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arXiv 2023年

作者： Liu, Feifan Zhang, Shuang Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No.2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in deep learning techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists' topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress. © 2023, CC BY-NC-ND.

关键词： Deep learning

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The Temporal Structural Pattern in Scientific Collaborative Behavior from the Perspective of Complex Network 23rd

The Temporal Structural Pattern in Scientific Collaborative...

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23rd International Symposium on Knowledge and Systems Sciences, KSS 2024

作者： Zholdoshbaeva, Elina Zhang, Shuang Liu, Feifan Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China School of Business East China University of Science and Technology 130 Meilong Road Xuhui District Shanghai200237 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

ISBN: (纸本)9789819601776

The rapid advancement of science has underscored the importance of collaboration among scientists. While many studies have explored the structure of collaboration networks, there is a lack of comprehensive quantitative research on the temporal patterns of these collaborations. This study addresses this gap by analyzing temporal scientific collaboration networks in physics, spanning from 1960 to 2021, using complex network theories and methods. By constructing five-year temporal collaborative networks and employing metrics such as network diameter, shortest path, and clustering coefficient, this research reveals an increase in connectivity and small-world characteristics over time. Additionally, community detection highlights a shift towards specialized collaboration patterns and increased cross-community interactions. The study also uncovers growing centrality and social stratification within the research system, with the "rich club" phenomenon indicating strong and dynamic collaborations among highly connected scientists. Temporal analysis shows that while ultra-high connectivity scientists engage in fluctuating relationships, medium-high connectivity scientists maintain more stable collaborations. These findings provide insights into the evolution of collaborative behaviors and inform science policy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

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Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

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Procedia Computer Science 2023年 226卷 36-42页

作者： Tianhang Liu Hechen Li Long Bai Yanan Wu An Wang Mobarakol Islam Hongliang Ren Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong China College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Department of Biomedical Engineering National University of Singapore Singapore Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Robot-assisted airway intubation application needs high accuracy in locating targets and organs. Two vital landmarks, nostrils and glottis, can be detected during the intubation to accommodate the stages of nasal intubation. Automated landmark detection can provide accurate localization and quantitative evaluation. The Detection Transformer (DeTR) leads object detectors to a new paradigm with long-range dependence. However, current DeTR requires long iterations to converge, and does not perform well in detecting small objects. This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation. The semantics aligner can effectively align the semantics of object queries and image features in the same embedding space using the most discriminative features. To evaluate the performance of our solution, we utilize a publicly accessible glottis dataset and automatically annotate a nostril detection dataset. The experimental results demonstrate our competitive performance in detection accuracy. Our code can be accessible at https://***/ConorLTH/airway-intubation_landmarks-detection .

关键词： Bleeding regions segmentation Medical image segmentation Semi-supervised learning Video capsule endoscopy

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A Protein Embedding Model for Drug Molecular Screening

A Protein Embedding Model for Drug Molecular Screening

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International Conference on Big Data and Smart computing (BIGCOMP)

作者： Chao Che Min Zhu Yongjun Zhu Qiang Zhang Dongsheng Zhou Bin Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China Department of Library and Information Science Sungkyunkwan University Seoul Republic of Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Application of machine learning method in new drug development can greatly shorten the process of experimental discovery and reduce the risk of clinical failure. However, the feature extraction of proteins sequence is very difficult due to the large dimension. To this end, we propose a Protein Embedding Model(PEM) for drug molecular screening to predict the interaction between proteins and small molecules. Specifically, PEM first classifies 20 kinds of amino acids into 6 categories to reduce the dimension and learns the representation of protein borrowing the idea of word embedding. Then the model uses multiple imputation to fill the physical and chemical properties of small molecule compounds. Finally, the model uses LightGBM model to predict the affinity value Ki between proteins and small molecules. Experiments show that the model can effectively extract the features of proteins and small molecules and outperforms other traditional methods on the data provided by a drug discovery and development company.

关键词： Amino acids Feature extraction Drugs Protein sequence Chemicals Compounds

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Speech Emotion Recognition Based on Image Enhancement 14

Speech Emotion Recognition Based on Image Enhancement

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14th IEEE International Conference on intelligent Systems and Knowledge Engineering, ISKE 2019

作者： Wang, Dongyan Dong, Jing Zhou, Dongsheng Wei, Xiaopeng Zhang, Qiang Ministry of Education Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9781728123486

The performance of an emotion recognition system is determined by the quality of emotional features. In this paper, we propose a feature optimization algorithm based on image enhancement and present a convolutional recurrent model to realize emotional recognition of natural speech. For three-dimensional (3-D) log-Mel spectrum and 3-D spectrogram features, the fast gamma transformation with an adaptive threshold is adopted for feature enhancement to make full use of the dynamic characteristics of non-stationary speech signals. Meanwhile, the model combining Convolutional Neural Network (CNN) with the rectangular kernels and Long Short-Term Memory (LSTM) is used to complete speech emotion recognition tasks. Experiments are carried out on two public emotional datasets, and results demonstrate the good generalization ability and recognition performance of our proposed model. © 2019 IEEE.

关键词： Image enhancement

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Using entropy-driven amplifier circuit response to build nonlinear model under the influence of Lévy jump

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BMC bioinformatics 2022年第SUPPL 12期22卷 437页

作者： Hao Fu Hui Lv Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. lh8481@***. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China. lh8481@***. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. zhangq@***. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China. zhangq@***.

BACKGROUND:Bioinformatics is a subject produced by the combination of life science and computer science. It mainly uses computer technology to study the laws of biological systems. The design and realization of DNA circuit reaction is one of the important contents of bioinformatics. RESULTS:In this paper, nonlinear dynamic system model with Lévy jump based on entropy-driven amplifier (EDA) circuit response is studied. Firstly, nonlinear biochemical reaction system model is established based on EDA circuit response. Considering the influence of disturbance factors on the system, nonlinear biochemical reaction system with Lévy jump is built. Secondly, in order to prove that the constructed system conforms to the actual meaning, the existence and uniqueness of the system solution is analyzed. Next, the sufficient conditions for the end and continuation of EDA circuit reaction are certified. Finally, the correctness of the theoretical results is proved by numerical simulation, and the reactivity of THTSignal in EDA circuit under different noise intensity is verified. CONCLUSIONS:In EDA circuit reaction, the intensity of external noise has a significant impact on the system. The end of EDA circuit reaction is closely related to the intensity of Lévy noise, and Lévy jump has a significant impact on the nature of biochemical reaction system.

关键词： Entropy-driven amplifier Lyapunov function Lévy jump Nonlinear system model

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A molecular device: A DNA molecular lock driven by the nicking enzymes

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Computational and structural biotechnology journal 2020年 18卷 2107-2116页

作者： Xiaokang Zhang Qiang Zhang Yuan Liu Bin Wang Shihua Zhou Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China.

As people are placing more and more importance on information security, how to realize the protection of information has become a hotspot of current research. As a security device, DNA molecular locks have great potential to realize information protection at the molecular level. However, building a highly secure molecular lock is still a serious challenge. Therefore, taking advantage of the DNA strand displacement and enzyme control technology, we constructed a molecular lock with a self-destructive mechanism. This molecular lock is mainly composed of logic circuits and takes nicking enzymes as inputs. To build this molecular lock, we first constructed a series of cascade circuits, including a YES-YES cascade circuit and a YES-AND cascade circuit. Then, an Inhibit logic gate was constructed to explore the inhibitory properties between different combinations of two nicking enzymes. Finally, using the characteristics of mutual inhibition between several enzymes, a DNA molecular lock driven by three nicking enzymes was constructed. In this molecular device, only the correct sequence of nicking enzymes can be input to ensure the normal operation of the molecular lock. Once the wrong password is entered, the device will be destroyed and cannot be recovered, which effectively prevents intruders from cracking the lock through exhaustive methods. The molecular lock has the function of simulating an electronic keyboard, which can realize the protection of information at the molecular level, and provides a new implementation method for building more advanced and complex molecular devices.

关键词： DNA Information Security Molecular Lock Nicking Enzyme

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Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

arXiv

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arXiv 2023年

作者： Liu, Tianhang Li, Hechen Bai, Long Wu, Yanan Wang, An Islam, Mobarakol Ren, Hongliang Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China University College London London United Kingdom Department of Biomedical Engineering National University of Singapore Singapore Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

关键词： Semantics

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Humanoid Robot Control Based on Deep Learning 12th

Humanoid Robot Control Based on Deep Learning

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Guo, Bin Yi, Pengfei Zhou, Dongsheng Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China College of Computer Science and Technology Dalian University of Technology Dalian1160243 China

ISBN: (纸本)9783030237110

The direct control of humanoid robot by human motion is an important aspect of current research. Most of these methods are based on additional equipments, such as Kinect, which are usually not equipped on robot. In order to avoid using these external equipments, we explored a robot controlling method only using the low-resolution camera on robot. Firstly, a stacked hourglass network is employed to obtain the accurate 2D heatmap containing positions of human joints from RGB image captured by camera on robot. Then, 3D human poses including coordinates of human body joints are estimated from 2D heatmaps by a method aiming to reconstruct 3D human poses from 2D poses. Finally, the rotation angles of robot are computed according to these 3D coordinates and are transmitted to the robot to reconstruct the original human pose. Using the NAO robot as an example, the experimental results show that the humanoid robot can imitate motions of different human actors in different scenes well while applying our method. © 2019, Springer Nature Switzerland AG.

关键词： Anthropomorphic robots

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Base station network traffic prediction approach based on LMA-DeepAR

arXiv

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arXiv 2021年

作者： Zhang, Jiachen Zuo, Xingquan Xu, Mingying Han, Jing Zhang, Baisheng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing Uinversity of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing Uinversity of Posts and Telecommunications Beijing China Network Interface Virtualization Shanghai System Design Deptment Zhongxing Telecommunication Equipment Corporation Shanghai China

Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the network traffic nonlinear and non-stationary, which brings challenges to the long-term prediction of network traffic. In this paper, the traffic model LMA-DeepAR for base station network is established based on DeepAR. Acordding to the distribution characteristics of network traffic, this paper proposes an artificial feature sequence calculation method based on local moving average (LMA). The feature sequence is input into DeepAR as covariant, which makes the statistical characteristics of network traffic near a period of time in the past be considered when updating parameters, and the interference of non-stationary network traffic on model training will be reduced. Experimental results show that the proposed prediction approach (LMA-DeepAR) outperforms other methods in the overall long-term prediction performance and stability of multi cell network traffic. Copyright © 2021, The Authors. All rights reserved.

关键词： Base stations

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