检索结果-内蒙古大学图书馆

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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Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias

arXiv

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arXiv 2022年

作者： Donhauser, Konstantin Ruggeri, Nicolò Stojanovic, Stefan Yang, Fanny ETH Zurich Department of Computer Science Tübingen Germany ETH AI Center Tübingen Germany Max-Planck-Institute for Intelligent Systems Tübingen Germany ETH Zurich Tübingen Germany

Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator. Even though this intuition is valid for regularized models, in this paper we caution against a strong inductive bias for interpolation in the presence of noise: While a stronger inductive bias encourages a simpler structure that is more aligned with the ground truth, it also increases the detrimental effect of noise. Specifically, for both linear regression and classification with a sparse ground truth, we prove that minimum p-norm and maximum p-margin interpolators achieve fast polynomial rates close to order 1/n for p > 1 compared to a logarithmic rate for p = 1. Finally, we provide preliminary experimental evidence that this tradeoff may also play a crucial role in understanding non-linear interpolating models used in practice. Copyright © 2022, The Authors. All rights reserved.

关键词： Interpolation

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Development and optimization of deep belief networks applied for academic performance prediction with larger datasets

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IEIE Transactions on Smart Processing and Computing 2020年第4期9卷 298-311页

作者： Sokkhey, Phauk Okazaki, Takeo Graduate School of Engineering and Science University of the Ryukyus 1 Senbaru Nishihara Okinawa903-0213 Japan Department of Foundation Year Institute of Technology of Cambodia Phnom Penh Cambodia Department of Computer Science and Intelligent Systems University of the Ryukyus 1 Senbaru Nishihara Okinawa903-0213 Japan

Deep learning has recently attracted increasing interest for several applications, and great progress has been made. This paper introduces a novel application of a deep learning framework called deep belief networks (DBNs) that can be used to predict the predicting academic performance of larger datasets. First, unsupervised training is considered a so-called pre-training section. The stacked restricted Boltzmann machine (RBM) was trained to obtain the trained weights instead of random initial weights. Subsequently, supervised learning was adopted using backpropagation in the fine-tuning section to classify the student performance levels. An optimization approach for improving the classification performance of the proposed DBN was proposed. The optimization approach consisted of using a feature selection method to obtain the optimal feature subset, optimizing the DBN model with the optimal values of the hyperparameters, and applying the L2-regularization method for weight decay. The experiment was carried out with two phases. Phase1 was implemented with an actual dataset. Phase 2 was then implemented with four artificial datasets of increasing sizes. Many experiments were performed independently on each dataset. With a larger dataset, the improved DBN generated the highest accuracy and lowest root mean square error with more accuracy and effectiveness than the other proposed algorithms. Copyrights © 2020 The institute of Electronics and Information Engineers

关键词： Feature Selection

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Self-Healing Distributed Swarm Formation Control Using Image Moments

arXiv

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arXiv 2023年

作者： Liu, C. Lin Donato Ridgley, Israel L. Elwin, Matthew L. Rubenstein, Michael Freeman, Randy A. Lynch, Kevin M. Department of Mechanical Engineering Northwestern University EvanstonIL60208 United States Center for Robotics and Biosystems Northwestern University EvanstonIL60208 United States Department of Electrical & Computer Engineering Northwestern University EvanstonIL60208 United States Department of Computer Science Northwestern University EvanstonIL60208 United States Northwestern Institute on Complex Systems Northwestern University EvanstonIL60208 United States

Human-swarm interaction is facilitated by a low-dimensional encoding of the swarm formation, independent of the (possibly large) number of robots. We propose using image moments to encode two-dimensional formations of robots. Each robot knows its pose and the desired formation moments, and simultaneously estimates the current moments of the entire swarm while controlling its motion to better achieve the desired group moments. The estimator is a distributed optimization, requiring no centralized processing, and self-healing, meaning that the process is robust to initialization errors, packet drops, and robots being added to or removed from the swarm. Our experimental results with a swarm of 50 robots, suffering nearly 50% packet loss, show that distributed estimation and control of image moments effectively achieves desired swarm formations. © 2023, CC BY.

关键词： Multi agent systems

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Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks

arXiv

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arXiv 2023年

作者： Gebhard, Timothy D. Angerhausen, Daniel Konrad, Björn S. Alei, Eleonora Quanz, Sascha P. Schölkopf, Bernhard Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany Max Planck ETH Center for Learning Systems Max-Planck-Ring 4 Tübingen72076 Germany ETH Zurich Institute for Particle Physics & Astrophysics Wolfgang-Pauli-Str. 27 Zurich8092 Switzerland Department of Computer Science ETH Zurich Zurich8092 Switzerland

Context. Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the pressure-temperature (PT) profile, which describes the thermal structure of the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters. Aims. In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods. Methods. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps P to T. Results. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three. Conclusions. By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability. Copyright © 2023, The Authors. All rights reserved.

关键词： Satellites

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Cross View Capture for Distributed Image Compression with Decoder Side Information

Cross View Capture for Distributed Image Compression with De...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Yankai Yin Zhe Sun Peiying Ruan Feng Duan Ruidong Li Chi Zhu Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China Institute of Natural Sciences Kanazawa University Ishikawa Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo University Chiba Japan NVIDIA AI Technology Center NVIDIA Japan Tokyo Japan Department of Systems Life Engineering Maebashi Institute of Technology Maebashi Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Image compression is increasingly important in applications like intelligent driving and smart surveillance systems. This study presents a novel cross view capture distributed image compression network (CVCDIC) to improve the compression quality by using decoder side information. The CVCDIC’s decoder utilizes feature extraction networks to extract features from both the primary image and the side information. Furthermore, a multi-level cross view attention module is designed to capture interrelated details between images at multiple hierarchical levels. Finally, a spatial refinement module, constructed on the foundation of information distillation networks, is designed to further refine the quality of reconstructed images. The results show that CVCDIC can achieve an MS-SSIM of 0.978 at 0.15 bpp, surpassing DSIN (0.925), NDIC (0.956), and ATN (0.955) on the KITTI Stereo dataset.

关键词： Measurement Image coding Fuses Surveillance Rate-distortion Feature extraction Decoding

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Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation

arXiv

引用

arXiv 2023年

作者： Li, Heng Li, Haojin Zhao, Wei Fu, Huazhu Su, Xiuyun Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Medical Intelligence and Innovation Academy Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Hospital Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. Therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. The code is available at https://***/liamheng/Non-IID Medical Image Segmentation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Hypergraphx: a library for higher-order network analysis

arXiv

引用

arXiv 2023年

作者： Lotito, Quintino Francesco Contisciani, Martina De Bacco, Caterina Di Gaetano, Leonardo Gallo, Luca Montresor, Alberto Musciotto, Federico Ruggeri, Nicolò Battiston, Federico Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Trento38123 Italy Max Planck Institute for Intelligent Systems Cyber Valley Tübingen72076 Germany Department of Network and Data Science Central European University Vienna1100 Austria Dipartimento di Fisica e Chimica Emilio Segrè Università di Palermo Viale delle Scienze Ed. 18 PalermoI-90128 Italy Department of Computer Science ETH Zürich8004 Switzerland

From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where hyperedges encode interactions among an arbitrary number of units. Here, we present an open-source python library, hypergraphx (HGX), providing a comprehensive collection of algorithms and functions for the analysis of higher-order networks. These include different ways to convert data across distinct higher-order representations, a large variety of measures of higher-order organization at the local and the mesoscale, statistical filters to sparsify higher-order data, a wide array of static and dynamic generative models, and an implementation of different dynamical processes with higher-order interactions. Our computational framework is general, and allows to analyse hypergraphs with weighted, directed, signed, temporal and multiplex group interactions. We provide visual insights on higher-order data through a variety of different visualization tools. We accompany our code with an extended higher-order data repository, and demonstrate the ability of HGX to analyse real-world systems through a systematic analysis of a social network with higher-order interactions. The library is conceived as an evolving, community-based effort, which will further extend its functionalities over the years. Our software is available at https://***/HGX-Team/hypergraphx. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph theory

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Industry 5.0 is Coming: A Survey on intelligent NextG Wireless Networks as Technological Enablers

arXiv

引用

arXiv 2022年

作者： Zeb, Shah Mahmood, Aamir Khowaja, Sunder Ali Dev, Kapal Hassan, Syed Ali Qureshi, Nawab Muhammad Faseeh Gidlund, Mikael Bellavista, Paolo Islamabad44000 Pakistan The Department of Information Systems & Technology Mid Sweden University Sweden Faculty of Engineering and Technology University of Sindh Jamshoro Pakistan Department of Mechatronics Engineering Korea Polytechnic University Korea Republic of The Department of Computer Science Munster Technological University Ireland Institute of Intelligent Systems University of Johannesburg South Africa The Department of Computer Education Sungkyunkwan University Seoul Korea Republic of The Department of Computer Science and Engineering University of Bologna Bologna40136 Italy

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, envisioned the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications, e.g., factories-of-the-future, digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zero-defect, and mass customization-based manufacturing solutions. However, the vision requires the merging of cyber-physical worlds through utilizing Industry 5.0 technological enablers, e.g., cognitive cobots, person-centric artificial intelligence (AI), cyberphysical systems, digital twins, hyperconverged data storage and computing, communication infrastructure, and others. In this regard, the convergence of the emerging computational intelligence (CI) paradigm and next-generation wireless networks (NGWNs) can fulfill the stringent communication and computation requirements of the technological enablers in the Industry 5.0 vision, which is the aim of this survey-based tutorial. In this article, we address this issue by reviewing and analyzing current emerging concepts and technologies, e.g., CI tools and frameworks, network-in-box architecture, open radio access networks, softwarized service architectures, potential enabling services, and others, essential for designing the objectives of CINGWNs to fulfill the Industry 5.0 vision requirements. Finally, we provide a list of lessons learned from our detailed review, research challenges, and open issues that should be addressed in CI-NGWNs to realize Industry 5.0. © 2022, CC BY-NC-ND.

关键词： Surveys

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Deep convolutional Generative Modeling for artificial microstructure development of aluminum-silicon alloy

arXiv

引用

arXiv 2021年

作者： Mishra, Akshansh Pathak, Tarushi Centre for Artificial Intelligent Manufacturing Systems Stir Research Technologies India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattangulathur India

Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain images of the microstructures. © 2021, CC BY-NC-ND.

关键词： Microstructure

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