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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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A Cyber-Physical Smart Grid Testbed for Validation of GOOSE-Based Protection Strategies

A Cyber-Physical Smart Grid Testbed for Validation of GOOSE-...

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2022 IEEE Industry Applications Society Annual Meeting, IAS 2022

作者： Abdelrahman, Mahmoud S. Kharchouf, Ibtissam Alrashide, Abdulmueen Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (数字)9781665478151

ISBN: (纸本)9781665478151

Although smart grid technologies bring valuable economic, social, and environmental benefits, testing the combination of heterogeneous and co-existing cyber-physical grid structure with legacy technologies presents many challenges. To overcome these challenges without adversely affecting the real-time environment, testbeds are required to explore, develop, evaluate, and validate security controls and protection algorithms in the power system. This paper presents an improved hardware/software design of the smart grid testbed at the Energy systems Research laboratory, Florida International University. The hardware/software-based system includes implementing protection and control strategies for power systems with industrial-grade commercial devices to represent the holistic real-time operation of smart power grid. By leveraging resources available at FIU's smart grid testbed, an in-depth experimental analysis is performed to demonstrate and verify the successful operation of the GOOSE publish-subscribe protocol towards the substation protection within the smart grid setup. © 2022 IEEE.

关键词： Smart power grids

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Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Subwavelength Fano Resonant Porous Silicon Metasurfaces for Sensing and Dynamic Structural Color

Subwavelength Fano Resonant Porous Silicon Metasurfaces for ...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Gafsi, Saddam Dos Santos, Estevao Marques Kravchenko, Ivan Ryckman, Judson D. Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Center for Nanophase Materials Science Oak Ridge National Laboratory Oak RidgeTN37831 United States

We introduce the design, fabrication, and experimental investigation of subwavelength Fano resonant porous silicon metasurfaces functioning on the principle of guided mode resonance. These metasurfaces exhibit promise... 详细信息

关键词： Porous materials

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Towards an Analytical Definition of Sufficient Data

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SN computer science 2023年第2期4卷 144页

作者： Byerly, Adam Kalganova, Tatiana Department of Electronic and Electrical Engineering Brunel University London Uxbridge UB8 3PH United Kingdom Department of Computer Science and Information Systems Bradley University Peoria 61615 IL United States

We show that, for each of five datasets of increasing complexity, certain training samples are more informative of class membership than others. These samples can be identified a priori to training by analyzing their position in reduced dimensional space relative to the classes’ centroids. Specifically, we demonstrate that for the datasets studied, samples that are nearer to their classes’ centroids are less informative than those that are furthest from them. For all five datasets studied, we show that there is no statistically significant difference between training on the entire training set and when excluding up to 2% of the data nearest to each class’s centroid. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Class separation Data reduction Dataset severability Dimensional reduction UMAP

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Deep CNN Model for Multimodal Medical Image Denoising

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computers, Materials & Continua 2022年第11期73卷 3795-3814页

作者： Walid El-Shafai Amira A.Mahmoud Anas M.Ali El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron Emission Tomography(PET).These techniques are organized into two main classes:the Multiple Image(MI)and the Single Image(SI)*** the MI techniques,images usually obtained for the same area scanned from different points of view are used.A single image is used in the entire procedure in the SI *** denoising techniques can be carried out both in a transform or spatial *** paper is concerned with single-image noise reduction techniques because we deal with single medical *** most well-known spatial domain noise reduction techniques,including Gaussian filter,Kuan filter,Frost filter,Lee filter,Gabor filter,Median filter,Homomorphic filter,Speckle reducing anisotropic diffusion(SRAD),Nonlocal-Means(NL-Means),and Total Variation(TV),are ***,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are ***,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image *** model utilizes the Batch Normalization(BN)and the ReLU as a basic *** a result,it attained a considerable improvement over the traditional *** previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),and system complexity to determine the optimum denoising algorithm to be applied *** on the quality metrics,it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditionaldenoising techniques.

关键词： Image enhancement medical imaging speckle noise Gaussian noise denoising filters CNN denoising

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A Multi-Sequence Prophet Inequality Under Observation Constraints

A Multi-Sequence Prophet Inequality Under Observation Constr...

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IEEE International Symposium on Information Theory

作者： Aristomenis Tsopelakos Olgica Milenkovic Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In our problem, we are given access to a number of sequences of nonnegative i.i.d. random variables, whose realizations are observed sequentially. All sequences are of the same finite length. The goal is to pick one element from each sequence in order to maximize a reward equal to the expected value of the sum of the selections from all sequences. The decision on which element to pick is irrevocable, i.e., rejected observations cannot be revisited. Furthermore, the procedure terminates upon having a single selection from each sequence. Our observation constraint is that we cannot observe the current realization of all sequences at each time instant. Instead, we can observe only a smaller, yet arbitrary, subset of them. Thus, together with a stopping rule that determines whether we choose or reject the sample, the solution requires a sampling rule that determines which sequence to observe at each instant. The problem can be solved via dynamic programming, but with an exponential complexity in the length of the sequences. In order to make the solution computationally tractable, we introduce a decoupling approach and determine each stopping time using either a single-sequence dynamic programming, or a Prophet Inequality inspired threshold method, with polynomial complexity in the length of the sequences. We prove that the decoupling approach guarantees at least 0.745 of the optimal expected reward of the joint problem. In addition, we describe how to efficiently compute the optimal number of samples for each sequence, and its' dependence on the variances.

关键词： Polynomials Finite element analysis Dynamic programming Complexity theory Random variables Information theory

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Tuning the number of chiral edge channels in a fixed quantum anomalous Hall system

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Physical Review B 2024年第20期109卷 L201402-L201402页

作者： Peng Deng Yulei Han Peng Zhang Su Kong Chong Zhenhua Qiao Kang L. Wang Beijing Academy of Quantum Information Sciences Beijing 100193 China Department of Electrical and Computer Engineering University of California Los Angeles California 90095 USA Department of Physics Fuzhou University Fuzhou Fujian 350108 China International Center for Quantum Design of Functional Materials CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics and Department of Physics University of Science and Technology of China Anhui 230026 China Hefei National Laboratory University of Science and Technology of China Hefei 230088 China

Quantum anomalous Hall (QAH) insulators exhibit chiral edge channels characterized by vanishing longitudinal conductance and quantized Hall conductance of Ce2/h, wherein the Chern number C is an integer equal to the number of the parallel chiral edge channels. These chiral edge channels conduct dissipationless transport in QAH insulators, making them pivotal for applications in low-consumption electronics and topological quantum computing. While the QAH effect with multiple chiral edge channels (i.e., C > 1) has been demonstrated in multilayers consisting of magnetic topological insulators and normal insulators, the channel number remains fixed for a given sample. Here, we unveil the tunability of the number of chiral edge channels within a single QAH insulator device. By tuning the magnetization of individual layers within the multilayer system, Chern insulating states with different Chern numbers are unveiled. The tunable Chern number was corroborated by our theoretical calculations. Furthermore, we conducted layer-dependent calculations to elucidate the contribution of the Chern number from different layers in the multilayer. Our findings demonstrate an extra degree of freedom in manipulating the chiral edge channels in QAH insulators. This tunability offers an extra dimension for the implementation of the QAH-based multichannel dissipationless transport.

关键词： Quantum anomalous Hall effect Quantum transport Topological insulators Molecular beam epitaxy

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An Efficient Federated Transfer Learning Approach for Multi-UAV systems

An Efficient Federated Transfer Learning Approach for Multi-...

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National Conference on Communications (NCC)

作者： Vedang Bhupesh Shenvi Nadkarni Sandeep Joshi L. Rajya Lakshmi Department of Computer Science and Information Systems Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Rajasthan India

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

Recent advances in multi-unmanned aerial vehicle (UAV) based federated learning do not take into consideration the massive computational requirements of modern deep learning models on mobile UAV s. Additionally, there has been significant progress that shows that the information transmitted between the federated agent and the central hub can be attacked to undermine the privacy of the data. We propose a novel multi-UAV-based federated transfer learning system that drastically reduces the computational burden overall, shifts it from UAV s to the ground fusion center, and reduces the bandwidth requirements while enhancing its secure nature. The proposed system makes multi-UAV learning significantly fast, reliable, power efficient, and practically feasible. Furthermore, we provide simulation and experimental results to demonstrate the effectiveness of the proposed system.

关键词： Deep learning Image segmentation Data privacy Federated learning Power supplies Transfer learning Object detection Autonomous aerial vehicles Data models Reliability

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A Deep Learning Approach for Satellite and Debris Detection: YOLO in Action 26

A Deep Learning Approach for Satellite and Debris Detection:...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahamed, Md Younus Bin Syed, Md Asif Chatterjee, Paroma Bin Habib, Al Zadid Sultan West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States

ISBN: (纸本)9798350359015

Satellite detection refers to the identification and recognition of satellites in satellite images. This task is crucial for various applications, including space surveillance, satellite tracking, and space debris monitoring. Several studies have been conducted to develop methods and techniques for satellite detection. The intricacy and scale of satellite images make satellite detection incredibly challenging. The process of manual identification and classification necessitates a significant amount of time. Recently, Deep Learning (DL) has emerged as a highly promising approach in computer Vision (CV) tasks. This study evaluates the effectiveness of three widely employed DL-based object detection algorithms, specifically YOLOv4, YOLOv5, and YOLOv8, within the field of satellite detection. The algorithms have undergone training utilizing a dataset of satellite photos comprising both satellites and debris. The evaluation of the algorithm's performance involved the utilization of broadly accepted metrics, including Precision, Recall, Mean Average Precision (mAP), Box Loss, and Class Loss. The findings indicated that all three algorithms exhibited a high level of accuracy in detecting and precisely determining the location of objects inside satellite data. The performance of YOLOv5 and YOLOv8 surpassed that of YOLOv4 across various measures, with a notable advantage observed in detecting tiny objects. Overall, this study demonstrates the capabilities of DL algorithms in the context of satellite detection and classification techniques. © 2023 IEEE.

关键词： Debris Object Detection Satellite YOLOv4 YOLOv5 YOLOv8

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