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A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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Fast target-aware learning for few-shot video object segmentation

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science China(Information sciences) 2022年第8期65卷 71-86页

作者： Yadang CHEN Chuanyan HAO Zhi-Xin YANG Enhua WU Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and Technology School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of Macau State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of Sciences Faculty of Science and Technology University of Macau

Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video *** proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained.

关键词： video object segmentation few-shot target-aware meta knowledge bilevel-optimization

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Building Holistic Situation Awareness through Large Language Models

Building Holistic Situation Awareness through Large Language...

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Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2023

作者： McDaniel, Emma L. Ruvinsky, Alicia I. Georgia State University Department of Computer Science AtlantaGA United States US Army Engineer Research and Development Center Cybersecurity Engineering and Analysis Branch VicksburgMS United States

In order to design a system to support a users’ situation awareness as they navigate and execute in a complex domain, the information landscape for the domain must be defined. Such a landscape becomes critical for recognizing and evaluating deficiencies in a user’s cognitive processing of the information landscape, and as such, the user’s state of situation awareness. This paper leverages previous work in defining the information landscape for the domain of rotorcraft pilotage, and explores an approach to identifying common deficiencies in a user’s awareness of the information landscape. This paper takes into account prominent frameworks on situation awareness, and presents a methodology leveraging Large Language Models (LLMs) to identify common information deficiencies occurring at various phases of rotorcraft flight through text mining aviation incident and accident reports. Once these deficiencies are identified, the system can provide holistic situation awareness considering a larger data space than the user, and also provide human-centered awareness of prioritized concerns of situation awareness factors and possible mitigations. © 2024, EasyChair. All rights reserved.

关键词： Aircraft accidents

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Toolkit for Satellite Visualization using Auxiliary Data 15

Toolkit for Satellite Visualization using Auxiliary Data

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Veena, O.S. Suhas, G.K. Department of Computer Science & Engineering Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur572106 India Research center for Computer & Information Sciences Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur572106 India

ISBN: (纸本)9798331300579

This paper provides a complete model for satellite tracking that uses Two Line Elements (TLE) data to accurately predict and track satellite locations over *** three-phase graphical user interface of the model allows for real-time viewing of satellite positions and critical tracking data: input, prediction, and *** work offers a comprehensive method for tracking and visualizing satellites in low Earth orbit (LEO), geostationary orbit, and remote sensing orbit utilizing TLE *** latitude and longitude calculations allow for ongoing trajectory mapping, which makes it easier to track satellite movement *** and clarity are further enhanced by visualizing satellite *** their courses, satellite names are added to make identification *** integrated method has significant benefits for applications including space exploration, telecommunication, and environmental ***, it makes constellation dynamics and satellite interactions more understandable, developing satellite-based technologies for a variety of useful uses. © Grenze Scientific Society, 2024.

关键词： Graphical user interfaces

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Wavelength-multiplexed orbital angular momentum meta-holography

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PhotoniX 2024年第1期5卷 276-289页

作者： Jaehyuck Jang Seong-Won Moon Joohoon Kim Jungho Mun Stefan A.Maier Haoran Ren Junsuk Rho Department of Chemical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea Department of Materials Science and Engineering Stanford UniversityStanfordCA 94305USA Department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907USA School of Physics and Astronomy Monash UniversityMelbourneVIC 3800Australia Department of Physics Imperial College LondonLondon SW72AZUK Department of Electrical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea POSCO-POSTECH-RIST Convergence Research Center for Flat Optics and Metaphotonics Pohang 37673Republic of Korea National Institute of Nanomaterials Technology(NINT) Pohang 37673Republic of Korea

The field of high-bandwidth holography has been extensively studied over the past *** angular momentum(OAM)holography,which utilizes vortex beams with theoretically unbounded OAM modes as information carriers,showcases the large capacitance of hologram ***,OAM holography has been limited to a single wavelength,restricting its potential for full-color holography and *** this study,we propose wavelength and OAM multiplexed holography that utilizes the multiple dimensions of light—wavelength and OAM—to provide a multi-color platform that expands the information capacity of holographic storage *** proposed wavelength-OAM multiplexed holography is physically realized by a metasurface,the state-of-the-art optical element consisting of an array of artificially engineered *** silicon meta-atoms,the constituents of the metasurface,are engineered to possess wavelength selectivity by tailoring the dispersion of polarization *** meta-atoms are used to encode the calculated OAM-preserved phase maps based on our *** sampling grid of the phase map is rotated by 45°,which effectively suppress higher-order diffraction,providing a great strategy for achieving large field-of-view(FOV)*** successfully demonstrate six holographic images that are selectively reconstructed under the illumination of light with specific wavelengths(λ=450,635 nm)and topological charges(l=-2,0,2),without high-order *** work suggests that ultrathin meta-holograms can potentially realize ultrahigh-bandwidth full-color holography and holographic video displays with large FOV.

关键词： Metasurface Meta-holography Wavelength-multiplexed holography OAM holography

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Self-assembled microtubular electrodes for on-chip low-voltage electrophoretic manipulation of charged particles and macromolecules

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Microsystems & Nanoengineering 2022年第1期8卷 341-352页

作者： Apratim Khandelwal Nagendra Athreya Michael Q.Tu Lukas L.Janavicius Zhendong Yang Olgica Milenkovic Jean-Pierre Leburton Charles M.Schroeder Xiuling Li Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Department of Chemical Engineering University of Illinois Urbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of Illinois Urbana-ChampaignUrbanaIL 61801USA Department of Electrical and Computer Engineering Microelectronics Research CenterUniversity of TexasAustinTX 78758USA Coordinated Science Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA

On-chip manipulation of charged particles using electrophoresis or electroosmosis is widely used for many applications,including optofluidic sensing,bioanalysis and macromolecular data *** hereby demonstrate a technique for the capture,localization,and release of charged particles and DNA molecules in an aqueous solution using tubular structures enabled by a strain-induced self-rolled-up nanomembrane(S-RuM)***-in 3D electrodes that are embedded in cylindrical S-RuM structures and biased by a constant DC voltage are used to provide a uniform electrical field inside the microtubular *** charged-particle manipulation is achieved at a bias voltage of<2-4 V,which is~3 orders of magnitude lower than the required potential in traditional DC electrophoretic ***,Poisson-Boltzmann multiphysics simulation validates the feasibility and advantage of our microtubular charge manipulation devices over planar and other 3D variations of microfluidic *** work lays the foundation for on-chip DNA manipulation for data storage applications.

关键词： electrodes cylindrical planar

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Dubins Vehicle Intercept of a Brownian Target on a Sphere 63

Dubins Vehicle Intercept of a Brownian Target on a Sphere

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Milutinovic, Dejan Casbeer, David W. University of California Department of Electrical and Computer Engineering Santa CruzCA United States Air Force Research Laboratory Control Science Center Wright-Patterson AFBOH United States

ISBN: (纸本)9798350316339

This paper focuses on a navigation of a Dubins vehicle (DV) to intercept a moving target on a sphere. The uncertainty in the target motion is described by a Brownian motion model. An Itô-type stochastic differential equation for the relative motion between DV and the Brownian moving target is derived. This stochastic differential equation is used to formulate an intercept controller as a minimum time optimal controller. The properties of the controller are incorporated into the numerical computations of the controller, which by its construction can intercept a Brownian target. Our numerical simulation results show that the computed controller can intercept both stationary and moving targets on the sphere. © 2024 IEEE.

关键词： Stochastic systems

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Transferring fashion to surveillance with weak labels

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Neural Computing and Applications 2023年第18期35卷 13021-13035页

作者： Zheng, Qi He, Zheng Liang, Chao Chen, Jun Lin, Chia-Wen Tao, Dapeng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan School of Information Science and Engineering Yunnan University Yunnan Kunming650504 China

In this paper, we address the problem of automatic clothing parsing in surveillance images using the information from user-generated tags, such as "jeans" and "T-shirt." Although clothing parsing has achieved great success in the fashion domain, it is quite challenging to parse target under practical surveillance conditions due to the presence of complex environmental interference, such as that from low resolution, viewpoint variations and lighting changes. Our method is developed to capture target information from the fashion domain and apply this information to a surveillance domain by weakly supervised transfer learning. Most target tags convey strong location information (e.g., "T-shirt" is always shown in the upper region), which can be used as weak labels for our transfer method. Both quantitative and qualitative experiments conducted on practical surveillance datasets demonstrate the effectiveness of the proposed surveillance data enhancing method. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Monitoring

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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