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Author Correction: Full reconstruction of simplicial complexes from binary contagion and Ising data

Nature communications 2022年第1期13卷 3692页

作者： Huan Wang Chuang Ma Han-Shuang Chen Ying-Cheng Lai Hai-Feng Zhang The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China. School of Internet Anhui University Hefei 230601 China. School of Physics and Material Science Anhui University Hefei 230601 China. School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ 85287 USA. The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China. haifengzhang1978@***.

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Multiple reconstruction compression framework based on PNG image

arXiv

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arXiv 2019年

作者： Lu, Zhiqing Yin, Zhaoxia Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, this paper proposes a joint framework based on neural network and zoom compression. The framework first encodes the incoming PNG or JPEG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next we import the intermediate state image into the zooming compressor and re-pressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system can not feel the change of the effect. Copyright © 2019, The Authors. All rights reserved.

关键词： Image compression

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GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state

arXiv

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arXiv 2022年

作者： Bian, Junyi Huang, Xiaodi Zhou, Hong Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia Atypon Systems LLC United Kingdom Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

Extracting summaries from long documents can be regarded as sentence classification using the structural information of the documents. How to use such structural information to summarize a document is challenging. In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization. In particular, GoSum encodes sentence states in reinforcement learning by building a heterogeneous graph for each input document at different discourse levels. An edge in the graph reflects the discourse hierarchy of a document for restraining the semantic drifts across section boundaries. We evaluate GoSum on two datasets of scientific articles summarization: PubMed and arXiv. The experimental results have demonstrated that GoSum achieve state-of-the-art results compared with strong baselines of both extractive and abstractive models. The ablation studies further validate that the performance of our GoSum benefits from the use of discourse information. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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An energy efficiency optimization algorithm based on the factor for portion of time division in backhaul-constrained ultra dense hetnets 4

An energy efficiency optimization algorithm based on the fac...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Chu, Shiqiang Hu, Yanjun Wang, Yi Ministry of Education Anhui University Key Laboratory of Intelligent Computing Signal Processing Hefei China

ISBN: (纸本)9781538683392

The ultra dense networks (UDN) are considered as a key technology of 5G for its ability to increase communication capacity. However, the problem of constrained backhaul and the lack of energy which is caused by micro base station without power supply equipment are two mainly bottlenecks of UDN. In order to solve the problem of low micro base station's energy efficiency (MIEE) due to constrained backhaul, we propose an energy efficiency optimization algorithm based on the factor for the portion of time division (FPTD). The optimization problem considers a joint design of downlink precoding, power allocation and the FPTD, meanwhile, we utilize first order Taylor convex approximation (FOTCA), convex slack variable and one-dimensional search method (ODS) to solve. Simulation results show that the proposed FPTD-based energy efficiency optimization algorithm (FEEOA) is improved with much better energy efficient compared with the primitive scheme without FPTD, and more throughputs can be transmitted at the same energy consumption. © 2018 IEEE.

关键词： Base stations

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Performance Evaluation of an Anomaly-Detection Algorithm for keystroke-Typing Based Insider Detection

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Tsinghua Science and Technology 2018年第5期23卷 513-525页

作者： Liang He Zhixiang Li Chao Shen National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu 610041 China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an 710004 China

keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user＇s legitimacy, and the performances of these classifications should be confirmed to identify the most promising research direction. However, classification research contains several experiments with different conditions such as datasets and methodologies. This study aims to benchmark the algorithms to the same dataset and features to equally measure all performances. Using a dataset that contains the typing rhythm of 51 subjects, we implement and evaluate 15 classifiers measured by Fl-measure, which is the harmonic mean of a false-negative identification rate and false-positive identification rate. We also develop a methodology to process the typing data. By considering a case in which the model will reject the outsider, we tested the algorithms on an open set. Additionally, we tested different parameters in random forest and k nearest neighbors classifications to achieve better results and explore the cause of their high performance. We also tested the dataset on one-class classification and explained the results of the experiment. The top-performing classifier achieves an Fl-measure rate of 92% while using the normalized typing data of 50 subjects to train and the remaining data to test. The results, along with the normalization methodology, constitute a benchmark for comparing the classifiers and measuring the performance of keystroke dynamics for insider detection.

关键词： keystroke dynamics insider identification Fl-measure normalization one-class classification

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Bernstein Bezout Matrix with Applications to the Generalized Lienard-Chipart Stability Criterion

Bernstein Bezout Matrix with Applications to the Generalized...

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第三十八届中国控制会议

作者： Huazhang Wu Xiaoting Shen Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Mathematical Sciences Anhui University

In polynomial and linear control systems, the Lienard-Chipart stability criterion plays an important role in the judgment of the zeros of a real polynomial based on the inertia of a Bezout matrix. In this paper we consider the case in the Bernstein polynomials basis. First, the Bernstein Bezout matrix and some important properties are introduced, and then a generalized perturbations of a real polynomial under the Bernstein polynomials basis is considered. Finally, a generalized Lienard-Chipart stability criterion in terms of the Bernstein Bezout matrix is established.

关键词： Bernstein polynomials basis Bernstein Bezout matrix perturbation Lienard-Chipart stability criterion

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Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Liao, Zhirui Huang, Xiaodi Mamitsuka, Hiroshi Zhu, Shanfeng Fudan University School of Computer Science Shanghai200433 China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University AlburyNSW2640 Australia Bioinformatics Center Institute for Chemical Research Kyoto University Kyoto Uji611-0011 Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9781665401265

A number of machine learning (ML) approaches for drug discovery have been availab.e that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is availab.e at (https://***/zhiruiliao/Drug3D-DTI). © 2021 IEEE.

关键词： Molecules

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Hybrid graph-based radiology report generation

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Expert Systems with Applications 2025年 289卷

作者： Dengdi Sun Chaofan Mu Xuyang Fan Jin Tang Zegeng Li Jie Zhu Zhuanlian Ding Key Laboratory of Intelligent Computing & Signal Processing (ICSP) Ministry of Education School of Artificial Intelligence Anhui University Hefei 230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei 230601 China First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031 China College of Integrated Chinese and Western Medicine Anhui University of Chinese Medicine Hefei 230012 China School of Internet Anhui University Hefei 230039 China

Automatic report generation plays a crucial role in clinical practice by alleviating the heavy workload on doctors and helping to prevent misdiagnoses or missed diagnoses. In the context of radiology report generation, knowledge injection is essential, particularly with the encoder-decoder framework commonly used in image captioning tasks. However, existing studies predominantly rely on expert knowledge, which is both challenging to collect and lacks universality. Additionally, this expert knowledge typically influences single-mode information and overlooks the importance of bridging the visual-to-textual gap. To address these challenges, we propose a hybrid graph-based approach for radiology report generation. Our method integrates two key components: the semantic homogeneous graph (SHG) and the cross-modal heterogeneous graph (CHG). Specifically, the SHG is constructed by mining semantic relationships between keywords across the entire corpus to generate universal knowledge. The CHG, conversely, is built from visual features, textual features, and corresponding knowledge embeddings, enabling knowledge injection during modal interaction. By leveraging graph convolutional networks to enhance graph embeddings, our model improves the quality of generated reports. Experimental results on two widely used benchmark datasets, IU-Xray and MIMIC-CXR, demonstrate the effectiveness of our approach. Notably, our method achieves a BLEU-4 score of 0.185 on the IU-Xray dataset and an F1 score of 0.418 on the MIMIC-CXR dataset, significantly outperforming existing methods.

关键词： Graph convolutional network Hybrid graph Knowledge injection Radiology report generation

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Pneumatic soft bionic actuators inspired by elephant trunk for multidisciplinary robotics applications

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Physica Scripta 2025年第7期100卷 075013-075013页

作者： Xiaohui Guo Deli Kong Zihan Lin Xianghui Li Yupeng Shao Zhaobin Li Yinuo Chen Xinyu Wu Weiqiang Hong Tianxu Zhang Xiaowen Zhu Congguang He Junjie Qiu Xiangchen Zhao Qi Hong Yunong Zhao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Integrated Circuits Anhui University Hefei 230601 People’s Republic of China State Key Laboratory of High-Performance Precision Manufacturing Dalian University of Technology Dalian 116024 People’s Republic of China Key Laboratory for Micro/Nano Technology and System of Liaoning Province Dalian University of Technology Dalian 116024 People’s Republic of China School of Physics University of Science and Technology of China Hefei 230000 People’s Republic of China

Soft robots in technology makes rapid development in the field of robotics, so that robots are widely used in all aspects of life. This paper proposed a bionic elephant trunk structure of pneumatic actuator (SPA). To solve the insufficient tip force and bending angle of SPA, mathematical model and Finite Element Analysis (FEA) are used to determine the optimal parameters of the SPA. Based on optimized SPAs, robots for multidisciplinary applications are developed. These applications include soft gripper with high gripping force of up to 480 N, a quadruped soft robot capable of adapting complex amphibious environments, and a robot designed to assist with sign language communication. These demonstrate the versatility of soft robots in addressing challenges across various domains, such as healthcare, human-machine interaction, and environmental exploration.

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Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

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