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arXiv 2021年

作者： Liu, Yuhan Tóth, Roland The Harbin Institute of Technology Department of Control Science and Engineering Harbin China The Eindhoven University of Tech. Dep. of Electrical Eng. Control Systems Group Eindhoven Netherlands The Institute for Computer Science and Control Budapest Hungary

An important issue in quadcopter control is that an accurate dynamic model of the system is nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation is an effective tool to learn unknown dynamics from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control strategy that improves the performance of a quadcopter during trajectory tracking. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation in an efficient way. Numerical simulations are used to demonstrate the effectiveness of the proposed strategy. © 2021, CC BY-NC-SA.

关键词： Model predictive control

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks

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Physical Review Applied 2022年第6期17卷 064025-064025页

作者： Henri Tertilt Jesse Bakker Marlon Becker Bram de Wilde Indrek Klanberg Bernard J. Geurts Wilfred G. van der Wiel Andreas Heuer Peter A. Bobbert Institut für physikalische Chemie Westfälische Wilhelms-Universität Münster Münster Germany NanoElectronics Group University of Twente Enschede Netherlands Multiscale Modeling and Simulation Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) University of Twente Enschede Netherlands Center for Computational Energy Research (CCER) Eindhoven University of Technology Netherlands Fachbereich Physik Westfälische Wilhelms-Universität Münster Münster Germany Department of Applied Physics Eindhoven University of Technology Netherlands

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic in dopant network processing units. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics and artificially evolved basic Boolean logic gates in very good agreement with experiment. The simulations provide unique insights into the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries.

关键词： Doping effects Hopping transport Neuromorphic computing Devices for digital logic, storage & processing Disordered systems Doped semiconductors Genetic algorithm Monte Carlo methods

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Fast Hildreth-based Model Predictive Control of Roll Angle for a Fixed-Wing UAV

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IFAC-PapersOnLine 2020年第2期53卷 5757-5763页

作者： V.T.T. Lam A. Sattar L. Wang M. Lazar Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands School of Engineering Cluster Electrical and Computer Engineering RMIT University Melbourne Australia School of Engineering Cluster Electrical and Biomedical Engineering RMIT University Melbourne Australia

In this paper we consider model predictive control (MPC) design for roll angle control for a fixed-wing unmanned aerial vehicle (UAV) with multiple segmented control surfaces. The challenge of roll angle control for a fixed-wing UAV consists of switching between inner and outer aileron pairs with hard constraints due to safety, energy saving and switching actuators. The novelty consists of formulating a hybrid control problem as a switched linear constrained MPC-QP problem and switched state observer design for fixed-wing UAV. A fast novel QP-solver based on the active-set QP-solver Hildreth is developed to meet the real-time implementation sampling time of T s = 10 ms. The designed MPC controllers are simulated using Matlab. Simulations and the CPU-time from the improved QP-solvers show MPC to be a very good solution for real-time roll angle control of fixed-wing UAVs.

关键词： Model Predictive Control Quadratic Programming Fixed-Wing Unmanned Aerial Vehicle Active Set Methods

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RECASNET: IMPROVING CONSISTENCY WITHIN THE TWO-STAGE MITOSIS DETECTION FRAMEWORK

arXiv

引用

arXiv 2022年

作者： Piansaddhayanon, Chawan Santisukwongchote, Sakun Shuangshoti, Shanop Tao, Qingyi Sriswasdi, Sira Chuangsuwanich, Ekapol Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand Chula Intelligent and Complex Systems Faculty of Science Chulalongkorn University Thailand Department of Pathology King Chulalongkorn Memorial Hospital Faculty of Medicine Chulalongkorn University Thailand NVIDIA AI Technology Center Singapore Computational Molecular Biology Group Faculty of Medicine Chulalongkorn University Thailand Center of Excellence in Computational Molecular Biology Faculty of Medicine Chulalongkorn University Thailand

Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is very time-consuming and prone to error. Therefore, deep learning models have been proposed to facilitate this process. Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences. However, this pipeline formulation can lead to inconsistencies in the classification stage due to the poor prediction quality of the detection stage and the mismatches in training data distributions between the two stages. In this study, we propose a Refine Cascade Network (ReCasNet), an enhanced deep learning pipeline that mitigates the aforementioned problems with three improvements. First, window relocation was used to reduce the number of poor quality false positives generated during the detection stage. Second, object re-cropping was performed with another deep learning model to adjust poorly centered objects. Third, improved data selection strategies were introduced during the classification stage to reduce the mismatches in training data distributions. ReCasNet was evaluated on two large-scale mitotic figure recognition datasets, canine cutaneous mast cell tumor (CCMCT) and canine mammary carcinoma (CMC), which resulted in up to 4.8% percentage point improvements in the F1 scores for mitotic cell detection and 44.1% reductions in mean absolute percentage error (MAPE) for MC prediction. Techniques that underlie ReCasNet can be generalized to other two-stage object detection networks and should contribute to improving the performances of deep learning models in broad digital pathology applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Application of Graph-Curvature Features in Auxiliary Diagnosis for Histopathological Image Identification of Gastric Cancer

SSRN

引用

SSRN 2023年

作者： He, Ruilin Li, Chen Yang, Xinyi Yang, Jinzhu Jiang, Tao Grzegorzek, Marcin Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors, but it has some drawbacks. This paper explores a computer-aided diagnostic method that can identify benign and malignant gastric cancer with histopathology images. Method: This article obtains the most suitable process through multiple experiments, compared multiple methods and features for classification. Firstly, the U-net is applied to segment the image. Next, the nucleus is extracted from the segmented image and the Minimum Spanning Tree (MST) diagram structure is drawn. The third step is to extract the graph-curvature features of histopathology image according to the MST image. Finally, by inputting graph-curvature features into the classifier, the recognition results for benign or malignant can be obtained. Result: During the experiment, we use various methods for comparison. In the image segmentation stage, U-net, watershed algorithm and Otsu threshold segmentation methods are used respectively. Combined with multiple indicators, we find that the U-net method is the most suitable for segmentation of histopathology images. In the feature extraction stage, in addition to extracting graph-edge feature and graph-curvature feature, several basic image features are also extracted, including Red, Green, Blue feature, Gray-Level Co-occurrence Matrix feature, Histogram of Oriented Gradient feature, and Local Binary Pattern feature. In the classifier design stage, we experimented with various methods, such as Support vector machine (SVM), Random forest, Artificial Neural Network, K Nearest Neighbors, VGG-16 and Inception-V3. Through the comparison and analysis, the classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into SVM classifier. Conclusion: This paper has created a unique feature, graph-curvature feature based on MST to represent and analyze histopathology images. This graph-based feature can be used

关键词： Local binary pattern

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Application-level Benchmarking of Quantum computers using Nonlocal Game Strategies

arXiv

引用

arXiv 2023年

作者： Furches, Jim Chehade, Sarah Hamilton, Kathleen Wiebe, Nathan Marrero, Carlos Ortiz College of Computing Georgia Institute of Technology AtlantaGA30332 United States Physical Detection Systems and Deployment Division Pacific Northwest National Laboratory RichlandWA99354 United States Quantum Computational Science Group Oak Ridge National Laboratory Oak RidgeTN37830 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States

In a nonlocal game, two noncommunicating players cooperate to convince a referee that they possess a strategy that does not violate the rules of the game. Quantum strategies allow players to optimally win some games by performing joint measurements on a shared entangled state, but computing these strategies can be challenging. We present a variational quantum algorithm to compute quantum strategies for nonlocal games by encoding the rules of a nonlocal game into a Hamiltonian. We show how this algorithm can generate a short-depth optimal quantum strategy for a graph coloring game with a quantum advantage. This quantum strategy is then evaluated on fourteen different quantum hardware platforms to demonstrate its utility as a benchmark. Finally, we discuss potential sources of errors that can explain the observed decreased performance of the executed task and derive an expression for the number of samples required to accurately estimate the win rate in the presence of noise. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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ARNA, a Service robot for Nursing Assistance: System Overview and User Acceptability

ARNA, a Service robot for Nursing Assistance: System Overvie...

引用

IEEE International Conference on Automation Science and engineering (CASE)

作者： Shamsudeen Abubakar Sumit K. Das Chris Robinson Mohammed N. Saadatzi M. Cynthia Logsdon Heather Mitchell Diane Chlebowy Dan O. Popa Department of Electrical and Computer Engineering Next Generation Systems Group Kentucky USA School of Nursing at University of Louisville Kentucky USA

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Using robots capable of collaboration with humans to complete physical tasks in unstructured spaces is a rapidly growing approach to work. Particular examples where increased levels of automation can increase productivity include robots used as nursing assistants. In this paper, we present a mobile manipulator designed to serve as an assistant to nurses in patient walking and patient sitting tasks in hospital environments. The Adaptive Robotic Nursing Assistant (ARNA) robot consists of an omnidirectional base with an instrumented handlebar, and a 7-DOF robotic arm. We describe its components and the novelties in its mechanisms and instrumentation. Experiments with human subjects that gauge the usability and ease of use of the ARNA robot in a medical environment indicate that the robot will get significant actual usage, and are used as a basis for a discussion on how the robot's features facilitate its adaptability for use in other scenarios and environment.

关键词： Robot sensing systems Task analysis Service robots Medical services Collision avoidance Manipulators

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Common-mode suppression in broadside coupled coplanar waveguides

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Progress In Electromagnetics Research C 2019年 92卷 113-121页

作者： He, Yujie Faia, Joseph M. Cracraft, Michael Wheeler, Edward Department of Electrical and Computer Engineering Rose-Hulman Institute of Technology 5500 Wabash Ave Terre HauteIN47803 United States IBM Systems and Technology Group PoughkeepsieNY12603 United States

Differential signaling is used in digital circuitry and high speed communication links due to its lower level of radiation and lower susceptibility to interference. Signal skew, amplitude differences and unequal parasitic electric or magnetic coupling to nearby structures can lead to common-mode signals being present on differential communication links which can result in unwanted electromagnetic interference and crosstalk. Common-mode filtering is often employed to suppress common-mode signal propagation in order to mitigate against these negative effects. In this paper broadside coupled differential coplanar waveguides are used which provide effective differential transmission from dc through 40GHz. Simulation and measurement show that dipole-like common-mode filtering elements placed between the broadside coupled traces offer common-mode suppression of more than 10 dB over bandwidths greater than 5GHz. A design equation is developed which can be used to estimate filtering frequencies from filter dimensions through 30GHz. Filters can be cascaded to broaden filtering around a single frequency to filter at multiple frequencies. Simulation based registration studies were conducted which show stable filtering performance in the presence of layer-to-layer misregistration up to 0.254mm. © 2019 Electromagnetics Academy. All rights reserved.

关键词： Coplanar waveguides

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Investigating the interrelationships between the barriers of green computing adoption: TISM-MICMAC-DEMATEL method

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Quality and Quantity 2024年第1期59卷 507-570页

作者： Dalvi-Esfahani, Mohammad Nilashi, Mehrbakhsh Abumalloh, Rabab Ali Thurasamy, Ramayah Ahmad, Muhammad Shakil Blockchain Fintech and Cryptocurrency Research Group University of Southampton Malaysia Johor Iskandar Puteri 79100 Malaysia UCSI Graduate Business School UCSI University No. 1 Jalan Menara Gading UCSI Heights Cheras Kuala Lumpur 56000 Malaysia Department of Computer Science and Engineering Qatar University Doha 2713 Qatar School of Management Universiti Sains Malaysia Minden Penang 11800 Malaysia Department of Information Technology and Management Daffodil International University Birulia Bangladesh Department of Management Sunway Business School (SBS) Selangor Petaling Jaya 47500 Malaysia University Center for Research & amp Development (UCRD) Chandigarh University Punjab Ludhiana 140413 India Faculty of Economics and Business Universitas Indonesia (UI) West Java Depok City 16424 Indonesia Management Information Systems Department School of Business The University of Jordan (UJ) Amman 11942 Jordan TUIBS Leadership Management & amp Human Resources Teesside University Middlesbrough United Kingdom Faculty of Business Sohar University Sohar P.C 311 Oman

While a considerable body of research addresses firms’ endeavors toward environmental sustainability, the prominence of green computing has been somewhat eclipsed by impediments hindering its swift integration and development. To address this gap, this study conducted a comprehensive analysis, identifying twelve distinct barriers categorized into adoption costs, technological challenges, and internal obstacles. Through consultation with fifteen field experts, the study employed Interpretive Structural Modeling (ISM) and Matrice d’Impacts Croises Multipication Applique´ an Classment (MICMAC) methodologies to scrutinize the influences among these barriers. The findings illuminated that firm size and legal constraints emerge as pivotal and autonomous factors exerting substantial influence on the adoption. Additionally, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was deployed to ascertain the relative importance of these barriers and delineate their causal relationships. Notably, the DEMATEL results underscored that, according to experts, adoption cost barriers hold greater significance compared to other dimensions. Transactional risk, technological complexity, and switching cost were identified as pivotal barriers within their respective dimensions. The decision-making framework formulated in this study offers valuable insights for managers and practitioners, providing a nuanced understanding of the interplay between identified barriers and their implications for implementing green computing initiatives. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Barriers DEMATEL Green computing MICMAC Total interpretive structural modeling

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