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3. Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study

Author

Oh, Bumjo, Hwangbo, Suhyun, Jung, Taeyeong, Min, Kyungha, Lee, Chanhee, Apio, Catherine, Lee, Hyejin, Lee, Seungyeoun, Moon, Min Kyong, Kim, Shin-Woo, and Park, Taesung

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundLimited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. ObjectiveThis study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission. MethodsThis retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical. ResultsOut of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models. ConclusionsOur prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.

Published
2021
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4. Hierarchical Climate Control Strategy for Electric Vehicles with Door-Opening Consideration

Author

Nam, Sanghyeon, Lee, Hyejin, Kim, Youngki, Kwak, Kyoung hyun, and Han, Kyoungseok

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

This study proposes a novel climate control strategy for electric vehicles (EVs) by addressing door-opening interruptions, an overlooked aspect in EV thermal management. We create and validate an EV simulation model that incorporates door-opening scenarios. Three controllers are compared using the simulation model: (i) a hierarchical non-linear model predictive control (NMPC) with a unique coolant dividing layer and a component for cabin air inflow regulation based on door-opening signals; (ii) a single MPC controller; and (iii) a rule-based controller. The hierarchical controller outperforms, reducing door-opening temperature drops by 46.96% and 51.33% compared to single layer MPC and rule-based methods in the relevant section. Additionally, our strategy minimizes the maximum temperature gaps between the sections during recovery by 86.4% and 78.7%, surpassing single layer MPC and rule-based approaches, respectively. We believe that this result opens up future possibilities for incorporating the thermal comfort of passengers across all sections within the vehicle., Comment: This paper, intended for presentation at the IEEE Intelligent Vehicles Symposium (IV) 2024, comprises six pages and includes eight figures

Published
2024

5. Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean

Author

Choi, ChangSu, Jeong, Yongbin, Park, Seoyoon, Won, InHo, Lim, HyeonSeok, Kim, SangMin, Kang, Yejee, Yoon, Chanhyuk, Park, Jaewan, Lee, Yiseul, Lee, HyeJin, Hahm, Younggyun, Kim, Hansaem, and Lim, KyungTae

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large language models (LLMs) use pretraining to predict the subsequent word; however, their expansion requires significant computing resources. Numerous big tech companies and research institutes have developed multilingual LLMs (MLLMs) to meet current demands, overlooking less-resourced languages (LRLs). This study proposed three strategies to enhance the performance of LRLs based on the publicly available MLLMs. First, the MLLM vocabularies of LRLs were expanded to enhance expressiveness. Second, bilingual data were used for pretraining to align the high- and less-resourced languages. Third, a high-quality small-scale instruction dataset was constructed and instruction-tuning was performed to augment the LRL. The experiments employed the Llama2 model and Korean was used as the LRL, which was quantitatively evaluated against other developed LLMs across eight tasks. Furthermore, a qualitative assessment was performed based on human evaluation and GPT4. Experimental results showed that our proposed Bllossom model exhibited superior performance in qualitative analyses compared to previously proposed Korean monolingual models.

Published
2024

6. A Mobile-Based Comprehensive Weight Reduction Program for the Workplace (Health-On): Development and Pilot Study

Author

Han, Min Kyu, Cho, Belong, Kwon, Hyuktae, Son, Ki Young, Lee, Hyejin, Lee, Joo Kyung, and Park, Jinho

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundThere is a growing interest in mobile technology for obesity management. Despite the known effectiveness of workplace-based weight loss programs, there are few studies on mobile phone–delivered interventions. ObjectiveThis study aimed to develop and verify an integrated and personalized mobile technology–based weight control program, named Health-On, optimized for workplaces. MethodsA weight reduction algorithm was developed for calorie prescription, continuous monitoring, periodic feedback and reevaluation, goal resetting, and offline intervention with behavior-changing strategies. A total of 30 obese volunteers (body mass index ≥25 kg/m2) participated in the 12-week Health-On pilot program. The primary outcome was weight reduction, and secondary outcomes were improved anthropometric measures, metabolic profiles, and fat computed tomography measures, all assessed pre- and postintervention. ResultsHealth-On incorporated proprietary algorithms and several strategies intended to maximize adherence, using compatible online and offline interventions. The mean weight of 30 participants decreased by 5.8%, and median weight also decreased from 81.3 kg (interquartile range [IQR] 77.1-87.8) before intervention to 76.6 kg (IQR 70.8-79.5) after the 12-week intervention period (P

Published
2019
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14. Students' Perception on Immersive Learning through 2D and 3D Metaverse Platforms

Author

Hwang, Yohan, Shin, Dongkwang, and Lee, Hyejin

Abstract

The main purpose of this paper is to add empirical data to the nascent field of metaverse learning and teaching by examining factors affecting student participation and their perceived experiences of different metaverse platforms. For data collection, 57 Korean undergraduates participated in a self-administered questionnaire and a short reflective essay regarding their experiences on three metaverse platforms (ifland, Gather Town, & Frame VR). For data analysis, exploratory factor analysis was first executed to derive the underlying factors that can explain student participation in metaverse platforms. The social and interactive learning as well as individualized and behavioral learning were identified as two main contributing factors. While the three platforms had no statistical difference in terms of social presence, students' sentimentally perceived differences among them. The sentiment analysis shows that 60.00% of "ifland" users were positive, followed by 53.66% of "Frame VR" users and 51.22% of "Gather Town" users. Furthermore, the additional keyword analysis shows why students expressed the perceived experiences of each platform in a different way. Given that the success of metaverse instruction can be dependent upon whether students regard it as beneficial, such measurements of student perception on the effectiveness of learning on metaverse platforms can offer meaningful recommendations for tech-savvy educators.

Published
2023
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16. Prevalence and Severity of COVID-19 among Children and Adolescents with Autism Spectrum Disorders in the Republic of Korea

Author

Yun, Jieun, Kang, Beomjun, Lee, Jae-ryun, Lee, Hyejin, and Lee, Jin Yong

Abstract

Autism spectrum disorder is considered a vulnerability for many diseases including coronavirus disease 2019. This study investigated trends in coronavirus disease 2019 among children and adolescents with and without autism spectrum disorder and to evaluate whether there are differences in the prevalence, severity, and case fatality rate. We used data from the National Health Insurance Service for all people [greater than or equal to] 19 years of age. Among 9,187,211 children and adolescents [greater than or equal to] 19 years of age, 402,499 (4.4%) were coronavirus disease 2019-positive. Of the total population, 63,054 (0.7%) were diagnosed with autism spectrum disorder, among whom 2557 (4.1%) were coronavirus disease 2019-positive. The coronavirus disease 2019 prevalence was lower among children and adolescents with autism spectrum disorder, with 4055 per 100,000 versus 4383 per 100,000 without autism spectrum disorder (p < 0.001). However, children and adolescents with autism spectrum disorder exhibited a higher proportion of hospitalization (24.8% vs 21.5%) and severe disease (0.2% vs 0.01%) than those without autism spectrum disorder (p < 0.001); the length of hospital stay among inpatients was not different between the two groups (9.5 vs 9.4 days, respectively; p = 0.48). There were six deaths in total, with no deaths among children and adolescents with autism spectrum disorder. The quarantine policies have played a great role in sustaining low prevalence and higher hospitalization rates among children and adolescents with autism spectrum disorder.

Published
2023
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25. Appendix 1

Author

Lee, Hyejin, primary, Woo, Eun Jin, additional, Kim, Yongjun, additional, Waghmare, Pranjali, additional, Shinde, Vasant, additional, Jadhav, Nilesh, additional, Oh, Chang Seok, additional, Hong, Jong Ha, additional, and Shin, Dong Hoon, additional

Published
2023
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26. AI increases unethical consumer behavior due to reduced anticipatory guilt

Author

Kim, TaeWoo, Lee, Hyejin, Kim, Michelle Yoosun, Kim, SunAh, and Duhachek, Adam

Subjects
Artificial intelligence -- Usage -- Psychological aspects -- Educational aspects, Consumer behavior -- Ethical aspects, Guilt -- Ethical aspects, Artificial intelligence, Advertising, marketing and public relations, Business
Abstract

The current research focuses on examining how the use of artificial intelligence and robotic technology, emerging non-human agent innovations in service industries, influences consumers' likelihood of engaging in unethical behavior. Previous research has shown that non-human (vs. human) agents are perceived differently along many dimensions by consumers (e.g., that they lack emotional capability), leading to various behavioral changes when interacting with them. We hypothesize and show across four studies that interacting with non-human (vs. human) agents, such as AI and robots, increases the tendency to engage in unethical consumer behaviors due to reduced anticipatory feelings of guilt. We also demonstrate the moderating role of anthropomorphism such that endowing humanlike features on non-human agents reduces unethical behavior. We also rule out alternative explanations for the effect, including differential perceptions about the agents (e.g., 'warmth,' 'competence,' or 'detection capacity') and other measures associated with the company capabilities., Author(s): TaeWoo Kim [sup.1] , Hyejin Lee [sup.2] , Michelle Yoosun Kim [sup.3] , SunAh Kim [sup.4] , Adam Duhachek [sup.5] [sup.6] Author Affiliations: (1) grid.117476.2, 0000 0004 1936 7611, [...]

Published
2023
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32. Power Distance Belief and Consumer Purchase Avoidance: Exploring the Role of Cultural Factors in Retail Dynamics.

Author

Lee, Hyejin and Lalwani, Ashok K.

Subjects
CONSUMER behavior, RETAIL industry, AVOIDANCE (Psychology), CONSUMPTION (Economics), POWER (Social sciences), PROFIT & loss, CULTURE, CONSUMERS
Abstract

A common problem faced by contemporary retailers is consumers' tendency to avoid purchases that postpone or prevent cash flow for retailers, thus negatively affecting retailers' sales and profits. Little is known about the factors that drive consumers to avoid purchases or about marketing tactics that may reduce the tendency, especially from a cultural perspective. The authors attempt to fill this gap by exploring the role of an important cultural variable, namely, power distance belief (PDB), on consumers' tendency to avoid purchases. PDB is the extent to which people accept and endorse inequalities in society. A series of 14 studies (including 6 studies in the Web Appendix) using a variety of operationalizations of the key variables suggest that consumers high (vs. low) in PDB are less likely to avoid purchases (Studies 1a, 1b, and 1c) because they generally perceive greater constraints on their behavior (Study 2). These constraints are aversive, triggering the desire to overcome them and to have more as a compensatory mechanism, thereby reducing the tendency to avoid purchases. Accordingly, low (but not high) PDB consumers' tendency to forgo purchases is significantly decreased when they perceive greater constraints on their choices and decisions (Study 3) and when they experience a high social density (Study 4). However, high (but not low) PDB consumers' tendency to avoid purchases significantly increases when individuals perceive that constraints facilitate hierarchy (Study 5) or that constraints lead to positive outcomes (Study 6). Theoretical and managerial implications are discussed. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Highly-scalable stochastic neuron based on Ovonic Threshold Switch (OTS) and its applications in Restricted Boltzmann Machine (RBM)

Author

Im, Seong-il, Lee, Hyejin, Lee, Jaesang, Jeong, Jae-Seung, Kwak, Joon Young, Kim, Keunsu, Cho, Jeong Ho, Ju, Hyunsu, and Lee, Suyoun

Subjects
Physics - Applied Physics, Computer Science - Machine Learning
Abstract

Interest in Restricted Boltzmann Machine (RBM) is growing as a generative stochastic artificial neural network to implement a novel energy-efficient machine-learning (ML) technique. For a hardware implementation of the RBM, an essential building block is a reliable stochastic binary neuron device that generates random spikes following the Boltzmann distribution. Here, we propose a highly-scalable stochastic neuron device based on Ovonic Threshold Switch (OTS) which utilizes the random emission and capture process of traps as the source of stochasticity. The switching probability is well described by the Boltzmann distribution, which can be controlled by operating parameters. As a candidate for a true random number generator (TRNG), it passes 15 among the 16 tests of the National Institute of Standards and Technology (NIST) Statistical Test Suite (Special Publication 800-22). In addition, the recognition task of handwritten digits (MNIST) is demonstrated using a simulated RBM network consisting of the proposed device with a maximum recognition accuracy of 86.07 %. Furthermore, reconstruction of images is successfully demonstrated using images contaminated with noises, resulting in images with the noise removed. These results show the promising properties of OTS-based stochastic neuron devices for applications in RBM systems.

Published
2020

34. Frequency-tunable nano-oscillator based on Ovonic Threshold Switch (OTS)

Author

Kim, Seon Jeong, Cho, Seong Won, Lee, Hyejin, Lee, Jaesang, Seong, Tae Yeon, Kim, Inho, Park, Jong-Keuk, Kwak, Joon Young, Kim, Jaewook, Park, Jongkil, Jeong, YeonJoo, Hwang, Gyu Weon, Lee, Kyeong Seok, and Lee, Suyoun

Subjects
Condensed Matter - Materials Science
Abstract

Nano-oscillator devices are gaining more and more attention as a prerequisite for developing novel energy-efficient computing systems based on coupled oscillators. Here, we introduce a highly scalable, frequency-tunable nano-oscillator consisting of one Ovonic threshold switch (OTS) and a field-effect transistor (FET). It is presented that the proposed device shows an oscillating behavior with a natural frequency (f_{nat}) adjustable from 0.5 to 2 MHz depending on the gate voltage applied to the FET. In addition, under a small periodic input, it is observed that the oscillating frequency (f_{osc}) of the device is locked to the frequency (f_{in}) of the input when f_{in} ~ f_{nat}, demonstrating the so-called synchronization phenomenon. It also shows the phase lock of the combined oscillator network using circuit simulation, where the phase relation between the oscillators can be controlled by the coupling strength. These results imply that the proposed device is promising for applications in oscillator-based computing systems., Comment: 18 pages including 5 figures

Published
2020

40. Evaluating the comparative MT1B, MT1F, MT1G, and MT1H expression in human pulmonary alveolar epithelial cells treated with polyhexamethylene guanidine-phosphate, chloromethylisothiazolinone/methylisothiazolinone, oligo(2-(2-ethoxy)ethoxyethyl guanidinium chloride, benzalkonium chloride, and sodium dichloroisocyanurate

Author

Kim, Jaeyoung, Baek, Yong-Wook, Kim, Cherry, Nam, Yoon Jeong, Lee, Yu-Seon, Lee, Hong, Kang, Ja Young, Lee, Hyejin, Choi, Jin Young, Park, Yoon Hee, Park, Su A., Park, Eun-Kee, Jeong, Sang Hoon, and Lee, Ju-Han

Published
2023
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45. Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study.

Author

Burn, Edward, You, Seng Chan, Sena, Anthony G, Kostka, Kristin, Abedtash, Hamed, Abrahão, Maria Tereza F, Alberga, Amanda, Alghoul, Heba, Alser, Osaid, Alshammari, Thamir M, Aragon, Maria, Areia, Carlos, Banda, Juan M, Cho, Jaehyeong, Culhane, Aedin C, Davydov, Alexander, DeFalco, Frank J, Duarte-Salles, Talita, DuVall, Scott, Falconer, Thomas, Fernandez-Bertolin, Sergio, Gao, Weihua, Golozar, Asieh, Hardin, Jill, Hripcsak, George, Huser, Vojtech, Jeon, Hokyun, Jing, Yonghua, Jung, Chi Young, Kaas-Hansen, Benjamin Skov, Kaduk, Denys, Kent, Seamus, Kim, Yeesuk, Kolovos, Spyros, Lane, Jennifer CE, Lee, Hyejin, Lynch, Kristine E, Makadia, Rupa, Matheny, Michael E, Mehta, Paras P, Morales, Daniel R, Natarajan, Karthik, Nyberg, Fredrik, Ostropolets, Anna, Park, Rae Woong, Park, Jimyung, Posada, Jose D, Prats-Uribe, Albert, Rao, Gowtham, Reich, Christian, Rho, Yeunsook, Rijnbeek, Peter, Schilling, Lisa M, Schuemie, Martijn, Shah, Nigam H, Shoaibi, Azza, Song, Seokyoung, Spotnitz, Matthew, Suchard, Marc A, Swerdel, Joel N, Vizcaya, David, Volpe, Salvatore, Wen, Haini, Williams, Andrew E, Yimer, Belay B, Zhang, Lin, Zhuk, Oleg, Prieto-Alhambra, Daniel, and Ryan, Patrick

Subjects
Humans, Pneumonia, Viral, Coronavirus Infections, Hospitalization, Prevalence, Cohort Studies, Age Factors, Comorbidity, Sex Factors, Adult, Aged, Aged, 80 and over, Middle Aged, United States, Spain, Female, Male, Influenza, Human, Young Adult, Republic of Korea, Pandemics, COVID-19, and over, Influenza, Human, Pneumonia, Viral
Abstract

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

Published
2020

47. A saturated map of common genetic variants associated with human height

Author

Yengo, Loïc, Vedantam, Sailaja, Marouli, Eirini, Sidorenko, Julia, Bartell, Eric, Sakaue, Saori, Graff, Marielisa, Eliasen, Anders U., Jiang, Yunxuan, Raghavan, Sridharan, Miao, Jenkai, Arias, Joshua D., Graham, Sarah E., Mukamel, Ronen E., Spracklen, Cassandra N., Yin, Xianyong, Chen, Shyh-Huei, Ferreira, Teresa, Highland, Heather H., Ji, Yingjie, Karaderi, Tugce, Lin, Kuang, Lüll, Kreete, Malden, Deborah E., Medina-Gomez, Carolina, Machado, Moara, Moore, Amy, Rüeger, Sina, Sim, Xueling, Vrieze, Scott, Ahluwalia, Tarunveer S., Akiyama, Masato, Allison, Matthew A., Alvarez, Marcus, Andersen, Mette K., Ani, Alireza, Appadurai, Vivek, Arbeeva, Liubov, Bhaskar, Seema, Bielak, Lawrence F., Bollepalli, Sailalitha, Bonnycastle, Lori L., Bork-Jensen, Jette, Bradfield, Jonathan P., Bradford, Yuki, Braund, Peter S., Brody, Jennifer A., Burgdorf, Kristoffer S., Cade, Brian E., Cai, Hui, Cai, Qiuyin, Campbell, Archie, Cañadas-Garre, Marisa, Catamo, Eulalia, Chai, Jin-Fang, Chai, Xiaoran, Chang, Li-Ching, Chang, Yi-Cheng, Chen, Chien-Hsiun, Chesi, Alessandra, Choi, Seung Hoan, Chung, Ren-Hua, Cocca, Massimiliano, Concas, Maria Pina, Couture, Christian, Cuellar-Partida, Gabriel, Danning, Rebecca, Daw, E. Warwick, Degenhard, Frauke, Delgado, Graciela E., Delitala, Alessandro, Demirkan, Ayse, Deng, Xuan, Devineni, Poornima, Dietl, Alexander, Dimitriou, Maria, Dimitrov, Latchezar, Dorajoo, Rajkumar, Ekici, Arif B., Engmann, Jorgen E., Fairhurst-Hunter, Zammy, Farmaki, Aliki-Eleni, Faul, Jessica D., Fernandez-Lopez, Juan-Carlos, Forer, Lukas, Francescatto, Margherita, Freitag-Wolf, Sandra, Fuchsberger, Christian, Galesloot, Tessel E., Gao, Yan, Gao, Zishan, Geller, Frank, Giannakopoulou, Olga, Giulianini, Franco, Gjesing, Anette P., Goel, Anuj, Gordon, Scott D., Gorski, Mathias, Grove, Jakob, Guo, Xiuqing, Gustafsson, Stefan, Haessler, Jeffrey, Hansen, Thomas F., Havulinna, Aki S., Haworth, Simon J., He, Jing, Heard-Costa, Nancy, Hebbar, Prashantha, Hindy, George, Ho, Yuk-Lam A., Hofer, Edith, Holliday, Elizabeth, Horn, Katrin, Hornsby, Whitney E., Hottenga, Jouke-Jan, Huang, Hongyan, Huang, Jie, Huerta-Chagoya, Alicia, Huffman, Jennifer E., Hung, Yi-Jen, Huo, Shaofeng, Hwang, Mi Yeong, Iha, Hiroyuki, Ikeda, Daisuke D., Isono, Masato, Jackson, Anne U., Jäger, Susanne, Jansen, Iris E., Johansson, Ingegerd, Jonas, Jost B., Jonsson, Anna, Jørgensen, Torben, Kalafati, Ioanna-Panagiota, Kanai, Masahiro, Kanoni, Stavroula, Kårhus, Line L., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kawaguchi, Takahisa, Kember, Rachel L., Kentistou, Katherine A., Kim, Han-Na, Kim, Young Jin, Kleber, Marcus E., Knol, Maria J., Kurbasic, Azra, Lauzon, Marie, Le, Phuong, Lea, Rodney, Lee, Jong-Young, Leonard, Hampton L., Li, Shengchao A., Li, Xiaohui, Li, Xiaoyin, Liang, Jingjing, Lin, Honghuang, Lin, Shih-Yi, Liu, Jun, Liu, Xueping, Lo, Ken Sin, Long, Jirong, Lores-Motta, Laura, Luan, Jian’an, Lyssenko, Valeriya, Lyytikäinen, Leo-Pekka, Mahajan, Anubha, Mamakou, Vasiliki, Mangino, Massimo, Manichaikul, Ani, Marten, Jonathan, Mattheisen, Manuel, Mavarani, Laven, McDaid, Aaron F., Meidtner, Karina, Melendez, Tori L., Mercader, Josep M., Milaneschi, Yuri, Miller, Jason E., Millwood, Iona Y., Mishra, Pashupati P., Mitchell, Ruth E., Møllehave, Line T., Morgan, Anna, Mucha, Soeren, Munz, Matthias, Nakatochi, Masahiro, Nelson, Christopher P., Nethander, Maria, Nho, Chu Won, Nielsen, Aneta A., Nolte, Ilja M., Nongmaithem, Suraj S., Noordam, Raymond, Ntalla, Ioanna, Nutile, Teresa, Pandit, Anita, Christofidou, Paraskevi, Pärna, Katri, Pauper, Marc, Petersen, Eva R. B., Petersen, Liselotte V., Pitkänen, Niina, Polašek, Ozren, Poveda, Alaitz, Preuss, Michael H., Pyarajan, Saiju, Raffield, Laura M., Rakugi, Hiromi, Ramirez, Julia, Rasheed, Asif, Raven, Dennis, Rayner, Nigel W., Riveros, Carlos, Rohde, Rebecca, Ruggiero, Daniela, Ruotsalainen, Sanni E., Ryan, Kathleen A., Sabater-Lleal, Maria, Saxena, Richa, Scholz, Markus, Sendamarai, Anoop, Shen, Botong, Shi, Jingchunzi, Shin, Jae Hun, Sidore, Carlo, Sitlani, Colleen M., Slieker, Roderick C., Smit, Roelof A. J., Smith, Albert V., Smith, Jennifer A., Smyth, Laura J., Southam, Lorraine, Steinthorsdottir, Valgerdur, Sun, Liang, Takeuchi, Fumihiko, Tallapragada, Divya Sri Priyanka, Taylor, Kent D., Tayo, Bamidele O., Tcheandjieu, Catherine, Terzikhan, Natalie, Tesolin, Paola, Teumer, Alexander, Theusch, Elizabeth, Thompson, Deborah J., Thorleifsson, Gudmar, Timmers, Paul R. H. J., Trompet, Stella, Turman, Constance, Vaccargiu, Simona, van der Laan, Sander W., van der Most, Peter J., van Klinken, Jan B., van Setten, Jessica, Verma, Shefali S., Verweij, Niek, Veturi, Yogasudha, Wang, Carol A., Wang, Chaolong, Wang, Lihua, Wang, Zhe, Warren, Helen R., Bin Wei, Wen, Wickremasinghe, Ananda R., Wielscher, Matthias, Wiggins, Kerri L., Winsvold, Bendik S., Wong, Andrew, Wu, Yang, Wuttke, Matthias, Xia, Rui, Xie, Tian, Yamamoto, Ken, Yang, Jingyun, Yao, Jie, Young, Hannah, Yousri, Noha A., Yu, Lei, Zeng, Lingyao, Zhang, Weihua, Zhang, Xinyuan, Zhao, Jing-Hua, Zhao, Wei, Zhou, Wei, Zimmermann, Martina E., Zoledziewska, Magdalena, Adair, Linda S., Adams, Hieab H. H., Aguilar-Salinas, Carlos A., Al-Mulla, Fahd, Arnett, Donna K., Asselbergs, Folkert W., Åsvold, Bjørn Olav, Attia, John, Banas, Bernhard, Bandinelli, Stefania, Bennett, David A., Bergler, Tobias, Bharadwaj, Dwaipayan, Biino, Ginevra, Bisgaard, Hans, Boerwinkle, Eric, Böger, Carsten A., Bønnelykke, Klaus, Boomsma, Dorret I., Børglum, Anders D., Borja, Judith B., Bouchard, Claude, Bowden, Donald W., Brandslund, Ivan, Brumpton, Ben, Buring, Julie E., Caulfield, Mark J., Chambers, John C., Chandak, Giriraj R., Chanock, Stephen J., Chaturvedi, Nish, Chen, Yii-Der Ida, Chen, Zhengming, Cheng, Ching-Yu, Christophersen, Ingrid E., Ciullo, Marina, Cole, John W., Collins, Francis S., Cooper, Richard S., Cruz, Miguel, Cucca, Francesco, Cupples, L. Adrienne, Cutler, Michael J., Damrauer, Scott M., Dantoft, Thomas M., de Borst, Gert J., de Groot, Lisette C. P. G. M., De Jager, Philip L., de Kleijn, Dominique P. V., Janaka de Silva, H., Dedoussis, George V., den Hollander, Anneke I., Du, Shufa, Easton, Douglas F., Elders, Petra J. M., Eliassen, A. Heather, Ellinor, Patrick T., Elmståhl, Sölve, Erdmann, Jeanette, Evans, Michele K., Fatkin, Diane, Feenstra, Bjarke, Feitosa, Mary F., Ferrucci, Luigi, Ford, Ian, Fornage, Myriam, Franke, Andre, Franks, Paul W., Freedman, Barry I., Gasparini, Paolo, Gieger, Christian, Girotto, Giorgia, Goddard, Michael E., Golightly, Yvonne M., Gonzalez-Villalpando, Clicerio, Gordon-Larsen, Penny, Grallert, Harald, Grant, Struan F. A., Grarup, Niels, Griffiths, Lyn, Gudnason, Vilmundur, Haiman, Christopher, Hakonarson, Hakon, Hansen, Torben, Hartman, Catharina A., Hattersley, Andrew T., Hayward, Caroline, Heckbert, Susan R., Heng, Chew-Kiat, Hengstenberg, Christian, Hewitt, Alex W., Hishigaki, Haretsugu, Hoyng, Carel B., Huang, Paul L., Huang, Wei, Hunt, Steven C., Hveem, Kristian, Hyppönen, Elina, Iacono, William G., Ichihara, Sahoko, Ikram, M. Arfan, Isasi, Carmen R., Jackson, Rebecca D., Jarvelin, Marjo-Riitta, Jin, Zi-Bing, Jöckel, Karl-Heinz, Joshi, Peter K., Jousilahti, Pekka, Jukema, J. Wouter, Kähönen, Mika, Kamatani, Yoichiro, Kang, Kui Dong, Kaprio, Jaakko, Kardia, Sharon L. R., Karpe, Fredrik, Kato, Norihiro, Kee, Frank, Kessler, Thorsten, Khera, Amit V., Khor, Chiea Chuen, Kiemeney, Lambertus A. L. M., Kim, Bong-Jo, Kim, Eung Kweon, Kim, Hyung-Lae, Kirchhof, Paulus, Kivimaki, Mika, Koh, Woon-Puay, Koistinen, Heikki A., Kolovou, Genovefa D., Kooner, Jaspal S., Kooperberg, Charles, Köttgen, Anna, Kovacs, Peter, Kraaijeveld, Adriaan, Kraft, Peter, Krauss, Ronald M., Kumari, Meena, Kutalik, Zoltan, Laakso, Markku, Lange, Leslie A., Langenberg, Claudia, Launer, Lenore J., Le Marchand, Loic, Lee, Hyejin, Lee, Nanette R., Lehtimäki, Terho, Li, Huaixing, Li, Liming, Lieb, Wolfgang, Lin, Xu, Lind, Lars, Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Loeffler, Markus, London, Barry, Lubitz, Steven A., Lye, Stephen J., Mackey, David A., Mägi, Reedik, Magnusson, Patrik K. 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Published
2022
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49. Bridging the Domain Gap Towards Generalization in Automatic Colorization

Author

Lee, Hyejin, Kim, Daehee, Lee, Daeun, Kim, Jinkyu, Lee, Jaekoo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Avidan, Shai, editor, Brostow, Gabriel, editor, Cissé, Moustapha, editor, Farinella, Giovanni Maria, editor, and Hassner, Tal, editor

Published
2022
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