Liang He

Hefei University of Technology

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Qipu Lin

Fujian Institute of Research on the Structure of Matter

Ming‐Bu Luo

Fujian Institute of Research on the Structure of Matter

Er‐Xia Chen

Chinese Academy of Sciences

Yu-Jia Cheng

Chinese Academy of Sciences

Yayong Sun

Fujian Institute of Research on the Structure of Matter

Qin Chen

Southwest Jiaotong University

Jie Zhou

Affiliated Hospital of Southwest Medical University

Xuechou Zhou

Fujian Agriculture and Forestry University

Xuanwen Ding

Nankai University

An-An Zhang

Luye Pharma (China)

Cooperative Institutions

Chinese Academy of Sciences

Fujian Institute of Research on the Structure of Matter

Fuzhou University

University of Chinese Academy of Sciences

East China Normal University

Ministry of Agriculture and Rural Affairs

Tsinghua University

Harbin Institute of Technology

Fujian Normal University

Shandong University

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CCDC 2240144: Experimental Crystal Structure Determination

The Cambridge Structural Database (2023)

Bo Wang Gui-Xin Yan Yu-Jia Cheng Er‐Xia Chen Liang He

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

10.5517/ccdc.csd.cc2f61qf

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Estimation of vital signs from facial videos via video magnification and deep learning

iScience (2023)

Bin Lin Jing Tao Jingjing Xu Liang He Nenrong Liu

The continuous monitoring of vital signs is one of the hottest topics in healthcare. Recent technological advances in sensors, signal processing, and image processing spawned the development of no-contact techniques such as remote photoplethysmography (rPPG). To solve the common problems of rPPG including weak extracted signals, body movements, and generalization with limited data resources, we proposed a dual-path estimation method based on video magnification and deep learning. First, image processes are applied to detect, track, and magnificate facial ROIs automatically. Then, the steady part of the wave of each processed ROI is used for the extraction of features including heart rate, PTT, and features of pulse wave waveform. The blood pressures are estimated from the features via a small CNN. Results comply with the current standard and promise potential clinical applications in the future.

Photoplethysmogram

10.1016/j.isci.2023.107845

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CGMI: Configurable General Multi-Agent Interaction Framework

arXiv (Cornell University) (2023)

Shi Jinxin Jiabao Zhao Yilei Wang Xingjiao Wu Jiawen Li

Benefiting from the powerful capabilities of large language models (LLMs), agents based on LLMs have shown the potential to address domain-specific tasks and emulate human behaviors. However, the content generated by these agents remains somewhat superficial, owing to their limited domain expertise and the absence of an effective cognitive architecture. To address this, we present the Configurable General Multi-Agent Interaction (CGMI) framework, designed to replicate human interactions in real-world scenarios. Specifically, we propose a tree-structured methodology for the assignment, detection, and maintenance of agent personality. Additionally, we designed a cognitive architecture equipped with a skill library based on the ACT* model, which contains memory, reflection, and planning modules. We have also integrated general agents to augment the virtual environment's realism. Using the CGMI framework, we simulated numerous classroom interactions between teacher and students. The experiments indicate that aspects such as the teaching methodology, curriculum, and student performance closely mirror real classroom settings. We will open source our work.

Reflection

10.48550/arxiv.2308.12503

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CCDC 2242746: Experimental Crystal Structure Determination

The Cambridge Structural Database (2023)

Qin Hou Lijun Chen Jing He Er‐Xia Chen Bo Wang

10.5517/ccdc.csd.cc2f8rn4

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Design of Multiple Gravity Assist Trajectories with Deep Space Maneuver

Zhongguo kongjian kexue jishu (2013)

Ying Zhang Xiaokui Yue Liang He

10.3780/j.issn.1000-758x.2013.02.010

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Curriculum Prompt Learning with Self-Training for Abstractive Dialogue Summarization

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2022)

Changqun Li Linlin Wang Xin Lin Gerard de Melo Liang He

Succinctly summarizing dialogue is a task of growing interest, but inherent challenges, such as insufficient training data and low information density impede our ability to train abstractive models. In this work, we propose a novel curriculum-based prompt learning method with self-training to address these problems. Specifically, prompts are learned using a curriculum learning strategy that gradually increases the degree of prompt perturbation, thereby improving the dialogue understanding and modeling capabilities of our model. Unlabeled dialogue is incorporated by means of self-training so as to reduce the dependency on labeled data. We further investigate topic-aware prompts to better plan for the generation of summaries. Experiments confirm that our model substantially outperforms strong baselines and achieves new state-of-the-art results on the AMI and ICSI datasets. Human evaluations also show the superiority of our model with regard to the summary generation quality.

Training set

10.18653/v1/2022.emnlp-main.72

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Citations (3)

CCDC 1967120: Experimental Crystal Structure Determination

The Cambridge Structural Database (2020)

Ruhuai Mei Xinyue Fang Liang He Junmei Sun Liang Zou

10.5517/ccdc.csd.cc240yhm

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Look-Up-Table Predistortion Technique Based on Polynomial Nonlinearity Compensation in OFDM Systems

Minjie Gong Jianguo Yu Liang He

A novel Look-Up-Table (LUT) predistortion power amplifier (PA) linearizer for OFDM systems is proposed in this paper. To improve conventional LUT linearizer, we employ Polynomial predistortion method to compensate the nonlinearity of PA through a filter. The provided method can speedup the algorithm convergence of LUT while reduce adjacent channel interference (ACI). In addition, hardware requirement of conventional LUT linearizer can also be decreased. The analysis, simulation and measured results are presented to validate the expected performances of the proposed method, a comparison with the conventional LUT linearizer is included.

Linearizer

Lookup table

Adjacent-channel interference

Adjacent channel

10.1109/wicom.2009.5300855

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Real-time dynamic addressing for spacecraft soft landing in the lunar surface

Liang He Qinhu Ren Yujian Feng Jianhua Zhang Sheng Liu

During the process of the soft landing, because of the lunar surface topography, the spacecraft will be subject to the threat of an obstacle and makes the landing fail. Therefore, hazard avoidance and addressing are the key technology for spacecraft soft landing in the lunar surface. In this paper, algorithms are described for vision-based hazard detection, safe site selection and real-time dynamic addressing. The algorithms are validated in a realistic simulated scenario representing a landing on lunar. Results show that the developed hazard avoidance algorithms are effective at detecting hazards and guiding the lander to a safe landing site.

Soft landing

Moon landing

10.1109/icinfa.2015.7279430

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Citations (2)

Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis

Xuanwen Ding Jie Zhou Liang Dou Qin Chen Yuanbin Wu

Boosting

Sentiment Analysis

10.18653/v1/2024.findings-emnlp.252

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Citations (1)