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pro vyhledávání: '"wang i"'
Publikováno v:
Harmonia: Journal of Arts Research and Education, Vol 23, Iss 2, Pp 223-234 (2023)
Although the Flow experience has been researched extensively in music education, there is limited investigation into exploring the link between the Suzuki piano method and Csikszentmihalyi’s flow theory. To address this gap, a quasi-experimental st
Externí odkaz:
https://doaj.org/article/bf5ac7b8692e45819c2805be99f2464d
Autor:
Li Nanxi, Ho Chong Pei, Wang I-Ting, Pitchappa Prakash, Fu Yuan Hsing, Zhu Yao, Lee Lennon Yao Ting
Publikováno v:
Nanophotonics, Vol 10, Iss 5, Pp 1437-1467 (2021)
With the emerging trend of big data and internet-of-things, sensors with compact size, low cost and robust performance are highly desirable. Spectral imaging and spectral LIDAR systems enable measurement of spectral and 3D information of the ambient
Externí odkaz:
https://doaj.org/article/e92432a705a74ae1a639a9fc273fdd67
Fundamental limits on the error probabilities of a family of decentralized detection algorithms (eg., the social learning rule proposed by Lalitha et al. over directed graphs are investigated. In decentralized detection, a network of nodes locally ex
Externí odkaz:
http://arxiv.org/abs/2409.00728
Artificial intelligence (AI) has revolutionized decision-making processes and systems throughout society and, in particular, has emerged as a significant technology in high-impact scenarios of national interest. Yet, despite AI's impressive predictiv
Externí odkaz:
http://arxiv.org/abs/2408.01301
Autor:
Hsu, Chia-Yu, Wang, I-Hsiang
Reliability of sequential hypothesis testing can be greatly improved when decision maker is given the freedom to adaptively take an action that determines the distribution of the current collected sample. Such advantage of sampling adaptivity has bee
Externí odkaz:
http://arxiv.org/abs/2405.06554
Autor:
Li, Ching-Fang, Wang, I-Hsiang
In the binary hypothesis testing problem, it is well known that sequentiality in taking samples eradicates the trade-off between two error exponents, yet implementing the optimal test requires the knowledge of the underlying distributions, say $P_0$
Externí odkaz:
http://arxiv.org/abs/2401.16213
Autor:
Johnson, Erik C., Robinson, Brian S., Vallabha, Gautam K., Joyce, Justin, Matelsky, Jordan K., Norman-Tenazas, Raphael, Western, Isaac, Villafañe-Delgado, Marisel, Cervantes, Martha, Robinette, Michael S., Reddy, Arun V., Kitchell, Lindsey, Rivlin, Patricia K., Reilly, Elizabeth P., Drenkow, Nathan, Roos, Matthew J., Wang, I-Jeng, Wester, Brock A., Gray-Roncal, William R., Hoffmann, Joan A.
Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large ne
Externí odkaz:
http://arxiv.org/abs/2305.17300
Autor:
Finocchio, Giovanni, Incorvia, Jean Anne C., Friedman, Joseph S., Yang, Qu, Giordano, Anna, Grollier, Julie, Yang, Hyunsoo, Ciubotaru, Florin, Chumak, Andrii, Naeemi, Azad J., Cotofana, Sorin D., Tomasello, Riccardo, Panagopoulos, Christos, Carpentieri, Mario, Lin, Peng, Pan, Gang, Yang, J. Joshua, Todri-Sanial, Aida, Boschetto, Gabriele, Makasheva, Kremena, Sangwan, Vinod K., Trivedi, Amit Ranjan, Hersam, Mark C., Camsari, Kerem Y., McMahon, Peter L., Datta, Supriyo, Koiller, Belita, Aguilar, Gabriel H., Temporão, Guilherme P., Rodrigues, Davi R., Sunada, Satoshi, Everschor-Sitte, Karin, Tatsumura, Kosuke, Goto, Hayato, Puliafito, Vito, Åkerman, Johan, Takesue, Hiroki, Di Ventra, Massimiliano, Pershin, Yuriy V., Mukhopadhyay, Saibal, Roy, Kaushik, Wang, I-Ting, Kang, Wang, Zhu, Yao, Kaushik, Brajesh Kumar, Hasler, Jennifer, Ganguly, Samiran, Ghosh, Avik W., Levy, William, Roychowdhury, Vwani, Bandyopadhyay, Supriyo
Publikováno v:
Nano Futures (2024)
In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in ener
Externí odkaz:
http://arxiv.org/abs/2301.06727
Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-making, where gains and losses are valued differently and outlying outcom
Externí odkaz:
http://arxiv.org/abs/2208.09106
Autor:
Liu Yao-Lung, Liu Jiung-Hsiun, Wang I-Kuan, Ju Shu-Woei, Yu Tung-Min, Chen I-Ru, Liu Yu-Ching, Huang Chung-Ming, Lin Shih-Yi, Chang Chiz-Tzung, Huang Chiu-Ching
Publikováno v:
BioMedicine, Vol 7, Iss 1, p 1 (2017)
Aims: Previous study on association between pro-inflammatory cytokines and mortality in PD population is limited. We aimed to investigate here. Methods: Total 50 patients who underwent incident PD were enrolled in this study. We measured the tite
Externí odkaz:
https://doaj.org/article/6439c7e5adcd42e4b860e59345649fc0