2020 Topical Symposia
Bioimaging and biophysics(BIO)
O2:1/28 09:00~10:30
O3:1/28 11:00~12:00
O4:1/28 13:30~15:00
Chen Chih Hall 508高甫仁 (國立陽明大學生醫光電研究所)
Fu-Jen Kao ( Institute of Biophotonics, National Yang-Ming University )/
謝佳龍 (中央研究院原子與分子科學研究所 )
Chia-Lung Hsieh ( Institute of Atomic and Molecular Sciences,  Academia Sinica )/
朱士維 (國立臺灣大學物理學系)
Shi-Wei Chu ( Department of Physics, National Taiwan University )/
Wen-Chuan Kuo ( Institute of Biophotonics, National Yang-Ming University )/
王俊杰 (國家同步輻射研究中心)
Chun-Chieh Wang ( National Synchrotron Radiation Research Center )
Bioimaging is a rapidly growing field and it has revolutionized biophysical research by providing data with high spatial and temporal resolutions. In the 2021 Annual Meeting, we are hosting a special symposium to bridge these two closely related fields of advanced imaging technology and biophysics. This year we have with a special emphasis on the development of x-ray imaging techniques (CT, crystallography, SAXS, etc), as well as their applications in biology such as brain connectome and proteomics. We aim to provide a platform not only for microscopists who work on various cutting-edge imaging techniques (super-resolution, high-speed and deep-tissue imaging, etc.), but also for researchers who study biophysics by using these advanced technologies. Finally, we hope this symposium to inspire the young talents and to stimulate emerging research activities in Taiwan.

Emerging Energy Science(EES)
O2:1/28 09:00~10:30
O3:1/28 11:00~12:00
O4:1/28 13:30~15:00
Chen Chih Hall 501董崇禮 (淡江大學物理學系)
Chung-Li Dong ( Department of Physics, Tamkang University )/
林彥谷 (國家同步輻射研究中心)
Yan-Gu Lin ( National Synchrotron Radiation Research Center )
The planet earth is currently experiencing extreme climatic conditions and severe shortages of resources. There is a need for the country to ride the wave of energy transition and grasp the opportunities presented by green energy technologies. It is urgent to comply with the greenhouse gas reduction ratified in the Paris Agreement (COP21). However, the global carbon dioxide concentration measured at the Mauna Loa Observatory this year, reached 415 ppm, a record high. Green and low-carbon energy will play a key role in achieving Carbon Cycle 2.0 and lead us to the third industrial revolution. Currently, energy self-sufficiency is less than 2% in Taiwan, more than 98% still rely on imports (especially for petrochemical sources). How to achieve 20% of renewable energy usage in 2025 planned by the Taiwan government depends on the integration of frontier scientific research and cutting-edge technology development from various disciplines. This forum “Emerging Energy Science” will encompass advanced functional materials for solar hydrogen energy, artificial photosynthesis, energy storage batteries/supercapacitors, energy saving windows, efficient nanocatalysis, etc. Through the exchanges among multiple parties, integrating cross-disciplinary knowledge (including nanoscience, semiconductor, and electro-optics/optoelectronics), we hope to accelerate local research on energy technologies, and early realization of government`s goal to development of sustainable and renewable energy.
石化燃料成本低、技術成熟,一直是能源主要供應來源。但所排放溫室氣體造成全球極端氣候。早在2010年哥本哈根會議中提出減碳排放以控制升溫2C內。並進一步在2015年聯合國氣候變化綱要公約締約國會議(COP21)通過「巴黎協定」,訂立國際溫室氣體減量規範,重申願景為控制1.5度內,並於2023年首度總檢視各國減碳成果。惟今年6月在Mauna Loa Observatory所測得全球二氧化碳濃度達416.39ppm、又創新高。期間,台灣已於2017規劃「能源轉型路徑」,其中之一為發展無碳再生能源、目標為2025年再生能源佔比為20%(目前僅約4%)。台灣能源結構勢必須面臨重大挑戰。有鑑於此,亦承如2019諾獎得主Goodenough教授於訪問中強調,除了儲能電池,如何利用太陽氫能是未來乾淨永續能源發展方向。有鑑於近年台灣在新穎、能源及功能性材料的國際高水準表現,本論壇將以「新興能源科學」為主軸,涵蓋太陽氫能、人工光合作用、奈米催化、儲能二次電池及超電容、熱電等能源材料。透過跨領域(包含奈米科學,半導體,光學光電)之基礎研究交流,整合研究技術與科學知識,盼能加速國內能源科技之研究及發展,以保持在國際上能源科技之地位,早日達到再生能源發展目標。
Numerical method for quantum many-body systems:
Non-equilibrium systems and machine learning(NM)
O2:1/28 09:00~10:30
O4:1/28 13:30~15:00
Chen Chih Hall 510高英哲 (國立臺灣大學物理學系)
Ying-Jer Kao ( Department of Physics, National Taiwan University )/
黃靜瑜 (東海大學應用物理系)
Ching-Yu Huang ( Department of Applied Physis, Tunghai University )/
林瑜琤 (國立政治大學應用物理所)
Yu-Cheng Lin ( Graduate Institute of Applied Physics, National Chengchi University )/
陳柏中 (國立清華大學物理學系)
Pochung Chen ( Department of Physics, National Tsing Hua University)
In recent years, theoretical connections between deep learning, renormalization group, and tensor networks have been actively explored. In this symposium we address various new frontiers in numerical methods for quantum many-body systems and their inter-connetions. In particular, we focus on non-equilibrium and dynamical system, topological order, and machine learning.

Chen Chih Hall:教學大樓

To find the details of agenda, please refer Oral sessions.