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38 results on '"Chang, Fu-Chieh"'

1. RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues

Author

Kuo, Tzu-Lin, Liao, Feng-Ting, Hsieh, Mu-Wei, Chang, Fu-Chieh, Hsu, Po-Chun, and Shiu, Da-Shan

Subjects
Computer Science - Computation and Language
Abstract

In real-world applications with Large Language Models (LLMs), external retrieval mechanisms - such as Search-Augmented Generation (SAG), tool utilization, and Retrieval-Augmented Generation (RAG) - are often employed to enhance the quality of augmented generations in dialogues. These approaches often come with multi-turn dialogue, where each interaction is enriched by relevant information retrieved from external sources. Existing benchmarks either assess LLMs' chat abilities in multi-turn dialogues or their use of retrieval for augmented responses in single-turn settings. However, there is a gap in evaluating LLMs' ability to leverage retrieval for more precise responses across multiple turns. To address this limitation, we introduce RAD-Bench (Retrieval Augmented Dialogue), a benchmark designed to evaluate LLMs' capabilities in multi-turn dialogues following retrievals, essential for their deployment in context-rich applications. RAD-Bench evaluates two key abilities of LLMs: Retrieval Synthesis and Retrieval Reasoning. These are measured using discriminative questions and retrieved contexts, and corresponding reference answers, assessing how effectively LLMs integrate and reason with context to maintain and enhance conversation quality over multiple turns. Our evaluation results on commonly used LLMs reveal that model performance deteriorates as additional layers of conditions or constraints are applied across conversation turns, even when accurate retrieved contexts are provided.

Published
2024

2. Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning

Author

Lai, Yen-Ru, Chang, Fu-Chieh, and Wu, Pei-Yuan

Subjects
Computer Science - Machine Learning
Abstract

Offline reinforcement learning (RL) learns policies from a fixed dataset, but often requires large amounts of data. The challenge arises when labeled datasets are expensive, especially when rewards have to be provided by human labelers for large datasets. In contrast, unlabelled data tends to be less expensive. This situation highlights the importance of finding effective ways to use unlabelled data in offline RL, especially when labelled data is limited or expensive to obtain. In this paper, we present the algorithm to utilize the unlabeled data in the offline RL method with kernel function approximation and give the theoretical guarantee. We present various eigenvalue decay conditions of $\mathcal{H}_k$ which determine the complexity of the algorithm. In summary, our work provides a promising approach for exploiting the advantages offered by unlabeled data in offline RL, whilst maintaining theoretical assurances.

Published
2024

3. CAPM: Fast and Robust Verification on Maxpool-based CNN via Dual Network

Author

Bai, Jia-Hau, Liu, Chi-Ting, Wang, Yu, Chang, Fu-Chieh, and Wu, Pei-Yuan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

This study uses CAPM (Convex Adversarial Polytope for Maxpool-based CNN) to improve the verified bound for general purpose maxpool-based convolutional neural networks (CNNs) under bounded norm adversarial perturbations. The maxpool function is decomposed as a series of ReLU functions to extend the convex relaxation technique to maxpool functions, by which the verified bound can be efficiently computed through a dual network. The experimental results demonstrate that this technique allows the state-of-the-art verification precision for maxpool-based CNNs and involves a much lower computational cost than current verification methods, such as DeepZ, DeepPoly and PRIMA. This method is also applicable to large-scale CNNs, which previous studies show to be often computationally prohibitively expensive. Under certain circumstances, CAPM is 40-times, 20-times or twice as fast and give a significantly higher verification bound (CAPM 98% vs. PRIMA 76%/DeepPoly 73%/DeepZ 8%) as compared to PRIMA/DeepPoly/DeepZ. Furthermore, we additionally present the time complexity of our algorithm as $O(W^2NK)$, where $W$ is the maximum width of the neural network, $N$ is the number of neurons, and $K$ is the size of the maxpool layer's kernel.

Published
2024

4. Sample Complexity of Kernel-Based Q-Learning

Author

Yeh, Sing-Yuan, Chang, Fu-Chieh, Yueh, Chang-Wei, Wu, Pei-Yuan, Bernacchia, Alberto, and Vakili, Sattar

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Modern reinforcement learning (RL) often faces an enormous state-action space. Existing analytical results are typically for settings with a small number of state-actions, or simple models such as linearly modeled Q-functions. To derive statistically efficient RL policies handling large state-action spaces, with more general Q-functions, some recent works have considered nonlinear function approximation using kernel ridge regression. In this work, we derive sample complexities for kernel based Q-learning when a generative model exists. We propose a nonparametric Q-learning algorithm which finds an $\epsilon$-optimal policy in an arbitrarily large scale discounted MDP. The sample complexity of the proposed algorithm is order optimal with respect to $\epsilon$ and the complexity of the kernel (in terms of its information gain). To the best of our knowledge, this is the first result showing a finite sample complexity under such a general model.

Published
2023

5. Flexible Multiple-Objective Reinforcement Learning for Chip Placement

Author

Chang, Fu-Chieh, Tseng, Yu-Wei, Yu, Ya-Wen, Lee, Ssu-Rui, Cioba, Alexandru, Tseng, I-Lun, Shiu, Da-shan, Hsu, Jhih-Wei, Wang, Cheng-Yuan, Yang, Chien-Yi, Wang, Ren-Chu, Chang, Yao-Wen, Chen, Tai-Chen, and Chen, Tung-Chieh

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, congestion, and timing) are fixed during pretraining. However, fixed-weighed models cannot generate the diversity of placements required for engineers to accommodate changing requirements as they arise. This paper proposes flexible multiple-objective reinforcement learning (MORL) to support objective functions with inference-time variable weights using just a single pretrained model. Our macro placement results show that MORL can generate the Pareto frontier of multiple objectives effectively., Comment: A short version of this article is published in DAC'22:LBR (see ACM DOI 10.1145/3489517.3530617)

Published
2022

6. G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data

Author

Chang, Fu-Chieh, Wang, Hao-Jen, Chou, Chun-Nan, and Chang, Edward Y.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications. First, GANs may generate more labeled training data, which may help improve classification accuracy. Second, in scenarios where real data cannot be released outside certain premises for privacy and/or security reasons, using GAN- synthetic data to conduct training is a plausible alternative. This paper proposes a generalization bound to guarantee the generalization capability of a classifier learning from GAN-synthetic data. This generalization bound helps developers gauge the generalization gap between learning from synthetic data and testing on real data, and can therefore provide the clues to improve the generalization capability.

Published
2019

7. Representation Learning on Large and Small Data

Author

Chou, Chun-Nan, Shie, Chuen-Kai, Chang, Fu-Chieh, Chang, Jocelyn, and Chang, Edward Y.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning owes its success to three key factors: scale of data, enhanced models to learn representations from data, and scale of computation. This book chapter presented the importance of the data-driven approach to learn good representations from both big data and small data. In terms of big data, it has been widely accepted in the research community that the more data the better for both representation and classification improvement. The question is then how to learn representations from big data, and how to perform representation learning when data is scarce. We addressed the first question by presenting CNN model enhancements in the aspects of representation, optimization, and generalization. To address the small data challenge, we showed transfer representation learning to be effective. Transfer representation learning transfers the learned representation from a source domain where abundant training data is available to a target domain where training data is scarce. Transfer representation learning gave the OM and melanoma diagnosis modules of our XPRIZE Tricorder device (which finished $2^{nd}$ out of $310$ competing teams) a significant boost in diagnosis accuracy., Comment: Book chapter

Published
2017

10. The Deep Hybrid Neural Network and an Application on Polyp Detection.

Author

Wu, Yi-Ta, Shih, Frank Y., Wang, Cheng-Long, Hsiao, Kuang-Ting, Liu, You-Cheng, Chang, Fu-Chieh, and Yu, En-Da

Subjects
CONVOLUTIONAL neural networks, MATHEMATICAL convolutions, POLYPS, MATHEMATICAL morphology, COMPUTATIONAL complexity
Abstract

Mathematical morphology and convolution operators are two different methods to extract the characteristics and structures of images. Over the past decades, Deep Convolutional Neural Networks (DCNN) have been proven to be more powerful than traditional image-processing approaches. In this paper, we propose a novel structure called Deep Hybrid Neural Network (DHNN) by taking advantage of the convolution and morphological neural layers. Its practical application to polyp detection in medical images is illustrated. For experimental completeness, we adopt nine polyp image datasets, including publicly available data and our own collected data. For performance comparisons, we select three backbone models. Experimental results show that our DHNN achieves the best performance in comparisons in terms of computational complexity and accurate performance. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Flexible chip placement via reinforcement learning

Author

Chang, Fu-Chieh, primary, Tseng, Yu-Wei, additional, Yu, Ya-Wen, additional, Lee, Ssu-Rui, additional, Cioba, Alexandru, additional, Tseng, I-Lun, additional, Shiu, Da-shan, additional, Hsu, Jhih-Wei, additional, Wang, Cheng-Yuan, additional, Yang, Chien-Yi, additional, Wang, Ren-Chu, additional, Chang, Yao-Wen, additional, Chen, Tai-Chen, additional, and Chen, Tung-Chieh, additional

Published
2022
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16. Aminoglycoside-mimicking carbonized polymer dots for bacteremia treatment

Author

Chiou, Yi-Ru, primary, Lin, Chin-Jung, additional, Harroun, Scott G., additional, Chen, Yi-Ru, additional, Chang, Lung, additional, Wu, An-Tai, additional, Chang, Fu-Chieh, additional, Lin, Yang-Wei, additional, Lin, Han-Jia, additional, Anand, Anisha, additional, Unnikrishnan, Binesh, additional, Nain, Amit, additional, and Huang, Chih-Ching, additional

Published
2022
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17. Catalytic and photoresponsive BiZ/CuxS heterojunctions with surface vacancies for the treatment of multidrug-resistant clinical biofilm-associated infections

Author

Nain, Amit, primary, Huang, Hao-Hsin, additional, Chevrier, Daniel M., additional, Tseng, Yu-Ting, additional, Sangili, Arumugam, additional, Lin, Yu-Feng, additional, Huang, Yu-Fen, additional, Chang, Lung, additional, Chang, Fu-Chieh, additional, Huang, Chih-Ching, additional, Tseng, Fan-Gang, additional, and Chang, Huan-Tsung, additional

Published
2021
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23. A novel 3-TFT voltage driving method of compensating V shift for a-Si:H TFT and OLED degradation for AMOLED

Author

Chih-Lung Lin, Chia-Che Hung, Chang Fu-Chieh, and Kuan-Wen Chou

Subjects
Brightness, Materials science, business.industry, Condensed Matter Physics, Luminance, Electronic, Optical and Magnetic Materials, AMOLED, Thin-film transistor, Materials Chemistry, OLED, Optoelectronics, Electrical and Electronic Engineering, Current (fluid), business, Voltage, Degradation (telecommunications)
Abstract

This work demonstrates the feasibility of a novel pixel circuit by using three a-Si:H TFTs. The proposed circuit can stabilize the OLED current and provide an additional driving current to ameliorate the brightness degradation of the AMOLED. Measurement results indicate that the current degradation of the proposed circuit, caused by V TH variations, is less than 5% over more than 50,000 s at 60 °C, whereas that of a conventional 2T1C pixel circuit is larger than 34%. Furthermore, to ameliorate the decrease in luminance owing to the OLED degradation, the OLED current can be increased by 10% by analyzing the current degradation and modulating the detected voltage appropriately.

Published
2011

35. Abstracts from the 8th International Congress of the Asia Pacific Society of Infection Control (APSIC)

Author

Sutthiruk, Nantanit, Botti, Mari, Considine, Julie, Driscoll, Andrea, Hutchinson, Ana, Malathum, Kumthorn, Cucunawangsih, Cucunawangsih, Wiwing, Veronica, Puspitasari, Vivien, Shanmugakani, Rathina Kumar, Akeda, Yukihiro, Kodera, Takuya, Santanirand, Pitak, Tomono, Kazunori, Yamanaka, Takayuki, Moriuchi, Hiroyuki, Kitajima, Hiroyuki, Horikoshi, Yuho, Lavrinenko, Alyona, Azizov, Ilya, Tabriz, Nurlan, Kozhamuratov, Margulan, Serbo, Yekatherine, Yang, Dahae, Lee, Woonhyoung, Bae, Il Kwon, Lee, Jae Hyun, Lee, Hyukmin, Kim, Jung Ok, Jeong, Seok Hoon, Lee, Kyungwon, Peremalo, Thiba, Madhavan, Priya, Hamzah, Sharina, Than, Leslie, Wong, Eng Hwa, Desa, Mohd Nasir Mohd, Ng, Kee Peng, Geronimo, Marionne, Tayzon, Maria Fe, Maño, Maria Jesusa, Chow, Angela, Hon, Pei-Yun, Win, Mar-Kyaw, Ang, Brenda, Leo, Yee-Sin, See, Tina, Marin, Rocio Alvarez, de Sousa, Marta Aires, Kieffer, Nicolas, Nordmann, Patrice, Poirel, Laurent, Laochareonsuk, Wison, Petyu, Sireekul, Wanasitchaiwat, Pawin, Thana, Sutasinee, Bunyaphongphan, Chollathip, Boonsomsuk, Woranan, Maneepongpermpoon, Pakpoom, Jamulitrat, Silom, Sureshkumar, Dorairajan, Supraja, Kalyanaraman, Sharmila, Soundararajan, Setiawan, Benny, Lumbuun, Nicolaski, Nakayama, Haruo, Ota, Toshiko, Shirane, Naoko, Matuoka, Chikako, Kodama, Kentaro, Ohtsuka, Masanobu, Bacolcol, Silverose Ann Andales, Velmonte, Melecia, Alde, Allan, Chavez, Keithleen, Esteban, Arlene Joy, Lee, Aisa Jensen, Hsieh, Tai-Chin, Shio-ShinJean, Huang, Huey-Jen, Huang, Shu-Ju, Huang, Yu-Huan, Cheng, Pei-Chen, Yu, Su-Fang, Tsao, Shih-Ming, Lee, Yuan-Ti, Li, Chien-Feng, Lu, Min-Chi, Pruetpongpun, Nattapol, Khawcharoenporn, Thana, Damronglerd, Pansachee, Suwantarat, Nuntra, Apisarnthanarak, Anucha, Rutjanawech, Sasinuch, Cushinotto, Lisa, McBride, Patty, Williams, Harding, Liu, Hans, Hang, Phan Thi, Anh, Dinh Pham Phuong, Le, Ngai, Khu, Dung, Nguyen, Lam, Castillo, Roel Beltran, Gopalakrishnan, Ram, Ramasubramanian, Venkatasubramanian, Sreevidya, Subramanian, Jayapradha, Ranganathan, Umetsu, Atsushi, Noda, Tetsuhiro, Hashimoto, Kenyuu, Hayashi, Akihiro, Kabashima, Mikie, Jadczak, Ursula, Elvelund, Knut, Johnsen, Marit, Borgen, Bente, Lingaas, Egil, Mao, Chia-Hua, Chang, Fu-Chieh, Liu, Chang-Pan, Chao, Ru-Hui, Chang, Fu-chieh, Liu, Chang-pan, Pawapotako, Junpen, Prasertpan, Chadanan, Malaihuan, Wantanee, Uirungroj, Phisit, Saenjum, Chalermpong, Ouirungrog, Teerapat, Borrell, Sue, Bass, Pauline, Worth, Leon, Xian-li, Zhao, Xiao-long, Li, Xue-hua, Yao, Wei, Ren, Zeng, Zhang Xia, Kong, Man Ying, Lai, Christopher Koon Chi, Lee, Suet Yi, Tsang, Ngai Chong, O’Donoghue, M. M., Boost, M. V., Suen, L. K. P., Siu, G. K., Mui, K. W., Lai, C. K. C., Tsang, D. N. C., Sato, Yuka, Tateishi, Mariko, Mihashi, Mutsuko, Flor, Jose Paulo, Bautista, Marko, De Roxas, V. Jay, Vergara, Justine, Añonuevo, Nicolo Andrei, Kwek, Marion, Acuin, Jose, Sanchez, Anna Josea, Bathan, Avel, Jantan, Jamilah Binte, Guek, Chua Chor, Kian, Eu Chiow, Pirido, Pampe Anak, Aron, Nur Fadilah Binte Mohd, Estacio, Leah May, Palana, Francis Alvarez, Gracia, Michelle, Shamsuddin, Nur Syafiqah Binte, Castro, Kersten Timbad, Baloria, Madonna, Adam, Faezah Binte, Wei, Zhang, Fong, Poh Bee, Kalisvar, Marimuthu, Chuang, I-Ju, Yi-ChunCho, Chiu, Yu-Fen, Chen, Lung-Chih, Lin, Yi-Chun, Dong, Shao-Xing, Lee, Yi-Chieh, Kuan, Hui-Chen, Lin, Hsin-Hua, Chi, Chia-Chun, Lu, Chin-Te, Ya-Fen, Tang, Li-Hsiang, Su, Jien-Wei, Liu, Chao, Hsuehlan, ChangChien, PinRu, Chen, WeiFang, Lai, ChungHsu, Ara, Lutfe, Mowla, Syed Mohammad Niaz, Vashkar, Shaikh Mahmud Kamal, Chan, Wai Fong, ChunYau, Mabel Yin, LingChong, Karen Kam, OnLi, Tze, Kaur, Rajwinder, Yan, Ng Po, Chiu, Gloria Chor Shan, Cheung, Christina W. Y., Ching, Patricia T. Y., Ching, Radley H. C., Lam, Conita H. S., Kan, C. H., Lee, Shirley S. Y., Chen, C. P., Chan, Regina F. Y., Leung, Annie F. Y., Wong, Isadora L. C., Lam, S. S., Chan, Queenie W. L., Chan, Cecilia, Nematian, Seyed Sadeq Seyed, Palenik, Charles John, Askarian, Mehrdad, Hatam, Nahid, Nakamura, Itaru, Fujita, Hiroaki, Tsukimori, Ayaka, Kobayashi, Takehito, Sato, Akihiro, Fukushima, Shinji, Matsumoto, Tetsuya, De Roxas, V. James, De Roxas, V James, AndreiAñonuevo, Nicolo, Ho, Yeng May, Kum, Jia Qi, Poh, Bee Fong, Marimuthu, Kalisvar, Liu, Tzu-Yin, Chu, Sin-Man, Chen, Hui-Zhu, Chen, Tun-chieh, Chen, Yichun, Tsao, Ya-Ching, Skuntaniyom, Sumawadee, Tipluy, Pirawadee, Paengta, Sangwan, wongsaen, Ratchanee, thanomphan, Sutthiphun, Tariyo, Samettanet, Thongchuea, Buachan, Khamfu, Pattama, Thanomphan, Sutthiphan, Songtaweesin, Wipaporn Natalie, Anugulruengkit, Suvaporn, Samransamruajkit, Rujipat, Sosothikul, Darintr, Tansrijitdee, Ornanong, Nakphunsung, Anry, Srimuan, Patchareeyawan, Sophonphan, Jirachaya, ThanyaweePuthanakit, Payuk, Kunyanut, Picheansathian, Wilawan, Viseskul, Nongkran, DeNardo, Elizabeth, Leslie, Rachel, Cartner, Todd, Barbosa, Luciana, Werner, Heinz-Peter, Brill, Florian H. H., Kawagoe, Julia Yaeko, De Nardo, Elizabeth, Wilson, Sarah Edmonds, Macinga, David, Mays-Suko, Patricia, Duley, Collette, Hang, Tran Thi Thuy, Hanh, Tran Thi My, Gordon, Christopher, Durairaj, Roopa, Rohit, Anusha, Saravanakumar, Saujanya, Hemalatha, Jothymani, Hirano, Ryuichi, Sakamoto, Yuichi, Yamamoto, Shoji, Tachibana, Naoki, Miura, Miho, Hieda, Fumiyo, Sakai, Yoshiro, Watanabe, Hiroshi, Bacolcol, Silverose Ann, Chavez, Keitleen, Lim, Jia-Wei, Hein, Aung-Aung, Tin, Grace, Lim, Vanessa, Chao, Huwi-chun, Yeh, Chiu-Yin, Lo, Mei-feng, Piwpong, Chonlada, Rajborirug, Songyos, Preechawetchakul, Ploypailin, Pruekrattananapa, Yada, Sangsuwan, Tharntip, Wongsaen, Ratchanee, Paengta, Sungwan, Nilchon, Napatnun, Thanompan, Sutthipun, Tariyo, Samattanet, Kolesnichenko, Svetlana, Tishkambayev, Yerbol, Alibecov, Asylkhan, Serbo, Yekaterina, Nam, Youngwon, Park, Jae Hyeon, Hong, Yun Ji, Kim, Taek Soo, Park, Jeong Su, Park, Kyoung Un, Kim, Eui-Chong, Aziegbemhin, Samuel Abumhere, Enabulele, Onaiwu, Tung, Yao-Shen, Chen, An-Chi, Huang, Shen-Min, Yang, Yui-Yein, Wu, Li-Hung, Lin, Chin-cheng, Lien, Tzu Hao, Chang, Jia Hao, Huang, Yu Shan, Chen, Yi Shun, Sirilun, Sasithorn, Ouirungroj, Phisit, Trakulsomboon, Suwanna, Prasajak, Patcharee, Kwok, Maryanne W. N., Ng, Lady S. H., Wong, Lindy M. T., Poon, Lenina S. L., Lai, Mary K. L., Cheng, Holly H. S., Fong, S. K., Leung, Cindy F. Y., Hasegawa, Jumpei, Shirakawa, Hiroki, Wakai, Sachiko, Mieno, Makiko, Hatakeyama, Shuji, Deeudom, Manu, Tharavichitkul, Prasit, Chinniah, Terrence, Tan, Jackson, Prabu, Kavitha, Alam, Sartaj, Wynn, Aung Kyaw, Ahmad, Rashidah, Sidek, Amalina, Samsuddin, Dg Azizah, Ajis, Noraini, Ahmad, Aliyah, Magon, Susylawathi, Chu, Boon, Kuang, Jiqiu, Gao, Yan, Wang, Shoujun, Hao, Yunxiao, Liu, Rong, Li, Dongmei, Wang, Hui, Nishio, Hisanori, Mori, Hitomi, Morokuma, Yoshiko, Yamada, Takaaki, Kiyosuke, Makiko, Yasunaga, Sachie, Toyoda, Kazuhiro, Shimono, Nobuyuki, Babenko, Dmitriy, Turmuhambetova, Anar, Cheşcă, Antonella, Toleman, Mark A., Akhmaltdinova, Lyudmila L., Magsakay, Mark Albert, Macatibag, Angelo, Lerios, Jeannica Kriselle, Lavrineko, Alyona, Babenko, Dmitry, Sheck, Eugene, Edelstein, Mikhail, Li, Lih-Yue, Chan, Chiung-Wen, Pan, Hui-Chuan, Vanishakije, Wipa, Jaikampun, Warisra, Li, Su-Yin, Li, Jian-Feng, Wu, Yu-Ping, Lin, Chiao-Hui, Chang, Ping-Chin, Tariyo, Samatanet, Thanompan, Suttsiphan, Sukkra, Suchada, Zaman, Khalequ, Zaman, Sheikh Farzana, Zaman, Farzana, Aziz, Asma, Faisal, Sayeed-Bin, Traskine, Magali, Ruiz-Guiñazú, Javier, Borys, Dorota, Lam, Wendy Wai Yee, Chow, May, Choy, Lucy, Kam, Joseph, Salleh, Sharifah Azura, Yacob, Razila, Yusof, Siti Rokiah, Jalil, Nordiah Awang, Millan, Maria Lourdes, Acuin, Jose Lito, Velmonte, Melecia A., Bacolcol, Silverose Ann A., Ting, Ching-I, Dissayasriroj, Sunisa, Chinniah, Terrence Rohan, DiniSuhaimi, Jauharatud, Mirasin, Aizzuddin, Morni, Nurul, Samsuddin, Azizah, AbuBakar, Amalina, Shafiee, Amanie, Safar, Julaini, Annie, Leung, Ling, Fung Yuk, Edna, Lau, Kristine, Luk, Shinomiya, Satoshi, Yamamoto, Kumiko, Kjiwara, Kayoko, Yamaguchi, Mitsuhiro, Zhang, Wei, Poh, Bee-Fong, Chan, Ming-Chin, Wang, Chih-Chien, Huang, Huan-Yu, Lai, Chiung-Ling, Kosol, Sajeerat, Sakolwirat, Wantana, Paepong, Patchanee, Jansanga, Sawalee, Jaisamoot, Pattarin, Thongnuanual, Nuttha, Srithong, Chittima, Somsakul, Somporn, Plongpunth, Sutima, Punpop, Mukkapon, Malathum, Porntip, Peautiwat, Kulada, boon kirdram, Nattawipa, Klunklin, Pimpaporn, Samethadka, Geetha, Suzuki, Naoko, Asada, Hitomi, Katayama, Masao, Komano, Atsushi, Watanabe, Hidehiro, Seo, Hye Kyung, Hwang, Joo-Hee, Shin, Myoung Jin, Kim, Su Young, Kim, Eu Suk, Song, Kyoung-Ho, Kim, Hong Bin, Un, Lai-Si, Vong, Choi-Ian, Koh, Jocelyn, Agustinus, Sherly, Hassan, Rozita Bte Abu, Thinn, Yin Phyu, Ng, Benjamin, Tun, Soe Pyae, Ha, Su Mon Thi, Xiaoting, Xue, Li, Lin, Chuang, Leyland, Niroshika, Attanayaka Mudiyanselage Chulani, Perera, Kaluarachchige Anoma Kaluarachchi, Fernando, Dimingo Kankanamalage Diana Grace, Hemamala, Bodhipakshage Rohini, Yeh, Chiu-yin, Yang, Hui-Chun, Chiu, Hsiang-Ju, Shih, Ya-Ling, Chien, Yu-Shan, Lin, Wan-Yi, Pan, Chia-Yun, Chang, Ying-Yun, Yea, Chiu-Yuch, Chu, Ming-Hsien, Lee, Li-Chu, Yu-Hsiu, Lin, Siao-Pei, Guo, Pak-On, Leung, Mei-Fe, Sie, Jyh-Jou, Chen, Yong-Yuan, Chang, Kuo, Shu-Yuan, Lin, Yu-Hsiu, Zhang, Ji-Sheng, Leung, Pak-On, Sie, Mei-Fe, Chen, Jyh-Jou, Chen, Yan-Ru, Chen, Ying-Ling, Taou, Chi-Fen, Chen, Hsiao-Shan, Tang, Hung-Jen, Chen, Shin Yu, Chen, Yin Yin, Der Wang, Fu, Shih, Tzu-Ping, Chen, Chin-Yu, Chen, Su-Jung, Wu, Mei-chi, Yang, Wan-ju, Chou, Mei-ling, Yu, Man-Ling, Li, Li-Chu, Chu, Cheng-Wei, Tsou, Wen-Hao, Wu, Wen-Chih, Cheng, Wen-Chi, Sun, Cho-Ching, Lu, Shu-Hua, Yang, Hsin-Ling, Lu, Cheng-Yu, Hirunprapakorn, Nitchawan, Apivanich, Sirilux, Pornmee, Ttipakorn, Beowsomboon, Chonnikarnt, Kumkoom, Itthaporn, Kasatpibal, Nongyao, Chitreecheur, Jittaporn, Whitney, JoAnne D., Saokaew, Surasak, Kengkla, Kirati, Heitkemper, Margaret M., Muntajit, Thanomvong, Apivanich, Siriluk, Phan, Hang Thi, Dinh, Anh Pham Phuong, and Nguyen, Tuyet Thi Kim

Subjects
lcsh:RC109-216, Meeting Abstracts, lcsh:Infectious and parasitic diseases
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36. A novel 3-TFT voltage driving method of compensating VTH shift for a-Si:H TFT and OLED degradation for AMOLED

Author

Lin, Chih-Lung, Chou, Kuan-Wen, Chang, Fu-Chieh, and Hung, Chia-Che

Subjects
*ELECTRIC potential, *AMORPHOUS substances, *SILICON, *ELECTRIC currents, *MEASUREMENT, *ELECTRONIC modulation, *ELECTRONIC circuits, *PIXELS
Abstract

Abstract: This work demonstrates the feasibility of a novel pixel circuit by using three a-Si:H TFTs. The proposed circuit can stabilize the OLED current and provide an additional driving current to ameliorate the brightness degradation of the AMOLED. Measurement results indicate that the current degradation of the proposed circuit, caused by VTH variations, is less than 5% over more than 50,000s at 60°C, whereas that of a conventional 2T1C pixel circuit is larger than 34%. Furthermore, to ameliorate the decrease in luminance owing to the OLED degradation, the OLED current can be increased by 10% by analyzing the current degradation and modulating the detected voltage appropriately. [Copyright &y& Elsevier]

Published
2011
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37. Catalytic and photoresponsive BiZ/Cu x S heterojunctions with surface vacancies for the treatment of multidrug-resistant clinical biofilm-associated infections.

Author

Nain A, Huang HH, Chevrier DM, Tseng YT, Sangili A, Lin YF, Huang YF, Chang L, Chang FC, Huang CC, Tseng FG, and Chang HT

Subjects
Animals, Anti-Bacterial Agents pharmacology, Biofilms, Catalysis, Mice, Microbial Sensitivity Tests, Diabetes Mellitus, Experimental
Abstract

We report a one-pot facile synthesis of highly photoresponsive bovine serum albumin (BSA) templated bismuth-copper sulfide nanocomposites (BSA-BiZ/Cu x S NCs, where BiZ represents in situ formed Bi 2 S 3 and bismuth oxysulfides (BOS)). As-formed surface vacancies and BiZ/Cu x S heterojunctions impart superior catalytic, photodynamic and photothermal properties. Upon near-infrared (NIR) irradiation, the BSA-BiZ/Cu x S NCs exhibit broad-spectrum antibacterial activity, not only against standard multidrug-resistant (MDR) bacterial strains but also against clinically isolated MDR bacteria and their associated biofilms. The minimum inhibitory concentration of BSA-BiZ/Cu x S NCs is 14-fold lower than that of BSA-Cu x S NCs because their multiple heterojunctions and vacancies facilitated an amplified phototherapeutic response. As-prepared BSA-BiZ/Cu x S NCs exhibited substantial biofilm inhibition (90%) and eradication (>75%) efficiency under NIR irradiation. Furthermore, MRSA-infected diabetic mice were immensely treated with BSA-BiZ/Cu x S NCs coupled with NIR irradiation by destroying the mature biofilm on the wound site, which accelerated the wound healing process via collagen synthesis and epithelialization. We demonstrate that BSA-BiZ/Cu x S NCs with superior antimicrobial activity and high biocompatibility hold great potential as an effective photosensitive agent for the treatment of biofilm-associated infections.

Published
2021
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38. Discordance of vancomycin minimum inhibitory concentration for methicillin-resistant Staphylococcus aureus at 2  μ g/mL between Vitek II, E -test, and Broth Microdilution.

Author

Kuo CF, Lio CF, Chen HT, Wang YT, Ma KS, Chou YT, Chang FC, and Tsai SY

Abstract

Background: Vancomycin, the first line antibiotic for methicillin-resistant Staphylococcus aureus (MRSA) bacteremia, is often administered inappropriately when MIC is greater than 2 µg/mL, including 'susceptible' strains. This study assessed the discordance of vancomycin minimum inhibitory concentration (MIC) for methicillin-resistant Staphylococcus aureus (MRSA)., Methods: In total, 229 MRSA isolates from blood cultures collected between 2009 and 2015 at a tertiary hospital in Taiwan were examined. The MICs of vancomycin were measured using Vitek 2, E -test, and standard broth microdilution at the level of 2 µg/mL., Results: The geometric mean of the MICs of hospital-acquired MRSA was higher than that of community-acquired MRSA ( P  < 0.001), with the exact agreement rates (with broth microdilution) at 2 µg/mL being 53.6% in Vitek 2 and 86.7% in E -test. Overall, E -test (98.1%) had more categorical accordance than did Vitek 2 (94.0%; P  = 0.026). Vitek 2 had a tendency to overestimate MRSA in high-MIC isolates, whereas E-test inclined underestimation in low-MIC isolates. Surprisingly, the discordance rates of MRSA vancomycin MICs were higher in hospital-acquired isolates (13.3%-17.0%) than in community-acquired isolates (6.2%-7.0%)., Conclusion: The Infectious Diseases Society of America recommends the use of alternative antimicrobial agents when vancomycin MIC is ≥ 2 µg/mL; in this study, only 53.6% of the isolates tested using Vitek 2 showed a high MIC in the broth microdilution method. Accurate identification of the resistance profile is a key component of antimicrobial stewardship programs. Therefore, to reduce inappropriate antibiotic use and mitigate the emergence of resistant strains, we recommend using complementary tests such as E -test or Broth microdilution to verify the MIC before administering second-line antibiotics., Strengths: (1) We compared the categorical agreement between different methods measuring MRSA MICs level. (2) Physicians should incorporate this information and consider a complementary test to verify the appropriateness of the decision of shifting vancomycin to second-line antibiotic treatment to improve patients' prognosis. (3) MRSA-vancomycin MICs at a cutoff of 2 µg/mL obtained using Vitek II exhibited a higher sensitivity level and negative predictive value than those obtained using E -test in the prediction of categorical agreement with standard broth microdilution., Limitation: (1) Our research was based on a single hospital-based study. (2) The MRSA strains in this study were stored for more than 12 months after isolation. (3) We did not collect information on clinical prognosis., Competing Interests: The authors declare there are no competing interests., (©2020 Kuo et al.)

Published
2020
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