ICICN will provide a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in all fields below.
The workshops will be held in Dunhuang on Aug. 23-24.
研讨分会将于8月23-24日在敦煌举行。
Quantum information processing, using quantum platforms as the information carrier and processor, allows to transmit fundamentally secure data, solve problems beyond the power of classical super computers, and enhance the precession of measurements beyond the quantum limit. The develop of micro and nanophotonics leads to compact photonic chips that would greatly improve the capability of the traditional electronic chips in terms of processing speed, channel capacity and energy consumption. Recently, optical micro and nanostructures become an increasingly important platform for quantum information, leading to high-quality quantum light sources, on-chip photonic quantum processors, on-chip quantum simulators and computers for specific quantum problems. The workshop welcomes researchers in the fields of micro/nanophotonics, and quantum information with photons and other platforms.
The topics of the workshop include, but not limited to:
* Optoelectronics at micro and nanoscale
* Optical microcavities, surface plasmon, metasurfaces and metamaterials
* Light-matter interactions in nanostructures, low-dimensional materials and atomic/molecular systems
* Quantum dots and quantum light sources
* Quantum communications
* Quantum computing and quantum simulation
* Quantum metrology of high precision
* Novel materials and devices for quantum information
* Quantum programming and quantum algorithms
The goals of this workshop are to (1) solicit novel methodologies of pre-training models for NLP, (2) investigate research opportunities of improving efficiency of pre-training models, (3) explore and discuss the advantage and possibilities of pre-training for more related tasks.
The topics of interest include (but not limited to):
* Self-supervised learning for NLP
* Pre-training NLP task
* Pre-training model optimization
* Lightweight pre-training model
* Advanced multi-modal applications
* Benchmark datasets and novel evaluation methods
We invite the submissions of long and short papers featuring substantial, original, and unpublished research, and expect industrial demonstrations concerning the related topics.
Chairs:
Xiao Sun, Hefei University of Technology, China 孙晓,合肥工业大学
Tong Xu, University of Science and Technology of China 徐童,中国科学与技术大学
Longfe Wu, JD.COM Silicon Valley Research Center, USA 吴凌飞,京东硅谷研究中心
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