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"Kim, Hyunsoo"
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The shape of time : Korean art after 1989
Here is an illustrated overview of contemporary Korean art that offers insight into the country's tumultuous modern history and its multifaceted and vibrant art scene. Focusing on the work of 33 artists, this volume examines the ways contemporary Korean art reflects the dynamic changes in the country following the 1980 Gwangju Uprising and 1988 Seoul Olympics, when a newly democratic South Korea opened up to the rest of the world and quickly became a key player, both economically and culturally, on the global stage.
Book
Unraveling the intricacies of osteoclast differentiation and maturation: insight into novel therapeutic strategies for bone-destructive diseases
Osteoclasts are the principal cells that efficiently resorb bone. Numerous studies have attempted to reveal the molecular pathways leading to the differentiation and activation of osteoclasts to improve the treatment and prevention of osteoporosis and other bone-destructive diseases. While the cumulative knowledge of osteoclast regulatory molecules, such as receptor activator of nuclear factor-kB ligand (RANKL) and nuclear factor of activated T cells 1 (NFATc1), contributes to the understanding of the developmental progression of osteoclasts, little is known about how the discrete steps of osteoclastogenesis modify osteoclast status but not the absolute number of osteoclasts. The regulatory mechanisms involved in osteoclast maturation but not those involved in differentiation deserve special attention due to their potential use in establishing a more effective treatment strategy: targeting late-phase differentiation while preserving coupled bone formation. Recent studies have shed light on the molecules that govern late-phase osteoclast differentiation and maturation, as well as the metabolic changes needed to adapt to shifting metabolic demands. This review outlines the current understanding of the regulation of osteoclast differentiation, as well as osteoclast metabolic adaptation as a differentiation control mechanism. Additionally, this review introduces molecules that regulate the late-phase osteoclast differentiation and thus minimally impact coupled bone formation.
Metabolic adaptation in osteoclasts: a novel approach to bone disease therapy
This study explores the process of bone remodeling, particularly the role of osteoclasts (cells that break down bone) and osteoblasts (cells that build new bone). There is a lack of understanding about the biology of the osteoclast differentiation process (the process by which a cell changes from one type to another), which is vital for maintaining bone health. The scientists reviewed how osteoclast differentiation is regulated, focusing on cell signaling (communication between cells) and metabolic adaptation (how cells change their metabolism to survive). They discovered certain molecules could change osteoclast-mediated bone breakdown without affecting bone formation, potentially serving as treatment targets for diseases that destroy bone. The study concludes that understanding the molecular complexities of late-phase osteoclast differentiation will help improve the treatment and prevention of osteoporosis and other bone-destructive diseases.
This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
Journal Article
Feasibility of DRNN for Identifying Built Environment Barriers to Walkability Using Wearable Sensor Data from Pedestrians’ Gait
Identifying built environment barriers to walkability is the first step toward monitoring and improving our walking environment. Although conventional approaches (i.e., surveys by experts or pedestrians, walking interviews, etc.) to identify built environment barriers have contributed to improving the walking environment, these approaches may require time and effort. To address the limitations of conventional approaches, wearable sensing technologies and data analysis techniques have recently been adopted in the investigation of the built environment. Among various wearable sensors, an inertial measurement unit (IMU) can continuously capture gait-related data, which can be used to identify built environment barriers to walkability. To propose a more efficient method, the author adopts a cascaded bidirectional and unidirectional long short-term memory (LSTM)-based deep recurrent neural network (DRNN) model for classifying human gait activities (normal and abnormal walking) according to walking environmental conditions (i.e., normal and abnormal conditions). This study uses 101,607 gait data collected from the author’s previous study for training and testing a DRNN model. In addition, 31,142 gait data (20 participants) have been newly collected to validate whether the DRNN model is feasible for newly added gait data. The gait activity classification results show that the proposed method can classify normal gaits and abnormal gaits with an accuracy of about 95%. The results also indicate that the proposed method can be used to monitor environmental barriers and improve the walking environment.
Journal Article
The Influence of Fatigue, Recovery, and Environmental Factors on the Body Stability of Construction Workers
In the construction industry, falls, slips, and trips (FST) account for 42.3% of all accidents. The primary cause of FST incidents is directly related to the deterioration of workers’ body stability. To prevent FST-related accidents, it is crucial to understand the interaction between physical fatigue and body stability in construction workers. Therefore, this study investigates the impact of fatigue on body stability in various construction site environments using Dynamic Time Warping (DTW) analysis. We conducted experiments reflecting six different fatigue levels and four environmental conditions. The analysis process involves comparing changes in DTW values derived from acceleration data obtained through wearable sensors across varying fatigue levels and construction environments. The results reveal the following changes in DTW values across different environments and fatigue levels: for non-obstacle, obstacle, water, and oil conditions, DTW values tend to increase as fatigue levels rise. In our experiments, we observed a significant decrease in body stability against external environments starting from fatigue Levels 3 or 4 (30% and 40% of the maximum failure point). In the non-obstacle condition, the DTW values were 9.4 at Level 0, 12.8 at Level 3, and 23.1 at Level 5. In contrast, for the oil condition, which exhibited the highest DTW values, the values were 10.5 at Level 0, 19.1 at Level 3, and 34.5 at Level 5. These experimental results confirm that the body stability of construction workers is influenced by both fatigue levels and external environmental conditions. Further analysis of recovery time, defined as the time it takes for body stability to return to its original level, revealed an increasing trend in recovery time as fatigue levels increased. This study quantitatively demonstrates through wearable sensor data that, as fatigue levels increase, workers experience decreased body stability and longer recovery times. The findings of this study can inform individual worker fatigue management in the future.
Journal Article
A crossbar array of magnetoresistive memory devices for in-memory computing
Implementations of artificial neural networks that borrow analogue techniques could potentially offer low-power alternatives to fully digital approaches
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. One notable example is in-memory computing based on crossbar arrays of non-volatile memories
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that execute, in an analogue manner, multiply–accumulate operations prevalent in artificial neural networks. Various non-volatile memories—including resistive memory
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, phase-change memory
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and flash memory
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—have been used for such approaches. However, it remains challenging to develop a crossbar array of spin-transfer-torque magnetoresistive random-access memory (MRAM)
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, despite the technology’s practical advantages such as endurance and large-scale commercialization
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. The difficulty stems from the low resistance of MRAM, which would result in large power consumption in a conventional crossbar array that uses current summation for analogue multiply–accumulate operations. Here we report a 64 × 64 crossbar array based on MRAM cells that overcomes the low-resistance issue with an architecture that uses resistance summation for analogue multiply–accumulate operations. The array is integrated with readout electronics in 28-nanometre complementary metal–oxide–semiconductor technology. Using this array, a two-layer perceptron is implemented to classify 10,000 Modified National Institute of Standards and Technology digits with an accuracy of 93.23 per cent (software baseline: 95.24 per cent). In an emulation of a deeper, eight-layer Visual Geometry Group-8 neural network with measured errors, the classification accuracy improves to 98.86 per cent (software baseline: 99.28 per cent). We also use the array to implement a single layer in a ten-layer neural network to realize face detection with an accuracy of 93.4 per cent.
A crossbar array of magnetic memory to execute analogue in-memory computing has been developed, and performs image classification and facial detection at low power.
Journal Article
Updating osteoimmunology: regulation of bone cells by innate and adaptive immunity
Osteoimmunology encompasses all aspects of the cross-regulation of bone and the immune system, including various cell types, signalling pathways, cytokines and chemokines, under both homeostatic and pathogenic conditions. A number of key areas are of increasing interest and relevance to osteoimmunology researchers. Although rheumatoid arthritis has long been recognized as one of the most common autoimmune diseases to affect bone integrity, researchers have focused increased attention on understanding how molecular triggers and innate signalling pathways (such as Toll-like receptors and purinergic signalling pathways) related to pathogenic and/or commensal microbiota are relevant to bone biology and rheumatic diseases. Additionally, although most discussions relating to osteoimmune regulation of homeostasis and disease have focused on the effects of adaptive immune responses on bone, evidence exists of the regulation of immune cells by bone cells, a concept that is consistent with the established role of the bone marrow in the development and homeostasis of the immune system. The active regulation of immune cells by bone cells is an interesting emerging component of investigations that seek to understand how to control immune-associated diseases of the bone and joints.
Journal Article
Nearly ferromagnetic spin-triplet superconductivity
Spin-triplet superconductors potentially host topological excitations that are of interest for quantum information processing. We report the discovery of spin-triplet superconductivity in UTe₂, featuring a transition temperature of 1.6 kelvin and a very large and anisotropic upper critical field exceeding 40 teslas. This superconducting phase stability suggests that UTe₂ is related to ferromagnetic superconductors such as UGe₂, URhGe, and UCoGe. However, the lack of magnetic order and the observation of quantum critical scaling place UTe₂ at the paramagnetic end of this ferromagnetic superconductor series. A large intrinsic zero-temperature reservoir of ungapped fermions indicates a highly unconventional type of superconducting pairing.
Journal Article
Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method
Nonnegative matrix factorization (NMF) determines a lower rank approximation of a matrix $A \\in \\mathbb{R}^{m \\times n} \\approx WH$ where an integer $k \\ll \\min(m,n)$ is given and nonnegativity is imposed on all components of the factors $W \\in \\mathbb{R}^{m \\times k}$ and $H \\in \\mathbb{R}^{k \\times n}$. NMF has attracted much attention for over a decade and has been successfully applied to numerous data analysis problems. In applications where the components of the data are necessarily nonnegative, such as chemical concentrations in experimental results or pixels in digital images, NMF provides a more relevant interpretation of the results since it gives nonsubtractive combinations of nonnegative basis vectors. In this paper, we introduce an algorithm for NMF based on alternating nonnegativity constrained least squares (NMF/ANLS) and the active set-based fast algorithm for nonnegativity constrained least squares with multiple right-hand side vectors, and we discuss its convergence properties and a rigorous convergence criterion based on the Karush-Kuhn-Tucker (KKT) conditions. In addition, we also describe algorithms for sparse NMFs and regularized NMF. We show how we impose a sparsity constraint on one of the factors by $L_1$-norm minimization and discuss its convergence properties. Our algorithms are compared to other commonly used NMF algorithms in the literature on several test data sets in terms of their convergence behavior.
Journal Article
Productivity Measurement through IMU-Based Detailed Activity Recognition Using Machine Learning: A Case Study of Masonry Work
Although measuring worker productivity is crucial, the measurement of the productivity of each worker is challenging due to their dispersion across various construction jobsites. This paper presents a framework for measuring productivity based on an inertial measurement unit (IMU) and activity classification. Two deep learning algorithms and three sensor combinations were utilized to identify and analyze the feasibility of the framework in masonry work. Using the proposed method, worker activity classification could be performed with a maximum accuracy of 96.70% using the convolutional neural network model with multiple sensors, and a minimum accuracy of 72.11% using the long short-term memory (LSTM) model with a single sensor. Productivity could be measured with an accuracy of up to 96.47%. The main contributions of this study are the proposal of a method for classifying detailed activities and an exploration of the effect of the number of IMU sensors used in measuring worker productivity.
Journal Article