A New Strategy for Active Metasurface Design Provi..
The new strategy displays an unprecedented upper limit of dynamic phase modulation with no significant variations in optical amplitude An international team of researchers led by Professor Min Seok Jang of KAIST and Professor Victor W. Brar of the University of Wisconsin-Madison has demonstrated a widely applicable methodology enabling a full 360° active phase modulation for metasurfaces while maintaining significant levels of uniform light amplitude. This strategy can be fundamentally applied to any spectral region with any structures and resonances that fit the bill. Metasurfaces are optical components with specialized functionalities indispensable for real-life applications ranging from LIDAR and spectroscopy to futuristic technologies such as invisibility cloaks and holograms. They are known for their compact and micro/nano-sized nature, which enables them to be integrated into electronic computerized systems with sizes that are ever decreasing as predicted by Moore’s law. < Figure1.The metasurface designed by the team that demonstrates complete 2π tunable phase modulation utilizing the avoided crossing of two resonances. > < Figure2. a: Complex reflection coefficient trajectories with different mobility values for the graphene sheet case. Full 2π phase modulation does not occur without the avoided crossing with graphene plasmons, despite the increasing mobilities and therefore the decreasing linewidths. b: Complex reflection coefficient trajectories with different mobility values for the graphene ribbon case.) > In order to allow for such innovations, metasurfaces must be capable of manipulating the impinging light, doing so by manipulating either the light’s amplitude or phase (or both) and emitting it back out. However, dynamically modulating the phase with the full circle range has been a notoriously difficult task, with very few works managing to do so by sacrificing a substantial amount of amplitude control. Challenged by these limitations, the team proposed a general methodology that enables metasurfaces to implement a dynamic phase modulation with the complete 360° phase range, all the while uniformly maintaining significant levels of amplitude. The underlying reason for the difficulty achieving such a feat is that there is a fundamental trade-off regarding dynamically controlling the optical phase of light. Metasurfaces generally perform such a function through optical resonances, an excitation of electrons inside the metasurface structure that harmonically oscillate together with the incident light. In order to be able to modulate through the entire range of 0-360°, the optical resonance frequency (the center of the spectrum) must be tuned by a large amount while the linewidth (the width of the spectrum) is kept to a minimum. However, to electrically tune the optical resonance frequency of the metasurface on demand, there needs to be a controllable influx and outflux of electrons into the metasurface and this inevitably leads to a larger linewidth of the aforementioned optical resonance. The problem is further compounded by the fact that the phase and the amplitude of optical resonances are closely correlated in a complex, non-linear fashion, making it very difficult to hold substantial control over the amplitude while changing the phase. The team’s work circumvented both problems by using two optical resonances, each with specifically designated properties. One resonance provides the decoupling between the phase and amplitude so that the phase is able to be tuned while significant and uniform levels of amplitude are maintained, as well as providing a narrow linewidth. The other resonance provides the capability of being sufficiently tuned to a large degree so that the complete full circle range of phase modulation is achievable. The quintessence of the work is then to combine the different properties of the two resonances through a phenomenon called avoided crossing, so that the interactions between the two resonances lead to an amalgamation of the desired traits that achieves and even surpasses the full 360° phase modulation with uniform amplitude. Professor Jang said, “Our research proposes a new methodology in dynamic phase modulation that breaks through the conventional limits and trade-offs, while being broadly applicable in diverse types of metasurfaces. We hope that this idea helps researchers implement and realize many key applications of metasurfaces, such as LIDAR and holograms, so that the nanophotonics industry keeps growing and provides a brighter technological future.” The research paper authored by Ju Young Kim and Juho Park, et al., and titled "Full 2π Tunable Phase Modulation Using Avoided Crossing of Resonances" was published in Nature Communications on April 19. The research was funded by the Samsung Research Funding & Incubation Center of Samsung Electronics. -Publication: Ju Young Kim, Juho Park, Gregory R. Holdman, Jacob T. Heiden, Shinho Kim, Victor W. Brar, and Min Seok Jang, “Full 2π Tunable Phase Modulation Using Avoided Crossing of Resonances” Nature Communications on April 19 (2022). doi.org/10.1038/s41467-022-29721-7 -Profile Professor Min Seok Jang School of Electrical Engineering KAIST
LightPC Presents a Resilient System Using Only Non..
Lightweight Persistence Centric System (LightPC) ensures both data and execution persistence for energy-efficient full system persistence A KAIST research team has developed hardware and software technology that ensures both data and execution persistence. The Lightweight Persistence Centric System (LightPC) makes the systems resilient against power failures by utilizing only non-volatile memory as the main memory. “We mounted non-volatile memory on a system board prototype and created an operating system to verify the effectiveness of LightPC,” said Professor Myoungsoo Jung. The team confirmed that LightPC validated its execution while powering up and down in the middle of execution, showing up to eight times more memory, 4.3 times faster application execution, and 73％ lower power consumption compared to traditional systems. Professor Jung said that LightPC can be utilized in a variety of fields such as data centers and high-performance computing to provide large-capacity memory, high performance, low power consumption, and service reliability. In general, power failures on legacy systems can lead to the loss of data stored in the DRAM-based main memory. Unlike volatile memory such as DRAM, non-volatile memory can retain its data without power. Although non-volatile memory has the characteristics of lower power consumption and larger capacity than DRAM, non-volatile memory is typically used for the task of secondary storage due to its lower write performance. For this reason, nonvolatile memory is often used with DRAM. However, modern systems employing non-volatile memory-based main memory experience unexpected performance degradation due to the complicated memory microarchitecture. To enable both data and execution persistent in legacy systems, it is necessary to transfer the data from the volatile memory to the non-volatile memory. Checkpointing is one possible solution. It periodically transfers the data in preparation for a sudden power failure. While this technology is essential for ensuring high mobility and reliability for users, checkpointing also has fatal drawbacks. It takes additional time and power to move data and requires a data recovery process as well as restarting the system. In order to address these issues, the research team developed a processor and memory controller to raise the performance of non-volatile memory-only memory. LightPC matches the performance of DRAM by minimizing the internal volatile memory components from non-volatile memory, exposing the non-volatile memory (PRAM) media to the host, and increasing parallelism to service on-the-fly requests as soon as possible. The team also presented operating system technology that quickly makes execution states of running processes persistent without the need for a checkpointing process. The operating system prevents all modifications to execution states and data by keeping all program executions idle before transferring data in order to support consistency within a period much shorter than the standard power hold-up time of about 16 minutes. For consistency, when the power is recovered, the computer almost immediately revives itself and re-executes all the offline processes immediately without the need for a boot process. The researchers will present their work (LightPC: Hardware and Software Co-Design for Energy-Efficient Full System Persistence) at the International Symposium on Computer Architecture (ISCA) 2022 in New York in June. More information is available at the CAMELab website (http://camelab.org). < Figure 1. Customized system prototype (left: System prototype, Right: Implementation view) > < Figure 2. Overview of the proposed LightPC > < Figure 3. Energy and performance of the LightPC/LegacyPC (Right: Cycle norm. to legacy PC) > -Profile: Professor Myoungsoo Jung Computer Architecture and Memory Systems Laboratory (CAMEL) http://camelab.org School of Electrical Engineering KAIST
Decoding Brain Signals to Control a Robotic Arm
Advanced brain-machine interface system successfully interprets arm movement directions from neural signals in the brain < Figure:Experimental paradigm. Subjects were instructed to perform reach-and-grasp movements to designate the locations of the target in three-dimensional space. (a) Subjects A and B were provided the visual cue as a real tennis ball at one of four pseudo-randomized locations. (b) Subjects A and B were provided the visual cue as a virtual reality clip showing a sequence of five stages of a reach-and-grasp movement. > Researchers have developed a mind-reading system for decoding neural signals from the brain during arm movement. The method, described in the journal Applied Soft Computing, can be used by a person to control a robotic arm through a brain-machine interface (BMI). A BMI is a device that translates nerve signals into commands to control a machine, such as a computer or a robotic limb. There are two main techniques for monitoring neural signals in BMIs: electroencephalography (EEG) and electrocorticography (ECoG). The EEG exhibits signals from electrodes on the surface of the scalp and is widely employed because it is non-invasive, relatively cheap, safe and easy to use. However, the EEG has low spatial resolution and detects irrelevant neural signals, which makes it difficult to interpret the intentions of individuals from the EEG. On the other hand, the ECoG is an invasive method that involves placing electrodes directly on the surface of the cerebral cortex below the scalp. Compared with the EEG, the ECoG can monitor neural signals with much higher spatial resolution and less background noise. However, this technique has several drawbacks. “The ECoG is primarily used to find potential sources of epileptic seizures, meaning the electrodes are placed in different locations for different patients and may not be in the optimal regions of the brain for detecting sensory and movement signals,” explained Professor Jaeseung Jeong, a brain scientist at KAIST. “This inconsistency makes it difficult to decode brain signals to predict movements.” To overcome these problems, Professor Jeong’s team developed a new method for decoding ECoG neural signals during arm movement. The system is based on a machine-learning system for analysing and predicting neural signals called an ‘echo-state network’ and a mathematical probability model called the Gaussian distribution. In the study, the researchers recorded ECoG signals from four individuals with epilepsy while they were performing a reach-and-grasp task. Because the ECoG electrodes were placed according to the potential sources of each patient’s epileptic seizures, only 22％ to 44％ of the electrodes were located in the regions of the brain responsible for controlling movement. During the movement task, the participants were given visual cues, either by placing a real tennis ball in front of them, or via a virtual reality headset showing a clip of a human arm reaching forward in first-person view. They were asked to reach forward, grasp an object, then return their hand and release the object, while wearing motion sensors on their wrists and fingers. In a second task, they were instructed to imagine reaching forward without moving their arms. The researchers monitored the signals from the ECoG electrodes during real and imaginary arm movements, and tested whether the new system could predict the direction of this movement from the neural signals. They found that the novel decoder successfully classified arm movements in 24 directions in three-dimensional space, both in the real and virtual tasks, and that the results were at least five times more accurate than chance. They also used a computer simulation to show that the novel ECoG decoder could control the movements of a robotic arm. Overall, the results suggest that the new machine learning-based BCI system successfully used ECoG signals to interpret the direction of the intended movements. The next steps will be to improve the accuracy and efficiency of the decoder. In the future, it could be used in a real-time BMI device to help people with movement or sensory impairments. This research was supported by the KAIST Global Singularity Research Program of 2021, Brain Research Program of the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning, and the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education. -Publication Hoon-Hee Kim, Jaeseung Jeong, “An electrocorticographic decoder for arm movement for brain-machine interface using an echo state network and Gaussian readout,” Applied Soft Computing online December 31, 2021 (doi.org/10.1016/j.asoc.2021.108393) -Profile Professor Jaeseung Jeong Department of Bio and Brain Engineering College of Engineering KAIST
Improving speech intelligibility with Privacy-Pres..
Privacy-Preserving AR system can augment the speaker's speech with real-life subtitles to overcome the loss of contextual cues caused by mask-wearing and social distancing during the COVID-19 pandemic. Degraded speech intelligibility induces face-to-face conversation participants to speak louder and more distinctively, exposing the content to potential eavesdroppers. Similarly, people with face masks deteriorate their speech intelligibility, especially during the post-covid-19 crisis. Augmented Reality (AR) can serve as an effective tool to visualise people conversations and promote speech intelligibility, known as speech augmentation. However, visualised conversations without proper privacy management can expose AR users to privacy risks. An international research team of Prof. Lik-Hang LEE in the Department of Industrial and Systems Engineering at KAIST and Prof. Pan HUI in Computational Media and Arts at Hong Kong University of Science and Technology employed a conversation-oriented Contextual Integrity (CI) principle to develop a privacy-preserving AR framework for speech augmentation. At its core, the framework, namely Theophany, establishes ad-hoc social networks between relevant conversation participants to exchange contextual information and improve speech intelligibility in real-time. < Figure 1: A real-life subtitle application with AR headsets > Theophany has been implemented as a real-life subtitle application in AR to improve speech intelligibility in daily conversations (Figure 1). This implementation leverages a multi-modal channel, such as eye-tracking, camera, and audio. Theophany transforms the user's speech into text and estimates the intended recipients through gaze detection. The CI Enforcer module evaluates the sentences' sensitivity. If the sensitivity meets the speaker's privacy threshold, the sentence is transmitted to the appropriate recipients (Figure 2). < Figure 2: Multi-modal Contextual Integrity Channel > Based on the principles of Contextual Integrity (CI), parameters of privacy perception are designed for privacy-preserving face-to-face conversations, such as topic, location, and participants. Accordingly, Theophany operation depends on the topic and session. Figure 3 demonstrates several illustrative conversation sessions: (a) the topic is not sensitive and transmitted to everybody in the user's gaze. (b) the topic is work-sensitive and only transmitted to the coworker. (c) the topic is sensitive and only transmitted to the friend in the user's gaze. A new friend entering the user's gaze only gets the textual transcription once a new session (topic) starts (d). (e) the topic is highly sensitive, and nobody gets the textual transcription. < Figure 3: Speech Augmentation in five illustrative sessions > Theophany within a prototypical AR system augments the speaker's speech with real-life subtitles to overcome the loss of contextual cues caused by mask-wearing and social distancing during the COVID-19 pandemic. The research was published in ACM Multimedia under the title of ＇Theophany: Multi-modal Speech Augmentation in Instantaneous Privacy Channels' (DOI: 10.1145/3474085.3475507), being selected as one of the best paper award candidates (Top 5). Note that the first author is an alumnus from the Industrial and Systems Engineering Department at KAIST. Short Bio: Lik-Hang Lee received a PhD degree from SyMLab, Hong Kong University of Science and Technology, and the Bachelor's and M.Phil. degrees from the University of Hong Kong. He is currently an assistant professor (tenure-track) with the Korea Advanced Institute of Science and Technology (KAIST), South Korea, and the head of the Augmented Reality and Media Laboratory, KAIST. He has built and designed various human-centric computing specializing in augmented and virtual realities (AR/VR). In recent years, he has published more than 30 research papers on AR/VR at prestigious conferences such as ACM WWW, ACM IMWUT, ACM Multimedia, ACM CSUR, IEEE Percom, and so on. He also serves the research community, as TPCs, PCs and workshop organizers, at some prestigious venues, such as AAAI, IJCAI, IEEE PERCOM, ACM CHI, ACM Multimedia, ACM IMWUT, IEEE VR, etc. Photo:
Eco-Friendly Micro-Supercapacitors Using Fallen Le..
Femtosecond micro-supercapacitors on a single leaf could easily be applied to wearable electronics, smart houses, and IoTs < Image: The schematic illustration of the production of femtosecond laser-induced graphene. > A KAIST research team has developed a graphene-inorganic-hybrid micro-supercapacitor made of leaves using femtosecond direct laser writing lithography. The advancement of wearable electronic devices is synonymous with innovations in flexible energy storage devices. Of the various energy storage devices, micro-supercapacitors have drawn a great deal of interest for their high electrical power density, long lifetimes, and short charging times. However, there has been an increase in waste battery generation with the increases in the consumption and use of electronic equipment as well as the short replacement period that follows advancements in mobile devices. The safety and environmental issues involved in the collection, recycling, and processing of such waste batteries are creating a number of challenges. Forests cover about 30 percent of the Earth’s surface, producing a huge amount of fallen leaves. This naturally occurring biomass comes in large quantities and is both biodegradable and reusable, which makes it an attractive, eco-friendly material. However, if the leaves are left neglected instead of being used efficiently, they can contribute to fires or water pollution. To solve both problems at once, a research team led by Professor Young-Jin Kim from the Department of Mechanical Engineering and Dr. Hana Yoon from the Korea Institute of Energy Research developed a one-step technology that can create porous 3D graphene micro-electrodes with high electrical conductivity without additional treatment in atmospheric conditions by irradiating femtosecond laser pulses on the surface of the leaves without additional materials. Taking this strategy further, the team also suggested a method for producing flexible micro-supercapacitors. They showed that this technique could quickly and easily produce porous graphene-inorganic-hybrid electrodes at a low price, and validated their performance by using the graphene micro-supercapacitors to power an LED and an electronic watch that could function as a thermometer, hygrometer, and timer. These results open up the possibility of the mass production of flexible and green graphene-based electronic devices. Professor Young-Jin Kim said, “Leaves create forest biomass that comes in unmanageable quantities, so using them for next-generation energy storage devices makes it possible for us to reuse waste resources, thereby establishing a virtuous cycle.” This research was published in Advanced Functional Materials last month and was sponsored by the Ministry of Agriculture Food and Rural Affairs, the Korea Forest Service, and the Korea Institute of Energy Research. -Publication Truong-Son Dinh Le, Yeong A. Lee, Han Ku Nam, Kyu Yeon Jang, Dongwook Yang, Byunggi Kim, Kanghoon Yim, Seung Woo Kim, Hana Yoon, and Young-jin Kim, “Green Flexible Graphene-Inorganic-Hybrid Micro-Supercapacitors Made of Fallen Leaves Enabled by Ultrafast Laser Pulses," December 05, 2021, Advanced Functional Materials (doi.org/10.1002/adfm.202107768) -Profile Professor Young-Jin Kim Ultra-Precision Metrology and Manufacturing (UPM2) Laboratory Department of Mechanical Engineering KAIST
AI Light-Field Camera Reads 3D Facial Expressions
Machine-learned, light-field camera reads facial expressions from high-contrast illumination invariant 3D facial images < Image: Facial expression reading based on MLP classification from 3D depth maps and 2D images obtained by NIR-LFC > A joint research team led by Professors Ki-Hun Jeong and Doheon Lee from the KAIST Department of Bio and Brain Engineering reported the development of a technique for facial expression detection by merging near-infrared light-field camera techniques with artificial intelligence (AI) technology. Unlike a conventional camera, the light-field camera contains micro-lens arrays in front of the image sensor, which makes the camera small enough to fit into a smart phone, while allowing it to acquire the spatial and directional information of the light with a single shot. The technique has received attention as it can reconstruct images in a variety of ways including multi-views, refocusing, and 3D image acquisition, giving rise to many potential applications. However, the optical crosstalk between shadows caused by external light sources in the environment and the micro-lens has limited existing light-field cameras from being able to provide accurate image contrast and 3D reconstruction. The joint research team applied a vertical-cavity surface-emitting laser (VCSEL) in the near-IR range to stabilize the accuracy of 3D image reconstruction that previously depended on environmental light. When an external light source is shone on a face at 0-, 30-, and 60-degree angles, the light field camera reduces 54％ of image reconstruction errors. Additionally, by inserting a light-absorbing layer for visible and near-IR wavelengths between the micro-lens arrays, the team could minimize optical crosstalk while increasing the image contrast by 2.1 times. Through this technique, the team could overcome the limitations of existing light-field cameras and was able to develop their NIR-based light-field camera (NIR-LFC), optimized for the 3D image reconstruction of facial expressions. Using the NIR-LFC, the team acquired high-quality 3D reconstruction images of facial expressions expressing various emotions regardless of the lighting conditions of the surrounding environment. The facial expressions in the acquired 3D images were distinguished through machine learning with an average of 85％ accuracy – a statistically significant figure compared to when 2D images were used. Furthermore, by calculating the interdependency of distance information that varies with facial expression in 3D images, the team could identify the information a light-field camera utilizes to distinguish human expressions. Professor Ki-Hun Jeong said, “The sub-miniature light-field camera developed by the research team has the potential to become the new platform to quantitatively analyze the facial expressions and emotions of humans.” To highlight the significance of this research, he added, “It could be applied in various fields including mobile healthcare, field diagnosis, social cognition, and human-machine interactions.” This research was published in Advanced Intelligent Systems online on December 16, under the title, “Machine-Learned Light-field Camera that Reads Facial Expression from High-Contrast and Illumination Invariant 3D Facial Images.” This research was funded by the Ministry of Science and ICT and the Ministry of Trade, Industry and Energy. -Publication “Machine-learned light-field camera that reads fascial expression from high-contrast and illumination invariant 3D facial images,” Sang-In Bae, Sangyeon Lee, Jae-Myeong Kwon, Hyun-Kyung Kim. Kyung-Won Jang, Doheon Lee, Ki-Hun Jeong, Advanced Intelligent Systems, December 16, 2021 (doi.org/10.1002/aisy.202100182) -Profile Professor Ki-Hun Jeong Biophotonic Laboratory Department of Bio and Brain Engineering KAIST Professor Doheon Lee Department of Bio and Brain Engineering KAIST
Face Detection in Untrained Deep Neural Networks
A KAIST team shows that primitive visual selectivity of faces can arise spontaneously in completely untrained deep neural networks Researchers have found that higher visual cognitive functions can arise spontaneously in untrained neural networks. A KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering has shown that visual selectivity of facial images can arise even in completely untrained deep neural networks. This new finding has provided revelatory insights into mechanisms underlying the development of cognitive functions in both biological and artificial neural networks, also making a significant impact on our understanding of the origin of early brain functions before sensory experiences. The study published in Nature Communications on December 16 demonstrates that neuronal activities selective to facial images are observed in randomly initialized deep neural networks in the complete absence of learning, and that they show the characteristics of those observed in biological brains. The ability to identify and recognize faces is a crucial function for social behavior, and this ability is thought to originate from neuronal tuning at the single or multi-neuronal level. Neurons that selectively respond to faces are observed in young animals of various species, and this raises intense debate whether face-selective neurons can arise innately in the brain or if they require visual experience. Using a model neural network that captures properties of the ventral stream of the visual cortex, the research team found that face-selectivity can emerge spontaneously from random feedforward wirings in untrained deep neural networks. The team showed that the character of this innate face-selectivity is comparable to that observed with face-selective neurons in the brain, and that this spontaneous neuronal tuning for faces enables the network to perform face detection tasks. These results imply a possible scenario in which the random feedforward connections that develop in early, untrained networks may be sufficient for initializing primitive visual cognitive functions. Professor Paik said, “Our findings suggest that innate cognitive functions can emerge spontaneously from the statistical complexity embedded in the hierarchical feedforward projection circuitry, even in the complete absence of learning”. He continued, “Our results provide a broad conceptual advance as well as advanced insight into the mechanisms underlying the development of innate functions in both biological and artificial neural networks, which may unravel the mystery of the generation and evolution of intelligence.” This work was supported by the National Research Foundation of Korea (NRF) and by the KAIST singularity research project. -Publication Seungdae Baek, Min Song, Jaeson Jang, Gwangsu Kim, and Se-Bum Baik, “Face detection in untrained deep neural network,” Nature Communications 12, 7328 on Dec.16, 2021 (https://doi.org/10.1038/s41467-021-27606-9) -Profile Professor Se-Bum Paik Visual System and Neural Network Laboratory Program of Brain and Cognitive Engineering Department of Bio and Brain Engineering College of Engineering KAIST
Connecting the Dots to Find New Treatments for Bre..
Systems biologists uncovered new ways of cancer cell reprogramming to treat drug-resistant cancers < Professor Kwang-Hyun Cho and colleagues have developed a mathematical model and identified optimal targets reprogramming basal-like cancer cells into hormone therapy-responsive luminal-A cells by deciphering the complex molecular interactions within these cells through a systems biological approach. > Scientists at KAIST believe they may have found a way to reverse an aggressive, treatment-resistant type of breast cancer into a less dangerous kind that responds well to treatment. The study involved the use of mathematical models to untangle the complex genetic and molecular interactions that occur in the two types of breast cancer, but could be extended to find ways for treating many others. The study’s findings were published in the journal Cancer Research. Basal-like tumours are the most aggressive type of breast cancer, with the worst prognosis. Chemotherapy is the only available treatment option, but patients experience high recurrence rates. On the other hand, luminal-A breast cancer responds well to drugs that specifically target a receptor on their cell surfaces, called estrogen receptor alpha (ERα). KAIST systems biologist Kwang-Hyun Cho and colleagues analyzed the complex molecular and genetic interactions of basal-like and luminal-A breast cancers to find out if there might be a way to switch the former to the latter and give patients a better chance to respond to treatment. To do this, they accessed large amounts of cancer and patient data to understand which genes and molecules are involved in the two types. They then input this data into a mathematical model that represents genes, proteins and molecules as dots and the interactions between them as lines. The model can be used to conduct simulations and see how interactions change when certain genes are turned on or off. “There have been a tremendous number of studies trying to find therapeutic targets for treating basal-like breast cancer patients,” says Cho. “But clinical trials have failed due to the complex and dynamic nature of cancer. To overcome this issue, we looked at breast cancer cells as a complex network system and implemented a systems biological approach to unravel the underlying mechanisms that would allow us to reprogram basal-like into luminal-A breast cancer cells.” Using this approach, followed by experimental validation on real breast cancer cells, the team found that turning off two key gene regulators, called BCL11A and HDAC1/2, switched a basal-like cancer signalling pathway into a different one used by luminal-A cancer cells. The switch reprograms the cancer cells and makes them more responsive to drugs that target ERα receptors. However, further tests will be needed to confirm that this also works in animal models and eventually humans. “Our study demonstrates that the systems biological approach can be useful for identifying novel therapeutic targets,” says Cho. The researchers are now expanding its breast cancer network model to include all breast cancer subtypes. Their ultimate aim is to identify more drug targets and to understand the mechanisms that could drive drug-resistant cells to turn into drug-sensitive ones. This work was supported by the National Research Foundation of Korea, the Ministry of Science and ICT, Electronics and Telecommunications Research Institute, and the KAIST Grand Challenge 30 Project. -Publication Sea R. Choi, Chae Young Hwang, Jonghoon Lee, and Kwang-Hyun Cho, “Network Analysis Identifies Regulators of Basal-like Breast Cancer Reprogramming and Endocrine Therapy Vulnerability,” Cancer Research, November 30. (doi:10.1158/0008-5472.CAN-21-0621) -Profile Professor Kwang-Hyun Cho Laboratory for Systems Biology and Bio-Inspired Engineering Department of Bio and Brain Engineering KAIST
A Study Shows Reactive Electrolyte Additives Impro..
Stable electrode-electrolyte interfaces constructed by fluorine- and nitrogen-donating ionic additives provide an opportunity to improve high-performance lithium metal batteries < A combination of lithium difluoro (bisoxalato) phosphate as an F donor and lithium nitrate as an N donor with different electron accepting abilities and adsorption tendencies improves the cycle performance of Li｜NCM811 full cells through the creation of a dual-layer SEI on a Li metal anode and a protective CEI on a Ni-rich cathode. > A research team showed that electrolyte additives increase the lifetime of lithium metal batteries and remarkably improve the performance of fast charging and discharging. Professor Nam-Soon Choi’s team from the Department of Chemical and Biomolecular Engineering at KAIST hierarchized the solid electrolyte interphase to make a dual-layer structure and showed groundbreaking run times for lithium metal batteries. The team applied two electrolyte additives that have different reduction and adsorption properties to improve the functionality of the dual-layer solid electrolyte interphase. In addition, the team has confirmed that the structural stability of the nickel-rich cathode was achieved through the formation of a thin protective layer on the cathode. This study was reported in Energy Storage Materials. Securing high-energy-density lithium metal batteries with a long lifespan and fast charging performance is vital for realizing their ubiquitous use as superior power sources for electric vehicles. Lithium metal batteries comprise a lithium metal anode that delivers 10 times higher capacity than the graphite anodes in lithium-ion batteries. Therefore, lithium metal is an indispensable anode material for realizing high-energy rechargeable batteries. However, undesirable reactions among the electrolytes with lithium metal anodes can reduce the power and this remains an impediment to achieving a longer battery lifespan. Previous studies only focused on the formation of the solid electrolyte interphase on the surface of the lithium metal anode. The team designed a way to create a dual-layer solid electrolyte interphase to resolve the instability of the lithium metal anode by using electrolyte additives, depending on their electron accepting ability and adsorption tendencies. This hierarchical structure of the solid electrolyte interphase on the lithium metal anode has the potential to be further applied to lithium-alloy anodes, lithium storage structures, and anode-free technology to meet market expectations for electrolyte technology. The batteries with lithium metal anodes and nickel-rich cathodes represented 80.9％ of the initial capacity after 600 cycles and achieved a high Coulombic efficiency of 99.94％. These remarkable results contributed to the development of protective dual-layer solid electrolyte interphase technology for lithium metal anodes. Professor Choi said that the research suggests a new direction for the development of electrolyte additives to regulate the unstable lithium metal anode-electrolyte interface, the biggest hurdle in research on lithium metal batteries. She added that anode-free secondary battery technology is expected to be a game changer in the secondary battery market and electrolyte additive technology will contribute to the enhancement of anode-free secondary batteries through the stabilization of lithium metal anodes. This research was funded by the Technology Development Program to Solve Climate Change of the National Research Foundation in Korea funded by the Ministry of Science, ICT & Future Planning and the Technology Innovation Program funded by the Ministry of Trade, Industry & Energy, and Hyundai Motor Company. - Publication Saehun Kim, Sung O Park, Min-Young Lee, Jeong-A Lee, Imanuel Kristanto, Tae Kyung Lee, Daeyeon Hwang, Juyoung Kim, Tae-Ung Wi, Hyun-Wook Lee, Sang Kyu Kwak, and Nam Soon Choi, “Stable electrode-electrolyte interfaces constructed by fluorine- and nitrogen-donating ionic additives for high-performance lithium metal batteries,” Energy Storage Materials, 45, 1-13 (2022), (doi: https://doi.org/10.1016/j.ensm.2021.10.031) - Profile Professor Nam-Soon Choi Energy Materials Laboratory Department of Chemical and Biomolecular Engineering KAIST
Deep Learning Framework to Enable Material Design ..
Researchers propose a deep neural network-based forward design space exploration using active transfer learning and data augmentation < Figure 1: Schematic of deep learning framework for material design space exploration. Schematic of gradual expansion of reliable prediction domain of DNN based on the addition of data generated from the hyper-heuristic genetic algorithm and active transfer learning. > A new study proposed a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. Professor Seungwha Ryu believes that this study will help address a variety of optimization problems that have an astronomical number of possible design configurations. For the grid composite optimization problem, the proposed framework was able to provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5％ of the initial training data-set size. This study was reported in npj Computational Materials last month. “We wanted to mitigate the limitation of the neural network, weak predictive power beyond the training set domain for the material or structure design,” said Professor Ryu from the Department of Mechanical Engineering. Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome this limitation also suffer from weak predictive power for the unseen domain. Professor Ryu’s team, in collaboration with researchers from Professor Grace Gu’s group at UC Berkeley, devised a design method that simultaneously expands the domain using the strong predictive power of a deep neural network and searches for the optimal design by repetitively performing three key steps. First, it searches for few candidates with improved properties located close to the training set via genetic algorithms, by mixing superior designs within the training set. Then, it checks to see if the candidates really have improved properties, and expands the training set by duplicating the validated designs via a data augmentation method. Finally, they can expand the reliable prediction domain by updating the neural network with the new superior designs via transfer learning. Because the expansion proceeds along relatively narrow but correct routes toward the optimal design (depicted in the schematic of Fig. 1), the framework enables an efficient search. As a data-hungry method, a deep neural network model tends to have reliable predictive power only within and near the domain of the training set. When the optimal configuration of materials and structures lies far beyond the initial training set, which frequently is the case, neural network-based design methods suffer from weak predictive power and become inefficient. Researchers expect that the framework will be applicable for a wide range of optimization problems in other science and engineering disciplines with astronomically large design space, because it provides an efficient way of gradually expanding the reliable prediction domain toward the target design while avoiding the risk of being stuck in local minima. Especially, being a less-data-hungry method, design problems in which data generation is time-consuming and expensive will benefit most from this new framework. The research team is currently applying the optimization framework for the design task of metamaterial structures, segmented thermoelectric generators, and optimal sensor distributions. “From these sets of on-going studies, we expect to better recognize the pros and cons, and the potential of the suggested algorithm. Ultimately, we want to devise more efficient machine learning-based design approaches,” explained Professor Ryu.This study was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research Project. -Publication Yongtae Kim, Youngsoo, Charles Yang, Kundo Park, Grace X. Gu, and Seunghwa Ryu, “Deep learning framework for material design space exploration using active transfer learning and data augmentation,” npj Computational Materials (https://doi.org/10.1038/s41524-021-00609-2) -Profile Professor Seunghwa Ryu Mechanics & Materials Modeling Lab Department of Mechanical Engineering KAIST
A Mechanism Underlying Most Common Cause of Epilep..
An interdisciplinary study shows that neurons carrying somatic mutations in MTOR can lead to focal epileptogenesis via non-cell-autonomous hyperexcitability of nearby nonmutated neurons < Image 1: Neurons carrying somatic mutations in MTOR lead to focal epileptogenesis via non-cell autonomous hyperexcitability of nearby non-mutated neurons. (Left) Neurons with mTOR mutation (green) observed in a mouse brain section image. (Middle) Network model consisting of a small portion of mutated and a large portion of nearby non-mutated neurons. (Right) Mitigated hyperactivity of non-mutated neurons after the treatment of inhibitor of adenosine kinase. > During fetal development, cells should migrate to the outer edge of the brain to form critical connections for information transfer and regulation in the body. When even a few cells fail to move to the correct location, the neurons become disorganized and this results in focal cortical dysplasia. This condition is the most common cause of seizures that cannot be controlled with medication in children and the second most common cause in adults. Now, an interdisciplinary team studying neurogenetics, neural networks, and neurophysiology at KAIST has revealed how dysfunctions in even a small percentage of cells can cause disorder across the entire brain. They published their results on June 28 in Annals of Neurology. The work builds on a previous finding, also by a KAIST scientists, who found that focal cortical dysplasia was caused by mutations in the cells involved in mTOR, a pathway that regulates signaling between neurons in the brain. “Only 1 to 2％ of neurons carrying mutations in the mTOR signaling pathway that regulates cell signaling in the brain have been found to include seizures in animal models of focal cortical dysplasia,” said Professor Jong-Woo Sohn from the Department of Biological Sciences. “The main challenge of this study was to explain how nearby non-mutated neurons are hyperexcitable.” Initially, the researchers hypothesized that the mutated cells affected the number of excitatory and inhibitory synapses in all neurons, mutated or not. These neural gates can trigger or halt activity, respectively, in other neurons. Seizures are a result of extreme activity, called hyperexcitability. If the mutated cells upend the balance and result in more excitatory cells, the researchers thought, it made sense that the cells would be more susceptible to hyperexcitability and, as a result, seizures. “Contrary to our expectations, the synaptic input balance was not changed in either the mutated or non-mutated neurons,” said Professor Jeong Ho Lee from the Graduate School of Medical Science and Engineering. “We turned our attention to a protein overproduced by mutated neurons.” The protein is adenosine kinase, which lowers the concentration of adenosine. This naturally occurring compound is an anticonvulsant and works to relax vessels. In mice engineered to have focal cortical dysplasia, the researchers injected adenosine to replace the levels lowered by the protein. It worked and the neurons became less excitable. “We demonstrated that augmentation of adenosine signaling could attenuate the excitability of non-mutated neurons,” said Professor Se-Bum Paik from the Department of Bio and Brain Engineering. The effect on the non-mutated neurons was the surprising part, according to Paik. “The seizure-triggering hyperexcitability originated not in the mutation-carrying neurons, but instead in the nearby non-mutated neurons,” he said. The mutated neurons excreted more adenosine kinase, reducing the adenosine levels in the local environment of all the cells. With less adenosine, the non-mutated neurons became hyperexcitable, leading to seizures. “While we need further investigate into the relationship between the concentration of adenosine and the increased excitation of nearby neurons, our results support the medical use of drugs to activate adenosine signaling as a possible treatment pathway for focal cortical dysplasia,” Professor Lee said. The Suh Kyungbae Foundation, the Korea Health Technology Research and Development Project, the Ministry of Health & Welfare, and the National Research Foundation in Korea funded this work. -Publication: Koh, H.Y., Jang, J., Ju, S.H., Kim, R., Cho, G.-B., Kim, D.S., Sohn, J.-W., Paik, S.-B. and Lee, J.H. (2021), ‘Non–Cell Autonomous Epileptogenesis in Focal Cortical Dysplasia’ Annals of Neurology, 90: 285 299. (https://doi.org/10.1002/ana.26149) -Profile Professor Jeong Ho Lee Translational Neurogenetics Lab https://tnl.kaist.ac.kr/ Graduate School of Medical Science and Engineering KAIST Professor Se-Bum Paik Visual System and Neural Network Laboratory http://vs.kaist.ac.kr/ Department of Bio and Brain Engineering KAIST Professor Jong-Woo Sohn Laboratory for Neurophysiology, https://sites.google.com/site/sohnlab2014/home Department of Biological Sciences KAIST Dr. Hyun Yong Koh Translational Neurogenetics Lab Graduate School of Medical Science and Engineering KAIST Dr. Jaeson Jang Ph.D. Visual System and Neural Network Laboratory Department of Bio and Brain Engineering KAIST Sang Hyeon Ju M.D. Laboratory for Neurophysiology Department of Biological Sciences KAIST
Brain-Inspired Highly Scalable Neuromorphic Hardwa..
Neurons and synapses based on single transistor can dramatically reduce the hardware cost and accelerate the commercialization of neuromorphic hardware < Single transistor neurons and synapses fabricated using a standard silicon CMOS process. They are co-integrated on the same 8-inch wafer. > KAIST researchers fabricated a brain-inspired highly scalable neuromorphic hardware by co-integrating single transistor neurons and synapses. Using standard silicon complementary metal-oxide-semiconductor (CMOS) technology, the neuromorphic hardware is expected to reduce chip cost and simplify fabrication procedures. The research team led by Yang-Kyu Choi and Sung-Yool Choi produced a neurons and synapses based on single transistor for highly scalable neuromorphic hardware and showed the ability to recognize text and face images. This research was featured in Science Advances on August 4. Neuromorphic hardware has attracted a great deal of attention because of its artificial intelligence functions, but consuming ultra-low power of less than 20 watts by mimicking the human brain. To make neuromorphic hardware work, a neuron that generates a spike when integrating a certain signal, and a synapse remembering the connection between two neurons are necessary, just like the biological brain. However, since neurons and synapses constructed on digital or analog circuits occupy a large space, there is a limit in terms of hardware efficiency and costs. Since the human brain consists of about 1011 neurons and 1014 synapses, it is necessary to improve the hardware cost in order to apply it to mobile and IoT devices. To solve the problem, the research team mimicked the behavior of biological neurons and synapses with a single transistor, and co-integrated them onto an 8-inch wafer. The manufactured neuromorphic transistors have the same structure as the transistors for memory and logic that are currently mass-produced. In addition, the neuromorphic transistors proved for the first time that they can be implemented with a ‘Janus structure’ that functions as both neuron and synapse, just like coins have heads and tails. Professor Yang-Kyu Choi said that this work can dramatically reduce the hardware cost by replacing the neurons and synapses that were based on complex digital and analog circuits with a single transistor. "We have demonstrated that neurons and synapses can be implemented using a single transistor," said Joon-Kyu Han, the first author. "By co-integrating single transistor neurons and synapses on the same wafer using a standard CMOS process, the hardware cost of the neuromorphic hardware has been improved, which will accelerate the commercialization of neuromorphic hardware,” Han added.This research was supported by the National Research Foundation (NRF) and IC Design Education Center (IDEC). -Publication Joon-Kyu Han, Sung-Yool Choi, Yang-Kyu Choi, et al.“Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware,” Science Advances (DOI: 10.1126/sciadv.abg8836) -Profile Professor Yang-Kyu Choi Nano-Oriented Bio-Electronics Lab https://sites.google.com/view/nobelab/ School of Electrical Engineering KAIST Professor Sung-Yool Choi Molecular and Nano Device Laboratory https://www.mndl.kaist.ac.kr/ School of Electrical Engineering KAIST