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Average Consensus over Directed Networks in Open Multi-Agent Systems with Acknowledgement Feedback

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arXiv 2024年

作者： Makridis, Evagoras Grammenos, Andreas Oliva, Gabriele Kalyvianaki, Evangelia Hadjicostis, Christoforos N. Charalambous, Themistoklis Department of Electrical and Computer Engineering School of Engineering University of Cyprus Aglantzia Nicosia2109 Cyprus Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo Roma21–00128 Italy

In this paper, we address the distributed average consensus problem over directed networks in open multi-agent systems (OMAS), where the stability of the network is disrupted by frequent agent arrivals and departures, leading to a time-varying average consensus target. To tackle this challenge, we introduce a novel ratio consensus algorithm (OPENRC) based on acknowledgement feedback, designed to be robust to agent arrivals and departures, as well as to unbalanced directed network topologies. We demonstrate that when all active agents execute the OPENRC algorithm, the sum of their state variables remains constant during quiescent epochs when the network remains unchanged. By assuming eventual convergence during such quiescent periods following persistent variations in system composition and size, we prove the convergence of the OPENRC algorithm using column-stochasticity and mass-preservation properties. Finally, we apply and evaluate our proposed algorithm in a simulated environment, where agents are departing from and arriving in the network to highlight its resilience against changes in the network size and topology. © 2024, CC BY.

关键词： Consensus algorithm

Objective diagnosis of ADHD through movement analysis by using a smart chair with piezoelectric material

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Pediatrics and Neonatology 2023年第1期64卷 46-52页

作者： Chang, Tung-Ming Wu, Rong-Ching Yang, Rei-Cheng Chiang, Ching-Tai Chiu, Yi-Hung Ouyang, Chen-Sen Wang, Yun-Ming Wu, Ming-Hsu Lin, Guang-Chung Lin, Lung-Chang Department of Pediatric Neurology Changhua Christian Children's Hospital Changhua Taiwan Department of Electrical Engineering I-Shou University Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Taiwan Department of Computer and Communication National Pingtung University Taiwan Department of Information Engineering I-Shou University Taiwan Department of Biological Science and Technology Institute of Molecular Medicine and Bioengineering Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) National Yang Ming Chiao Tung University Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan

Background: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The diagnosis is made clinically based on observation and information provided by parents and teachers, which is highly subjective and can lead to disparate results. Considering that hyperactivity is one of the main symptoms of ADHD, the inaccuracy of ADHD diagnosis based on subjective criteria necessitates the identification of a method to objectively diagnose ADHD. Methods: In this study, a medical chair containing a piezoelectric material was applied to objectively analyze movements of patients with ADHD, which were compared with those of patients without ADHD. This study enrolled 62 patients—31 patients with ADHD and 31 patients without ADHD. During the clinical evaluation, participants’ movements were recorded by the piezoelectric material for analysis. The variance, zero-crossing rate, and high energy rate of movements were subsequently analyzed. Results: The results revealed that the variance, zero-crossing rate, and high energy rate were significantly higher in patients with ADHD than in those without ADHD. Classification performance was excellent in both groups, with the area under the curve as high as 98.00%. Conclusion: Our findings suggest that the use of a smart chair equipped with piezoelectric material is an objective and potentially useful method for supporting the diagnosis of ADHD. © 2022 Taiwan Pediatric Association

关键词： Attention-deficit/hyperactivity disorder piezoelectric material smart chair

A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images

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Cancer Imaging 2024年第1期24卷 1页

作者： Lee, I-Cheng Tsai, Yung-Ping Lin, Yen-Cheng Chen, Ting-Chun Yen, Chia-Heng Chiu, Nai-Chi Hwang, Hsuen-En Liu, Chien-An Huang, Jia-Guan Lee, Rheun-Chuan Chao, Yee Ho, Shinn-Ying Huang, Yi-Hsiang Division of Gastroenterology and Hepatology Department of Medicine Taipei Veterans General Hospital Taipei Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Computer Science and Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Radiology Taipei Veterans General Hospital Taipei Taiwan National Taiwan University School of Medicine Taipei Taiwan Cancer Center Taipei Veterans General Hospital Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS 2 B) National Yang Ming Chiao Tung University Hsinchu Taiwan College of Health Sciences Kaohsiung Medical University Kaohsiung Taiwan Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan Healthcare and Service Center Taipei Veterans General Hospital Taipei Taiwan

Background: Automatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from dynamic CT images. Methods: Dynamic CT images of 595 patients with HCC were used. Tumors in dynamic CT images were labeled by radiologists. Patients were randomly divided into training, validation and test sets in a ratio of 5:2:3, respectively. We developed a hierarchical fusion strategy of deep learning networks (HFS-Net). Global dice, sensitivity, precision and F1-score were used to measure performance of the HFS-Net model. Results: The 2D DenseU-Net using dynamic CT images was more effective for segmenting small tumors, whereas the 2D U-Net using portal venous phase images was more effective for segmenting large tumors. The HFS-Net model performed better, compared with the single-strategy deep learning models in segmenting small and large tumors. In the test set, the HFS-Net model achieved good performance in identifying HCC on dynamic CT images with global dice of 82.8%. The overall sensitivity, precision and F1-score were 84.3%, 75.5% and 79.6% per slice, respectively, and 92.2%, 93.2% and 92.7% per patient, respectively. The sensitivity in tumors < 2 cm, 2–3, 3–5 cm and > 5 cm were 72.7%, 92.9%, 94.2% and 100% per patient, respectively. Conclusions: The HFS-Net model achieved good performance in the detection and segmentation of HCC from dynamic CT images, which may support radiologic diagnosis and facilitate automatic radiomics analysis. © The Author(s) 2024.

关键词： Computed tomography Deep learning Detection Hepatocellular carcinoma Segmentation

CARDIOCARE platform: A beyond the state of the art approach for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity

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CARDIOCARE platform: A beyond the state of the art approach ...

2023 IEEE International Conference on bioinformatics and biomedicine, BIBM 2023

作者： Tsiouris, Kostas M. Kalliatakis, Grigoris Mazzocco, Ketti Seruga, Bostjan Marias, Kostas Karanasiou, Georgia Antoniades, Athos Papakonstantinou, Andri Conti, Constanza Tsiknakis, Manolis Sfakianakis, Stelios Lakkas, Lampros Filippatos, Gerasimos Bucur, Anca Fotiadis, Dimitrios I. Manikis, George Mauri, Davide Constantinidou, Anastasia Pacella, Elsa University of Ioannina Unit of Medical Technology and Intelligent Information Systems Ioannina Greece Foundation for Research and Technology - Hellas Computational Bio-Medicine Laboratory Institute of Computer Science Heraklion Greece European Institute of Oncology Cardiology Division Milan Italy Institute of Oncology Ljubljana Department of Medical Oncology Ljubljana Slovenia Stremble Ventures Ltd Limassol Cyprus Karolinska University Hospital Endocrine Tumors and Sarcoma Theme Cancer Dept. for Breast Cancer Sweden Istituto di Management Sanitario S.r.l Milano Italy Foundation for Research and Technology - Hellas Institute of Computer Science Heraklion Greece University of Ioannina 2nd Dept. of Cardiology School of Medicine Ioannina Greece National and Kapodistrian University of Athens Athens Greece Attikon University Hospital School of Medicine Dept. of Cardiology Athens Greece Philips Research Europe Eindhoven Netherlands University of Ioannina Dept. of Medical Oncology Ioannina Greece Bank of Cyprus Oncology Centre Nicosia Cyprus European Society of Cardiology Brussels Belgium

ISBN: (纸本)9798350337488

Breast cancer (BC) is the most common cancer in women in Europe and worldwide, with a high prevalence in middle-aged and older women. The last years, the evolution in the existing treatment approaches have contributed to improved clinical outcomes and survival rates. Nevertheless, BC therapy-related cardiotoxicity, poses a severe impact in the short- and long-term Quality of Life (QoL) and associated survival of the BC patients. This study demonstrates how the CARDIOCARE platform and the developed risk stratification models provides healthcare professionals with a valuable tool for effectively managing BC patients, preventing treatment induced cardiotoxicity and improving their QoL. This is accomplished through the integration of multi-source patient-specific data from patient-oriented mobile applications and wearable sensors, and by the employment of beyond the state-of-the-art data mining and machine learning approaches. © 2023 IEEE.

关键词： Breast cancer cardiotoxicity machine learning mobile application risk stratification models

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning

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Protein science 2023年第10期32卷 e4758-e4758页

作者： Yao, Lantian Zhang, Yuntian Li, Wenshuo Chung, Chia-Ru Guan, Jiahui Zhang, Wenyang Chiang, Ying-Chih Lee, Tzong-Yi Kobilka Institute of Innovative Drug Discovery School of Medicine The Chinese University of Hong Kong Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen China Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan

Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1-score of 93.45% on the DeepAFP-Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state-of-the-art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large-scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks. © 2023 The Protein Society.

关键词： antifungal peptides deep learning drug discovery sequence analysis

BPLF: A bi-parallel linear flow model for facial expression generation from emotion set images

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arXiv 2021年

作者： Xu, Gao Long, Yuanpeng Liu, Siwei Yang, Lijia Xu, Shimei Yao, Xiaoming Shu, Kunxian School of Computer Science and Technology Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu611130 China Hetie International Square Building 2 Room404 Sichuan Chengdu China

The flow-based generative model is a deep learning generative model, which obtains the ability to generate data by explicitly learning the data distribution. Theoretically its ability to restore data is stronger than other generative models. However, its implementation has many limitations, including limited model design, too many model parameters and tedious calculation. In this paper, a bi-parallel linear flow model for facial emotion generation from emotion set images is constructed, and a series of improvements have been made in terms of the expression ability of the model and the convergence speed in training. The model is mainly composed of several coupling layers superimposed to form a multi-scale structure, in which each coupling layer contains 1*1 reversible convolution and linear operation modules. Furthermore, this paper sorted out the current public data set of facial emotion images, made a new emotion data, and verified the model through this data set. The experimental results show that, under the traditional convolutional neural network, the 3-layer 3*3 convolution kernel is more conducive to extracte the features of the face images. The introduction of principal component decomposition can improve the convergence speed of the model. © 2021, CC BY.

关键词： Convolution

An optimized capsule-LSTM model for facial expression recognition with video sequences

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arXiv 2021年

作者： Liu, Siwei Long, Yuanpeng Xu, Gao Yang, Lijia Xu, Shimei Yao, Xiaoming Shu, Kunxian School of Computer Science and Technology Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu611130 China *** Hetie International Square Building 2 Room404 Sichuan Chengdu China

To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includingcapsule encoders, capsule decoders and LSTM network. The capsule encoder extracts the spatial information of facial expressions in video frames. Capsule decoder reconstructs the images to optimize the network. LSTM extracts the temporal information between video frames and analyzes the differences in expression changes between frames. The experimental results from the MMI dataset show that the Capsule-LSTM model proposed in this paper can effectively improve the accuracy of video expression recognition. © 2021, CC BY.

关键词： Long short-term memory

CARDIOCARE platform: A beyond the state of the art approach for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity*

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CARDIOCARE platform: A beyond the state of the art approach ...

IEEE International Conference on bioinformatics and biomedicine (BIBM)

作者： Kostas M. Tsiouris Grigoris Kalliatakis Ketti Mazzocco Bostjan Šeruga Kostas Marias Georgia Karanasiou Athos Antoniades Andri Papakonstantinou Constanza Conti Manolis Tsiknakis Stelios Sfakianakis Lampros Lakkas Gerasimos Filippatos Anca Bucur Dimitrios I. Fotiadis George Manikis Davide Mauri Anastasia Constantinidou Elsa Pacella Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Computational Bio-Medicine Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas Heraklion Greece Cardiology Division European Institute of Oncology Milan Italy Department of Medical Oncology Institute of Oncology Ljubljana Ljubljana Slovenia Stremble Ventures Ltd Limassol Cyprus Dept. for Breast Cancer Endocrine Tumors and Sarcoma Theme Cancer Karolinska University Hospital Sweden Istituto di Management Sanitario S.r.l Milano Italy Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion Greece 2nd Dept. of Cardiology School of Medicine University of Ioannina Ioannina Greece National and Kapodistrian University of Athens Athens Greece Dept. of Cardiology School of Medicine Attikon University Hospital Athens Greece Philips Research Europe Eindhoven Netherlands Dept. of Medical Oncology University of Ioannina Ioannina Greece Bank of Cyprus Oncology Centre Nicosia Cyprus European Society of Cardiology Brussels Belgium

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RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-based Semantic Segmentation

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arXiv 2021年

作者： He, Shuang Lu, Xia Gu, Jason Tang, Haitong Yu, Qin Liu, Kaiyue Ding, Haozhou Chang, Chunqi Wang, Nizhuan Jiangsu Key Laboratory of Marine Bioresources and Environment Jiangsu Key Laboratory of Marine Biotechnology Co-Innovation Center of Jiangsu Marine Bio-Industry Technology Jiangsu Ocean University Lianyungang222005 China School of Geomatics and Marine Information Jiangsu Ocean University Lianyungang222005 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing211800 China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518055 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China

For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important. How to get the trade-off effectively is an open question, where current approaches of utilizing very deep models result in complex models with large memory consumption. In contrast to previous work that utilizes dilated convolutions or deep models, we propose a novel two-stream deep neural network for semantic segmentation of RSI (RSI-Net) to obtain improved performance through modeling and propagating spatial contextual structure effectively and a decoding scheme with image-level and graph-level combination. The first component explicitly models correlations between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions by using graph convolutional network, while densely connected atrous convolution network (DenseAtrousCNet) with multi-scale atrous convolution can expand the receptive fields and obtain image global information. Extensive experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets, where the comparison results demonstrate the superior performance of RSI-Net in terms of overall accuracy (91.83%, 93.31% and 93.67% on three datasets, respectively), F1 score (90.3%, 91.49% and 89.35% on three datasets, respectively) and kappa coefficient (89.46%, 90.46% and 90.37% on three datasets, respectively) when compared with six state-of-the-art RSI semantic segmentation methods. © 2021, CC BY-NC-ND.

关键词： Semantic Segmentation