrated with 3D-printed microbump array. > < Figure 3. High pressure sensitivity and reliable sensing performances of the proposed sensor and wireless heel pressure monitoring application. > Profile: Prof. Inkyu Park firstname.lastname@example.org Micro/Nano Transducers Laboratory http://mintlab1.kaist.ac.kr/ Department of Mechanical Engineering KAIST
KAIST and Google Jointly Develop AI Curricula
KAIST selected the two professors who will develop AI curriculum under the auspices of the KAIST-Google Partnership for AI Education and Research. The Graduate School of AI announced the two authors among the 20 applicants who will develop the curriculum next year. They will be provided 7,500 USD per subject. Professor Changho Suh from the School of Electrical Engineering and Professor Yong-Jin Yoon from the Department of Mechanical Engineering will use Google technology such as TensorFlow, Google Cloud, and Android to create the curriculum. Professor Suh’s “TensorFlow for Information Theory and Convex Optimization “will be used for curriculum in the graduate courses and Professor Yoon’s “AI Convergence Project Based Learning (PBL)” will be used for online courses. Professor Yoon’s course will explore and define problems by utilizing AI and experiencing the process of developing products that use AI through design thinking, which involves product design, production, and verification. Professor Suh’s course will discus“information theory and convergence,” which uses basic sciences and engineering as well as AI, machine learning, and deep learning.
AI to Determine When to Intervene with Your Driving
(Professor Uichin Lee (left) and PhD candidate Auk Kim) Can your AI agent judge when to talk to you while you are driving? According to a KAIST research team, their in-vehicle conservation service technology will judge when it is appropriate to contact you to ensure your safety. Professor Uichin Lee from the Department of Industrial and Systems Engineering at KAIST and his research team have developed AI technology that automatically detects safe moments for AI agents to provide conversation services to drivers. Their research focuses on solving the potential problems of distraction created by in-vehicle conversation services. If an AI agent talks to a driver at an inopportune moment, such as while making a turn, a car accident will be more likely to occur. In-vehicle conversation services need to be convenient as well as safe. However, the cognitive burden of multitasking negatively influences the quality of the service. Users tend to be more distracted during certain traffic conditions. To address this long-standing challenge of the in-vehicle conversation services, the team introduced a composite cognitive model that considers both safe driving and auditory-verbal service performance and used a machine-learning model for all collected data. The combination of these individual measures is able to determine the appropriate moments for conversation and most appropriate types of conversational services. For instance, in the case of delivering simple-context information, such as a weather forecast, driver safety alone would be the most appropriate consideration. Meanwhile, when delivering information that requires a driver response, such as a “Yes” or “No,” the combination of driver safety and auditory-verbal performance should be considered. The research team developed a prototype of an in-vehicle conversation service based on a navigation app that can be used in real driving environments. The app was also connected to the vehicle to collect in-vehicle OBD-II/CAN data, such as the steering wheel angle and brake pedal position, and mobility and environmental data such as the distance between successive cars and traffic flow. Using pseudo-conversation services, the research team collected a real-world driving dataset consisting of 1,388 interactions and sensor data from 29 drivers who interacted with AI conversational agents. Machine learning analysis based on the dataset demonstrated that the opportune moments for driver interruption could be correctly inferred with 87% accuracy. The safety enhancement technology developed by the team is expected to minimize driver distractions caused by in-vehicle conversation services. This technology can be directly applied to current in-vehicle systems that provide conversation services. It can also be extended and applied to the real-time detection of driver distraction problems caused by the use of a smartphone while driving. Professor Lee said, “In the near future, cars will proactively deliver various in-vehicle conversation services. This technology will certainly help vehicles interact with their drivers safely as it can fairly accurately determine when to provide conversation services using only basic sensor data generated by cars.” The researchers presented their findings at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp’19) in London, UK. This research was supported in part by Hyundai NGV and by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT. (Figure: Visual description of safe enhancement technology for in-vehicle conversation services)
Ultrafast Quantum Motion in a Nanoscale Trap Detected
< Professor Heung-Sun Sim (left) and Co-author Dr. Sungguen Ryu (right) > KAIST researchers have reported the detection of a picosecond electron motion in a silicon transistor. This study has presented a new protocol for measuring ultrafast electronic dynamics in an effective time-resolved fashion of picosecond resolution. The detection was made in collaboration with Nippon Telegraph and Telephone Corp. (NTT) in Japan and National Physical Laboratory (NPL) in the UK and is the first report to the best of our knowledge. When an electron is captured in a nanoscale trap in solids, its quantum mechanical wave function can exhibit spatial oscillation at sub-terahertz frequencies. Time-resolved detection of such picosecond dynamics of quantum waves is important, as the detection provides a way of understanding the quantum behavior of electrons in nano-electronics. It also applies to quantum information technologies such as the ultrafast quantum-bit operation of quantum computing and high-sensitivity electromagnetic-field sensing. However, detecting picosecond dynamics has been a challenge since the sub-terahertz scale is far beyond the latest bandwidth measurement tools. A KAIST team led by Professor Heung-Sun Sim developed a theory of ultrafast electron dynamics in a nanoscale trap, and proposed a scheme for detecting the dynamics, which utilizes a quantum-mechanical resonant state formed beside the trap. The coupling between the electron dynamics and the resonant state is switched on and off at a picosecond so that information on the dynamics is read out on the electric current being generated when the coupling is switched on. NTT realized, together with NPL, the detection scheme and applied it to electron motions in a nanoscale trap formed in a silicon transistor. A single electron was captured in the trap by controlling electrostatic gates, and a resonant state was formed in the potential barrier of the trap. The switching on and off of the coupling between the electron and the resonant state was achieved by aligning the resonance energy with the energy of the electron within a picosecond. An electric current from the trap through the resonant state to an electrode was measured at only a few Kelvin degrees, unveiling the spatial quantum-coherent oscillation of the electron with 250 GHz frequency inside the trap. Professor Sim said, “This work suggests a scheme of detecting picosecond electron motions in submicron scales by utilizing quantum resonance. It will be useful in dynamical control of quantum mechanical electron waves for various purposes in nano-electronics, quantum sensing, and quantum information”. This work was published online at Nature Nanotechnology on November 4. It was partly supported by the Korea National Research Foundation through the SRC Center for Quantum Coherence in Condensed Matter. For more on the NTT news release this article, please visit https://www.ntt.co.jp/news2019/1911e/191105a.html Profile: Prof. Heung-Sun Sim Professor, Department of Physics Director, SRC Center for Quantum Coherence in Condensed Matter Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea email@example.com https://qet.kaist.ac.kr Publication: Gento Yamahata, Sungguen Ryu, Nathan Johnson, H.-S. Sim, Akira Fujiwara, and Masaya Kataoka. 2019. Picosecond coherent electron motion in a silicon single-electron source. Nature Nanotechnology (Online Publication). 6 pages. https://doi.org/10.1038/s41565-019-0563-2 Link to download the full-text paper: https://www.nature.com/articles/s41565-019-0563-2.pdf
Image Analysis to Automatically Quantify Gender Bias in Movies
Many commercial films worldwide continue to express womanhood in a stereotypical manner, a recent study using image analysis showed. A KAIST research team developed a novel image analysis method for automatically quantifying the degree of gender bias in films. The ‘Bechdel Test’ has been the most representative and general method of evaluating gender bias in films. This test indicates the degree of gender bias in a film by measuring how active the presence of women is in a film. A film passes the Bechdel Test if the film (1) has at least two female characters, (2) who talk to each other, and (3) their conversation is not related to the male characters. However, the Bechdel Test has fundamental limitations regarding the accuracy and practicality of the evaluation. Firstly, the Bechdel Test requires considerable human resources, as it is performed subjectively by a person. More importantly, the Bechdel Test analyzes only a single aspect of the film, the dialogues between characters in the script, and provides only a dichotomous result of passing the test, neglecting the fact that a film is a visual art form reflecting multi-layered and complicated gender bias phenomena. It is also difficult to fully represent today’s various discourse on gender bias, which is much more diverse than in 1985 when the Bechdel Test was first presented. Inspired by these limitations, a KAIST research team led by Professor Byungjoo Lee from the Graduate School of Culture Technology proposed an advanced system that uses computer vision technology to automatically analyzes the visual information of each frame of the film. This allows the system to more accurately and practically evaluate the degree to which female and male characters are discriminatingly depicted in a film in quantitative terms, and further enables the revealing of gender bias that conventional analysis methods could not yet detect. Professor Lee and his researchers Ji Yoon Jang and Sangyoon Lee analyzed 40 films from Hollywood and South Korea released between 2017 and 2018. They downsampled the films from 24 to 3 frames per second, and used Microsoft’s Face API facial recognition technology and object detection technology YOLO9000 to verify the details of the characters and their surrounding objects in the scenes. Using the new system, the team computed eight quantitative indices that describe the representation of a particular gender in the films. They are: emotional diversity, spatial staticity, spatial occupancy, temporal occupancy, mean age, intellectual image, emphasis on appearance, and type and frequency of surrounding objects. Figure 1. System Diagram Figure 2. 40 Hollywood and Korean Films Analyzed in the Study According to the emotional diversity index, the depicted women were found to be more prone to expressing passive emotions, such as sadness, fear, and surprise. In contrast, male characters in the same films were more likely to demonstrate active emotions, such as anger and hatred. Figure 3. Difference in Emotional Diversity between Female and Male Characters The type and frequency of surrounding objects index revealed that female characters and automobiles were tracked together only 55.7 % as much as that of male characters, while they were more likely to appear with furniture and in a household, with 123.9% probability. In cases of temporal occupancy and mean age, female characters appeared less frequently in films than males at the rate of 56%, and were on average younger in 79.1% of the cases. These two indices were especially conspicuous in Korean films. Professor Lee said, “Our research confirmed that many commercial films depict women from a stereotypical perspective. I hope this result promotes public awareness of the importance of taking prudence when filmmakers create characters in films.” This study was supported by KAIST College of Liberal Arts and Convergence Science as part of the Venture Research Program for Master’s and PhD Students, and will be presented at the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) on November 11 to be held in Austin, Texas. Publication: Ji Yoon Jang, Sangyoon Lee, and Byungjoo Lee. 2019. Quantification of Gender Representation Bias in Commercial Films based on Image Analysis. In Proceedings of the 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW). ACM, New York, NY, USA, Article 198, 29 pages. https://doi.org/10.1145/3359300 Link to download the full-text paper: https://files.cargocollective.com/611692/cscw198-jangA--1-.pdf Profile: Prof. Byungjoo Lee, MD, PhD firstname.lastname@example.org http://kiml.org/ Assistant Professor Graduate School of Culture Technology (CT) Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr Daejeon 34141, Korea Profile: Ji Yoon Jang, M.S. email@example.com Interactive Media Lab Graduate School of Culture Technology (CT) Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr Daejeon 34141, Korea Profile: Sangyoon Lee, M.S. Candidate firstname.lastname@example.org Interactive Media Lab Graduate School of Culture Technology (CT) Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr Daejeon 34141, Korea (END)
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