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Real-Time Camera Localization during Robot-Assisted Telecystoscopy for Bladder Cancer Surveillance

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Journal of Medical Robotics Research 2022年第2-3期7卷

作者： Gong, Chen Zhou, Yaxuan Lewis, Andrew Chen, Pengcheng Speich, Jason R. Porter, Michael P. Hannaford, Blake Seibel, Eric J. Mechanical Engineering University of Washington 3900 E Stevens Way NE Seattle 98195 WA United States Electrical and Computer Engineering University of Washington 185 W Stevens Way NE Seattle 98195 WA United States Center for Research and Education in Simulation Technologies (CREST) University of Washington 1959 NE Pacific St. Seattle 98195 WA United States Department of Urology University of Washington 1959 NE Pacific St. Seattle 98195 WA United States

Telecystoscopy can lower the barrier to access critical urologic diagnostics for patients around the world. A major challenge for robotic control of flexible cystoscopes and intuitive teleoperation is the pose estimation of the scope tip. We propose a novel real-time camera localization method using video recordings from a prior cystoscopy and 3D bladder reconstruction to estimate cystoscope pose within the bladder during follow-up telecystoscopy. We map prior video frames into a low-dimensional space as a dictionary so that a new image can be likewise mapped to efficiently retrieve its nearest neighbor among the dictionary images. The cystoscope pose is then estimated by the correspondence among the new image, its nearest dictionary image, and the prior model from 3D reconstruction. We demonstrate performance of our methods using bladder phantoms with varying fidelity and a servo-controlled cystoscope to simulate the use case of bladder surveillance through telecystoscopy. The servo-controlled cystoscope with 3 degrees of freedom (angulation, roll, and insertion axes) was developed for collecting cystoscope videos from bladder phantoms. Cystoscope videos were acquired in a 2.5D bladder phantom (bladder-shape cross-section plus height) with a panorama of a urothelium attached to the inner surface. Scans of the 2.5D phantom were performed in separate arc trajectories each of which is generated by actuation on the angulation with a fixed roll and insertion length. We further included variance in moving speed, imaging distance and existence of bladder tumors. Cystoscope videos were also acquired in a water-filled 3D silicone bladder phantom with hand-painted vasculature. Scans of the 3D phantom were performed in separate circle trajectories each of which is generated by actuation on the roll axis under a fixed angulation and insertion length. These videos were used to create 3D reconstructions, dictionary sets, and test data sets for evaluating the computational effici

关键词： 3D reconstruction camera re-localization image retrieval Telecystoscopy telemedicine

Optimizing Multi-Hop Quantum Communication using Bidirectional Quantum Teleportation Protocol

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arXiv 2025年

作者： Ikken, N. Kumar, P. Slaoui, A. Kar, B. Laamara, R. Ahl Almousa, M. El-Latif, A.A. Abd LPHE-Modeling and Simulation Faculty of Sciences Mohammed V University Rabat Morocco Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Centre of Physics and Mathematics CPM Faculty of Sciences Mohammed V University in Rabat Rabat Morocco EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

In this paper, we introduce a new method for Bidirectional Quantum Teleportation called Bidirectional Quantum Teleportation using the Modified Dijkstra Algorithm and Quantum Walk (BQT-MDQW). This method uses different types of entangled states, such as the GHZ-Bell state, W-Bell state, and Cluster-Bell state, to improve quantum communication in multi-hop quantum wireless networks. We focus on the W-Bell state and compare the quantum Dijkstra algorithm with the classical Dijkstra method to see which one works better. We apply both versions to quantum and classical simulators, measuring their performance through fidelity, memory utilization, and throughput calculations. Our results show that the shortest path problem may be solved with significantly reduced computer complexity using the quantum Dijkstra algorithm based on quantum walks. The introduction of a quantum walk, which permits dynamic transitions between quantum channels and the effective exploration of quantum network states, is an important part of the protocol. Using the capacity of the quantum walk to adjust to changing quantum states, we also introduce a method for successfully identifying unitary matrices under varying quantum channels. The bidirectional teleportation structure of the protocol is designed to solve the multi-hop teleportation problem in quantum wireless networks. In addition, we present quantum Dijkstra’s algorithm, which uses quantum gates to significantly decrease computational complexity and solve the networking problem by building on the quantum walk framework. This method shows how quantum computing may be used to solve arbitrary optimization issues such as the shortest path problem. Finally, we present a novel multi-hop quantum teleportation system encompassing both unidirectional and bidirectional communication, as introduced in the quantum Dijkstra algorithm system. This system significantly enhances communication in quantum networks by enabling efficient and reliable information

关键词： Quantum channel

Transferring orbital angular momentum to an electron beam reveals toroidal and chiral order

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Physical Review B 2023年第20期107卷 205419-205419页

作者： K. X. Nguyen Y. Jiang M. C. Cao P. Purohit A. K. Yadav P. García-Fernández M. W. Tate C. S. Chang P. Aguado-Puente J. Íñiguez F. Gomez-Ortiz S. M. Gruner J. Junquera L. W. Martin R. Ramesh D. A. Muller Department of Chemistry and Chemical Biology Cornell University Ithaca 14853 New York USA School of Applied and Engineering Physics Cornell University Ithaca 14853 New York USA Department of Physics Cornell University Ithaca 14853 New York USA Department of Electrical Engineering and Computer Sciences University of California Berkeley 94720 California USA Departamento de Ciencias de la Tierra y Física de la Materia Condensada Universidad de Cantabria Cantabria Campus Internacional Avenida de los Castros s/n 39005 Santander Cantabria Spain Atomistic Simulation Center Queen's University Belfast Belfast BT7 1NN Northern Ireland United Kingdom CIC nanoGUNE BRTA E-20018 Donostia-San Sebastian Spain Materials Research and Technology Department Luxembourg Institute of Science and Technology (LIST) 5 avenue des Hauts-Fourneaux Esch-Sur-Alzette L-4362 Luxembourg Physics and Materials Science Research Unit University of Luxembourg 41 Rue du Brill Belvaux L-4422 Luxembourg Kavli Institute at Cornell for Nanoscale Science Ithaca 14853 New York USA Cornell High Energy Synchrotron Source (CHESS) Cornell University Ithaca 14853 New York USA Department of Material Science and Engineering University of California Berkeley Berkeley 94720 California USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley 94720 California USA Department of Physics University of California Berkeley 94720 California USA

Orbital angular momentum (OAM) and torque transfer play central roles in a wide range of magnetic textures and devices including skyrmions and spin-torque electronics. Analogous topological structures are now also being explored in ferroelectrics, including polarization vortex arrays in ferroelectric/dielectric superlattices. Unlike magnetic toroidal order, electric toroidal order does not couple directly to linear external fields. Instead, we find that the presence of an electric toroidal moment in a ferrorotational phase transfers measurable torque and OAM to a localized electron beam in the ballistic limit. We record these torque transfers from a high-energy electron beam using a momentum-resolved detector. This approach provides a high-sensitivity method to detect polarization fields and their more complex order parameters and topologies. In addition to toroidal order, we also demonstrate high-precision measurements of vorticity and chirality for polar vortexlike phases.

关键词： Chirality Electric polarization Ferroelectricity Ferroelectrics Angular momentum Electron diffraction Electron microscopy Scanning transmission electron microscopy

Climate warming restructures seasonal dynamics of grassland soil microbial communities

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mLife 2022年第3期1卷 245-256页

作者： Xue Guo Mengting Yuan Jiesi Lei Zhou Shi Xishu Zhou Jiabao Li Ye Deng Yunfeng Yang Liyou Wu Yiqi Luo James MTiedje Jizhong Zhou State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijingChina Institute for Environmental Genomics and Department of Microbiology and Plant Biology University of OklahomaNormanOklahomaUSA Department of Environmental Science Policyand ManagementUniversity of CaliforniaBerkeleyCaliforniaUSA Gladstone Institutes and Chan‐Zuckerberg Biohub San FranciscoCaliforniaUSA School of Minerals Processing and Bioengineering Central South UniversityChangshaChina Key Laboratory of Environmental and Applied Microbiology Chinese Academy of Sciences and Environmental Microbiology&Key Laboratory of Sichuan ProvinceChengdu Institute of BiologyChinese Academy of SciencesChengduChina Key Laboratory of Environmental Biotechnology Research Center for Eco‐Environmental SciencesChinese Academy of SciencesBeijingChina Department of Biological Sciences Center for Ecosystem Science and SocietyNorthern Arizona UniversityFlagstaffArizonaUSA Center for Microbial Ecology Michigan State UniversityEast LansingMichiganUSA School of Civil Engineering and Environmental Sciences University of OklahomaNormanOklahomaUSA Earth and Environmental Sciences Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA School of Computer Sciences University of OklahomaNormanOklahomaUSA.

Soil microbial community's responses to climate warming alter the global carbon *** temperate ecosystems,soil microbial communities function along seasonal ***,little is known about how the responses of soil microbial communities to warming vary when the season *** this study,we investigated the seasonal dynamics of soil bacterial community under experimental warming in a temperate tall‐grass prairie *** results showed that warming significantly(p=0.001)shifted community structure,such that the differences of microbial communities between warming and control plots increased nonlinearly(R^(2)=0.578,p=0.021)from spring to ***,warming significantly(p<0.050)increased microbial network complexity and robustness,especially during the colder seasons,despite large variations in network size and complexity in different *** addition,the relative importance of stochastic processes in shaping the microbial community decreased by warming in fall and winter but not in spring and *** study indicates that climate warming restructures the seasonal dynamics of soil microbial community in a temperate *** seasonality of microbial responses to warming may enlarge over time and could have significant impacts on the terrestrial carbon cycle.

关键词： climate warming microbial community microbial network seasonal dynamics

State estimation-based robust optimal control of influenza epidemics in an interactive human society

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arXiv 2020年

作者： Azimi, Vahid Sharifi, Mojtaba Fakoorian, Seyed Nguyen, Thang Tien Department of Energy Resources Engineering Stanford University StanfordCA United States Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH United States Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical & Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam

This paper presents a state estimation-based robust optimal control strategy for influenza epidemics in an interactive human society in the presence of modeling uncertainties. Interactive society is influenced by random entrance of individuals from other human societies whose effects can be modeled as a non-Gaussian noise. Since only the number of exposed and infected humans can be measured, states of the influenza epidemics are first estimated by an extended maximum correntropy Kalman filter (EMCKF) to provide a robust state estimation in the presence of the non-Gaussian noise. An online quadratic program (QP) optimization is then synthesized subject to a robust control Lyapunov function (RCLF) to minimize susceptible and infected humans, while minimizing and bounding the rates of vaccination and antiviral treatment. The joint QP-RCLF-EMCKF meets multiple design specifications such as state estimation, tracking, pointwise control optimality, and robustness to parameter uncertainty and state estimation errors that have not been achieved simultaneously in previous studies. The uniform ultimate boundedness (UUB)/convergence of error trajectories is guaranteed using a Lyapunov stability argument. The soundness of the proposed approach is validated on the influenza epidemics of an interactive human society with population of 16000. simulation results show that the QP-RCLF-EMCKF achieves appropriate tracking and state estimation performance. Robustness of the proposed controller is finally illustrated in the presence of modeling error and non-Gaussian noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Robust control

An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

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Applied Energy 2022年 319卷

作者： Jiaqi Qu Zheng Qian Yan Pei Lu Wei Hamidreza Zareipour Qiang Sun School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

关键词： Photovoltaic systems performance Hourly fault detection Unsupervised hourly weather status pattern recognition Blending fitting model

A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification

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arXiv 2022年

作者： Li, Heng-Chao Hu, Wen-Shuai Li, Wei Li, Jun Du, Qian Plaza, Antonio The Sichuan Provincial Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu611756 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation School of Geography and Planning Sun Yat-Sen University Guangzhou510275 China The Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS39762 United States The Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10060 Spain

The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing. In this paper, we propose a new approach to exploit the complementarity of two data sources: hyperspectral images (HSIs) and light detection and ranging (LiDAR) data. Specifically, we develop a new dual-channel spatial, spectral and multiscale attention convolutional long short-term memory neural network (called dual-channel A3CLNN) for feature extraction and classification of multisource remote sensing data. Spatial, spectral and multiscale attention mechanisms are first designed for HSI and LiDAR data in order to learn spectral- and spatial-enhanced feature representations, and to represent multiscale information for different classes. In the designed fusion network, a novel composite attention learning mechanism (combined with a three-level fusion strategy) is used to fully integrate the features in these two data sources. Finally, inspired by the idea of transfer learning, a novel stepwise training strategy is designed to yield a final classification result. Our experimental results, conducted on several multisource remote sensing data sets, demonstrate that the newly proposed dual-channel A3CLNN exhibits better feature representation ability (leading to more competitive classification performance) than other state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

Erratum to “Modeling Particle Transport in Astrophysical Outflows and simulations of Associated Emissions from Hadronic Microquasar Jets”

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Advances in High Energy Physics 2023年第1期2023卷

作者： D. A. Papadopoulos O. T. Kosmas S. Ganatsios Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greeceuowm.gr Theoretical Physics Section University of Ioannina GR-45110 Ioannina Greeceuoi.gr Modelling and Simulation Center MACE University of Manchester Sackville Street Manchester UKmanchester.ac.uk