Search

Your search keyword '"Lee, Victor"' showing total 3,067 results
Start Over Author "Lee, Victor"
3,067 results on '"Lee, Victor"'

2. Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification

Author

Gu, Mengyang, Lin, Yizi, Lee, Victor Chang, and Qiu, Diana Y

Subjects
Applied Mathematics, Mathematical Sciences, Bayesian prior, Generative models, Dynamic mode decomposition, Forecast, Gaussian processes, Uncertainty quantification, Fluids & Plasmas, Applied mathematics, Mathematical physics, Numerical and computational mathematics
Published
2024

7. Scalp cooling therapy for chemotherapy-induced hair loss in patients with breast or gynecological cancers—an Asian tertiary institution experience

Author

Lee, Victor Guan Hui, Loh, Jerold, Hui, Fu, Sundar, Raghav, Tan, Belinda, Lee, Moy Chong, Lin, Hui Ying, Ong, Lay Ching, Visvanadan, Nisha, Ow, Samuel Guan Wei, Wong, Andrea Li Ann, Chan, Gloria Hui Jia, Lim, Siew Eng, Lim, Yi Wan, Tan, David Shao Peng, Ang, Yvonne, Choo, Joan, Lee, Matilda Xin Wei, Ngoi, Natalie Yan Li, Lee, Soo Chin, Paxman, Richard, Parker, Anna, Lee, Yee Mei, and Lim, Joline Si Jing

Published
2024
Full Text
View/download PDF

8. The BIAD Standards: Recommendations for Archaeological Data Publication and Insights From the Big Interdisciplinary Archaeological Database

Author

Reiter Samantha S., Staniuk Robert, Kolář Jan, Bulatović Jelena, Rose Helene Agerskov, Ryabogina Natalia E., Speciale Claudia, Schjerven Nicoline, Paulsson Bettina Schulz, Lee Victor Yan Kin, Canteri Elisabetta, Revill Alice, Dahlberg Fredrik, Sabatini Serena, Frei Karin M., Racimo Fernando, Ivanova-Bieg Maria, Traylor Wolfgang, Kate Emily J., Derenne Eve, Frank Lea, Woodbridge Jessie, Fyfe Ralph, Shennan Stephen, Kristiansen Kristian, Thomas Mark G., and Timpson Adrian

Subjects
publishing, fair, data longevity, “big data”, archaeology, Archaeology, CC1-960
Abstract

This article presents a series of recommendations for the publication of archaeological data, to improve their usability. These 12 recommendations were formulated by archaeological data experts who mined thousands of publications for different data types (including funerary practices, accelerator mass spectrometry dating, stable isotopes, zooarchaeology, archaeobotany and pathologies) during the initial construction of the Big Interdisciplinary Archaeological Database (BIAD). We also include data harmonisation vocabularies utilised for the integration of data from different recording systems. The case studies we cite to illustrate the recommendations are grounded in examples from the published literature and are presented in a problem/solution format. Though practically oriented towards the facilitation of efficient databasing, these recommendations – which we refer to as the BIAD Standards – are broadly applicable by those who want to extract scientific data from archaeological information, those who work with a specific region or theoretical focus and journal editors and manuscript authors. We anticipate that the use of the BIAD Standards will increase the usability, visibility, interoperability and longevity of published data and also increase the citations of those publications from which data were mined. The Standards will also help frame a unified foundation to support the continued integration of the natural sciences with archaeological research in the future.

Published
2024
Full Text
View/download PDF

10. Conclusion

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
Full Text
View/download PDF

17. See Through System

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
Full Text
View/download PDF

25. Background of IoV

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
Full Text
View/download PDF

26. State of the Art

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
Full Text
View/download PDF

27. Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification

Author

Gu, Mengyang, Lin, Yizi, Lee, Victor Chang, and Qiu, Diana

Subjects
Statistics - Methodology, Physics - Data Analysis, Statistics and Probability, Statistics - Applications
Abstract

Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the underlying process is unknown or too expensive to compute. Having reliable uncertainty assessment of the forecast enables tools to be deployed to predict new scenarios unobserved before. In this work, we first extend parallel partial Gaussian processes for predicting the vector-valued transition function that links the observations between the current and next time points, and quantify the uncertainty of predictions by posterior sampling. Second, we show the equivalence between the dynamic mode decomposition and the maximum likelihood estimator of the linear mapping matrix in the linear state space model. The connection provides a {probabilistic generative} model of dynamic mode decomposition and thus, uncertainty of predictions can be obtained. Furthermore, we draw close connections between different data-driven models for approximating nonlinear dynamics, through a unified view of generative models. We study two numerical examples, where the inputs of the dynamics are assumed to be known in the first example and the inputs are unknown in the second example. The examples indicate that uncertainty of forecast can be properly quantified, whereas model or input misspecification can degrade the accuracy of uncertainty quantification.

Published
2023
Full Text
View/download PDF

28. Exploring Measurement through Coding: Children's Conceptions of a Dynamic Linear Unit with Robot Coding Toys

Author

Welch, Lise E., Shumway, Jessica F., Clarke-Midura, Jody, and Lee, Victor R.

Abstract

Programming activities have the potential to provide a rich context for exploring measurement units in early elementary mathematics. This study examines how a small group of young children (ages 5-6) express their emergent conception of a dynamic linear unit and the measurement concepts they found challenging. Video of an introductory programming lesson was analyzed for evidence of preconceptions and conceptions of a dynamic linear unit. Using Artifact-Centric Activity Theory as a lens for the analysis, we found that social context, gesturing, and verbal descriptions influenced the children's understanding of a dynamic linear unit. Challenges that students encountered included developing a constructed conception of a unit, reconciling preconceptions about the meaning of a code, and socially-influenced preconceptions. This study furthers the exploration of computational thinking and mathematics connections and provides a basis for future exploration of dynamic mathematics and programming learning in early elementary education.

Published
2022

29. Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment

Author

Chen, Kecheng, Liu, Jie, Wan, Renjie, Lee, Victor Ho-Fun, Vardhanabhuti, Varut, Yan, Hong, and Li, Haoliang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning is widely used in this problem, but the performance of testing data (a.k.a. target domain) is often degraded in clinical scenarios due to the variations that were not encountered in training data (a.k.a. source domain). Unsupervised domain adaptation (UDA) of LDCT reconstruction has been proposed to solve this problem through distribution alignment. However, existing UDA methods fail to explore the usage of uncertainty quantification, which is crucial for reliable intelligent medical systems in clinical scenarios with unexpected variations. Moreover, existing direct alignment for different patients would lead to content mismatch issues. To address these issues, we propose to leverage a probabilistic reconstruction framework to conduct a joint discrepancy minimization between source and target domains in both the latent and image spaces. In the latent space, we devise a Bayesian uncertainty alignment to reduce the epistemic gap between the two domains. This approach reduces the uncertainty level of target domain data, making it more likely to render well-reconstructed results on target domains. In the image space, we propose a sharpness-aware distribution alignment to achieve a match of second-order information, which can ensure that the reconstructed images from the target domain have similar sharpness to normal-dose CT images from the source domain. Experimental results on two simulated datasets and one clinical low-dose imaging dataset show that our proposed method outperforms other methods in quantitative and visualized performance., Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems

Published
2023

30. Future Directions

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
Full Text
View/download PDF

32. Phase III KEYNOTE-789 Study of Pemetrexed and Platinum With or Without Pembrolizumab for Tyrosine Kinase Inhibitor[FIGURE DASH]Resistant, EGFR–Mutant, Metastatic Nonsquamous Non–Small Cell Lung Cancer

Author

Yang, James Chih-Hsin, Lee, Dae Ho, Lee, Jong-Seok, Fan, Yun, de Marinis, Filippo, Iwama, Eiji, Inoue, Takako, Rodríguez-Cid, Jerónimo, Zhang, Li, Yang, Cheng-Ta, de la Mora Jimenez, Emmanuel, Zhou, Jianying, Pérol, Maurice, Lee, Ki Hyeong, Vicente, David, Ichihara, Eiki, Riely, Gregory J., Luo, Yiwen, Chirovsky, Diana, Pietanza, M. Catherine, Bhagwati, Niyati, Lu, Shun, Joseph Boyer, Michael, Hui, Rina, Wong, Mark, Mant, Andrew, Parente, Phillip, John, Thomas, Parakh, Sagun, Castro, Gilberto, Jr, Werutsky, Gustavo, de Azevedo, Sergio Jobim, Andre Franke, Fabio, De Almeida Rego, Joilda Batista, Martins De Marchi, Pedro Rafael, Dix Junqueira, Gustavo, Maris Peria, Fernanda, Brust, Leandro, Cheema, Parneet, Doherty, Mark, Parmar, Ambika, Menjak, Ines B., Leighl, Natasha B., Agulnik, Jason, Lu, Shun, Han, Zhigang, Cui, Jiuwei, Zhang, Li, Cheng, Ying, Chen, Gongyan, Zhang, Helong, Yao, Yu, Hu, Chengping, Wang, Qiming, Zhang, Xin, Zhang, Yong, Zhou, Jianying, Ying, Kejing, Fan, Yun, Wang, Yan, Wang, Ziping, Feng, Jifeng, Du, YingYing, Wu, Lin, Huang, Cheng, Zhou, Xiangdong, Perol, Maurice, Domont, Julien, Lamour, Corinne, Dutilh, Julien, Oulkhouir, Youssef, Westeel, Virginie, Carmier, Delphine, Coudert, Bruno, Lagrange, Aurelie, Spaeth, Dominique, Ropert, Stanislas, Christoph, Daniel C., Kern, Jens, Kopp, Hans-Georg, Griesinger, Frank, Wiewrodt, Rainer, Wermke, Martin, Wesseler, Claas, Mueller, Annette, Vogel, Gunther, Lee, Victor, James Ho, Chung Man, Oscar Chan, Siu Hong, Hung Lo, Sing, Jonathan Nyaw, Shi Feng, Jacky Li, Yu Chung, Bar, Jair, Gottfried, Maya, Dudnik, Julia, Zer, Alona, Moskovitz, Mor, Wollner, Mirjana, Rotem, Ofer, Shamai, Sivan, Asna, Noam, Kamel, Mhameed, Novello, Silvia, Di Costanzo, Francesco, Doni, Laura, Mazzoni, Francesca, Ferrau, Francesco, de Marinis, Filippo, Tonini, Giuseppe, Galetta, Domenico, Piantedosi, Francovito, Vitiello, Fabiana, Kirita, Keisuke, Yoh, Kiyotaka, Takahashi, Toshiaki, Ohe, Yuichiro, Hattori, Yoshihiro, Okamoto, Isamu, Kurata, Takayasu, Yoshioka, Hiroshige, Saka, Hideo, Oki, Masahide, Kato, Terufumi, Tanaka, Hiroshi, Kumagai, Toru, Inoue, Takako, Hida, Toyoaki, Horio, Yoshitsugu, Teraoka, Shunsuke, Ichihara, Eiki, Kishi, Kazuma, Takaya, Hisashi, Takai, Daiya, Kozuki, Toshiyuki, Kasahara, Kazuo, Tambo, Yuichi, Hosomi, Yukio, Kondo, Masashi, Ichiki, Masao, Takeoka, Hiroaki, de la Mora Jimenez, Emmanuel, Hernandez, Carlos Alberto, Rodriguez Cid, Jeronimo Rafael, Rodriguez, Oscar Gerardo Arrieta, Han, Ji-Youn, Min, Young Joo, Kim, Dong-Wan, Park, Keunchil, Lee, Se-Hoon, Lee, Ki Hyeong, Lee, Jong-Seok, Kang, Jin Hyoung, Lee, Dae Ho, Cho, Eun Kyung, Carcereny, Enric, Lopez, Pilar Garrido, Tarruella, Margarita Majem, Rodriguez, Luis Paz-Ares, Vicente Baz, David, Font, Enriqueta Felip, Cobo-Dols, Manuel, Ekman, Simon, Bergman, Bengt, Ohman, Ronny, Vikstrom, Anders, Yang, Chih-Hsin, Chiu, Chao-Hua, Huang, Hsu-Ching, Yang, Cheng-Ta, Su, Jian, Chang, Gee-Chen, Yang, Tsung-Ying, Hsia, Te-Chun, Su, Wu-Chou, Wu, Shang-Yin, Wang, Chin-Chou, Lee, Kang-Yun, Lin, Sheng-Hao, Lin, Chih-Bin, Lee, Jih-Hsiang, Huang, Chun-Yao, Ahmed, Samreen, Newsom-Davis, Thomas, Baijal, Shobhit, Brock, Juliet, Zaki, Kam, Shamash, Jonathan, Papadatos-Pastos, Dionysis, Jain, Pooja, Jane Mackean, Melanie, Kendall, Stephan DiSean, Anderson, Ian, Costin, Dan, Hall, Richard, Campbell, Nicholas, Khan, Saad, Dowell, Jonathan, Mashru, Sandeep, Menon, Smitha, Raza, Ahmad, Ge, Li, Riely, Gregory J., Seetharamu, Nagashree, Stampleman, Laura, Subramanian, Janakiraman, Wender, Donald B., Natale, Ronald B., Zhu, Viola, Ignatius Ou, Sai-Hong, Sanborn, Rachel, and Evangelist, Makenzi C.

Published
2024
Full Text
View/download PDF

33. Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Author

Shi, Jianxin, Shiraishi, Kouya, Choi, Jiyeon, Matsuo, Keitaro, Chen, Tzu-Yu, Dai, Juncheng, Hung, Rayjean J, Chen, Kexin, Shu, Xiao-Ou, Kim, Young Tae, Landi, Maria Teresa, Lin, Dongxin, Zheng, Wei, Yin, Zhihua, Zhou, Baosen, Song, Bao, Wang, Jiucun, Seow, Wei Jie, Song, Lei, Chang, I-Shou, Hu, Wei, Chien, Li-Hsin, Cai, Qiuyin, Hong, Yun-Chul, Kim, Hee Nam, Wu, Yi-Long, Wong, Maria Pik, Richardson, Brian Douglas, Funderburk, Karen M, Li, Shilan, Zhang, Tongwu, Breeze, Charles, Wang, Zhaoming, Blechter, Batel, Bassig, Bryan A, Kim, Jin Hee, Albanes, Demetrius, Wong, Jason YY, Shin, Min-Ho, Chung, Lap Ping, Yang, Yang, An, She-Juan, Zheng, Hong, Yatabe, Yasushi, Zhang, Xu-Chao, Kim, Young-Chul, Caporaso, Neil E, Chang, Jiang, Ho, James Chung Man, Kubo, Michiaki, Daigo, Yataro, Song, Minsun, Momozawa, Yukihide, Kamatani, Yoichiro, Kobayashi, Masashi, Okubo, Kenichi, Honda, Takayuki, Hosgood, Dean H, Kunitoh, Hideo, Patel, Harsh, Watanabe, Shun-ichi, Miyagi, Yohei, Nakayama, Haruhiko, Matsumoto, Shingo, Horinouchi, Hidehito, Tsuboi, Masahiro, Hamamoto, Ryuji, Goto, Koichi, Ohe, Yuichiro, Takahashi, Atsushi, Goto, Akiteru, Minamiya, Yoshihiro, Hara, Megumi, Nishida, Yuichiro, Takeuchi, Kenji, Wakai, Kenji, Matsuda, Koichi, Murakami, Yoshinori, Shimizu, Kimihiro, Suzuki, Hiroyuki, Saito, Motonobu, Ohtaki, Yoichi, Tanaka, Kazumi, Wu, Tangchun, Wei, Fusheng, Dai, Hongji, Machiela, Mitchell J, Su, Jian, Kim, Yeul Hong, Oh, In-Jae, Lee, Victor Ho Fun, Chang, Gee-Chen, Tsai, Ying-Huang, Chen, Kuan-Yu, Huang, Ming-Shyan, Su, Wu-Chou, Chen, Yuh-Min, Seow, Adeline, Park, Jae Yong, and Kweon, Sun-Seog

Subjects
Genetics, Cancer, Clinical Research, Human Genome, Prevention, Lung, Lung Cancer, Tobacco, Tobacco Smoke and Health, 2.1 Biological and endogenous factors, Aetiology, Respiratory, Humans, Genome-Wide Association Study, Genetic Predisposition to Disease, Adenocarcinoma of Lung, Asia, Eastern, Lung Neoplasms, Polymorphism, Single Nucleotide
Abstract

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

Published
2023

35. Alternately Optimized Graph Neural Networks

Author

Han, Haoyu, Liu, Xiaorui, Mao, Haitao, Torkamani, MohamadAli, Shi, Feng, Lee, Victor, and Tang, Jiliang

Subjects
Computer Science - Machine Learning
Abstract

Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that can be viewed as tackling a bi-level optimization problem. This process is often inefficient in computation and memory usage. In this work, we propose a new optimization framework for semi-supervised learning on graphs. The proposed framework can be conveniently solved by the alternating optimization algorithms, resulting in significantly improved efficiency. Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.

Published
2022

39. ports athletes’ group sensemaking of team gameplay data analytics

Author

Poole, Frederick J. and Lee, Victor R.

Subjects
esports, sensemaking, data visualization, quantified self
Abstract

In this exploratory case study, we investigate how a collegiate esports team makes sense of gameplay data visualizations. Through our intervention we introduced the team to new data collection practices, provided data analysis and visualization support, and organized sensemaking sessions with the team to discuss implications of the analytics. Through an exploratory analysis of video footage, we identified three different sensemaking activities.

Published
2022

40. Learning Sciences and Learning Engineering: A Natural or Artificial Distinction?

Author

Lee, Victor R.

Abstract

"Learning engineering" has gained popularity as a term connected to the work of learning sciences. However, the nature of that connection is not entirely clear. For some, learning engineering represents distinct, industry-inspired practices enabled by data abundance and digital platformization of learning technologies. That view is presented as one where learning engineers apply learning research that has resided in experimental studies. For others, learning engineering should refer to the use of the full breadth of knowledge developed within the learning sciences research community. This second view is more inclusive of the fundamentally situated, design-oriented, and real-world commitments that are the backbone of the learning sciences, as reflected in this journal. The two views differ even as far as whether the academic field is labeled "learning science" or "learning sciences". This article examines and articulates these differences. It also argues that without course correction, many who identify with learning engineering will conduct technology-supported learning improvement work that, at its own risk, will neglect the full and necessary scope of what has already been and continues to be discovered in the learning sciences. Moreover, it behooves all to consider recently elevated, but deeply fundamental questions being asked in the learning sciences about what is important to learn and toward what ends. With some more clarity around what is actually encompassed by the learning sciences and how all interested in design and educational improvement can build upon that knowledge, we can make greater collective progress to understanding and supporting human learning.

Published
2023
Full Text
View/download PDF

41. Kindergarten Students' Mathematics Knowledge at Work: The Mathematics for Programming Robot Toys

Author

Shumway, Jessica F., Welch, Lise E., Kozlowski, Joseph S., Clarke-Midura, Jody, and Lee, Victor R.

Abstract

The purpose of this study was to explore how kindergarten students (aged 5-6 years) engaged with mathematics as they learned programming with robot coding toys. We video-recorded 16 teaching sessions of kindergarten students' (N = 36) mathematical and programming activities. Students worked in small groups (4-5 students) with robot coding toys on the floor in their classrooms, solving tasks that involved programming these toys to move to various locations on a grid. Drawing on a semiotic mediation perspective, we analyzed video data to identify the mathematics concepts and skills students demonstrated and the overlapping mathematics-programming knowledge exhibited by the students during these programming tasks. We found that kindergarten children used spatial, measurement, and number knowledge, and the design of the tasks, affordances of the robots, and types of programming knowledge influenced how the students engaged with mathematics. The paper concludes with a discussion about the intersections of mathematics and programming knowledge in early childhood, and how programming robot toys elicited opportunities for students to engage with mathematics in dynamic and interconnected ways, thus creating an entry point to reassert mathematics beyond the traditional school content and curriculum.

Published
2023
Full Text
View/download PDF

46. State of the Art

Author

Liu, Kai, primary, Dai, Penglin, additional, Lee, Victor C. S., additional, Ng, Joseph Kee-Yin, additional, and Son, Sang Hyuk, additional

Published
2023
Full Text
View/download PDF

48. Conclusion

Author

Liu, Kai, primary, Dai, Penglin, additional, Lee, Victor C. S., additional, Ng, Joseph Kee-Yin, additional, and Son, Sang Hyuk, additional

Published
2023
Full Text
View/download PDF

49. See Through System

Author

Liu, Kai, primary, Dai, Penglin, additional, Lee, Victor C. S., additional, Ng, Joseph Kee-Yin, additional, and Son, Sang Hyuk, additional

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results