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International Journal of Internet Manufacturing and Services

作者： Xu, Jiuyun Sun, Xiaoting Zhang, Ruru Liang, Hongliang Duan, Qiang School of Computer and Communication Engineering China University of Petroleum Qingdao China Software Technology Company No. 58 Kunshan Road Science and Technology City Suzhou High-Tech Zone Jiangsu Province China Department of Informatics Beijing University of Posts and Telecommunications Beijing China Information Sciences and Technology Department Pennsylvania State University PennsylvaniaPA United States

The emerging IoT introduces many new challenges that cannot be adequately addressed by the current 'cloud-only' architectures. The cooperation of the fog and cloud is considered to be a promising architecture, which efficiently handles IoT's data processing and communications requirements. However, how to schedule tasks to better adapt to IoT real-time needs and reduce the energy in the fog-cloud system is not well addressed. In this paper, we first model the energy consumption of the fog and cloud, respectively, and formulate a task scheduling problem into a constrained optimisation problem in fog-cloud computing system. Then, an efficient deadline-energy scheduling algorithm based on ant colony optimisation (DEACO) is put forward to tackle this problem, which achieves to reduce energy consumption on the condition of satisfying the task deadline. Finally, algorithms have been simulated on the extended CloudSim simulator. The experimental results have shown that our scheduling approach reduces energy more effective. © 2020 Inderscience Enterprises Ltd.

关键词： Green computing Ant colony optimization computer architecture Constrained optimization Data handling Energy utilization Fog Internet of things Multitasking Cloud systems Cloudsim Constrained optimisation problems Optimisations Real time Reduce energy consumption Task scheduling problem Task scheduling

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Inequivalent multipartite coherence classes and two operational coherence monotones

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Physical Review A 2019年第4期99卷 042306-042306页

作者： Yu Luo Yongming Li Min-Hsiu Hsieh College of Computer Science Shaanxi Normal University Xi'an 710062 China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

Quantum coherence has received significant attention in recent years, but the structure of multipartite coherent states is unclear. In this paper, we generalize important results in multipartite entanglement theory to their counterparts in quantum coherence theory. First, we give a necessary and sufficient condition for when two pure multipartite states are equivalent under local incoherent operations assisted by classical communications (LICC), i.e., two states can be deterministically transformed to each other under LICC operations. Next, we investigate and give the conditions in which such a transformation succeeds only stochastically. Different from the entanglement case for two-qubit states, we find that the stochastic LICC (sLICC) equivalence classes are infinite. Moreover, it is possible that there are some classes of states in multipartite entanglement that can convert into each other, whereas they cannot convert into each other in multipartite coherence. In order to show the difference among sLICC classes, we introduce two coherence monotones: accessible coherence and source coherence, following the logistics given in [Phys. Rev. Lett. 115, 150502 (2015)]. These coherence monotones have a straightforward operational interpretation, namely, the accessible coherence characterizes the proficiency of a state to generate other states via quantum incoherent operations, whereas the source coherence characterizes the set of states that can be reached via quantum incoherent operations acting on the given state of interest.

关键词： Quantum coherence & coherence measures Quantum entanglement Quantum protocols Resource theories

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Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation

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Knowledge-Based Systems 2025年 324卷

作者： Qiangguo Jin Hui Cui Junbo Wang Changming Sun Yimiao He Ping Xuan Linlin Wang Cong Cong Leyi Wei Ran Su School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China Department of Computer Science and Information Technology La Trobe University Melbourne Australia CSIRO Data61 Sydney Australia Department of Computer Science School of Engineering Shantou University Guangdong China Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Shandong China Australian Institute of Health Innovation (AIHI) Macquarie University Australia School of Software Shandong University Shandong China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University Macao Special Administrative Region of China School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

Semi-supervised learning (SSL) has attracted considerable attention in medical image processing. The latest SSL methods use a combination of consistency regularization and pseudo-labeling to achieve remarkable success. However, most existing SSL studies focus on segmenting large organs, neglecting the challenging scenarios where there are numerous tumors or tumors of small volume. Furthermore, the extensive capabilities of data augmentation strategies, particularly in the context of both labeled and unlabeled data, have yet to be thoroughly investigated. To tackle these challenges, we introduce a straightforward yet effective approach, termed iterative pseudo-labeling based adaptive copy-paste supervision (IPA-CP), for tumor segmentation in CT scans. IPA-CP incorporates a two-way uncertainty based adaptive augmentation mechanism, aiming to inject tumor uncertainties present in the mean teacher architecture into adaptive augmentation. Additionally, IPA-CP employs an iterative pseudo-label transition strategy to generate more robust and informative pseudo labels for the unlabeled samples. Extensive experiments on both in-house and public datasets show that our framework outperforms state-of-the-art SSL methods in medical image segmentation. Ablation study results demonstrate the effectiveness of our technical contributions.

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Multiphase MRI radiomics model for predicting microvascular invasion in HCC: Development and clinical validation

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iLIVER 2025年第2期4卷

作者： Peng, Yue Wu, Songxiong Xiong, Bing Chen, Fuqiang Zaki, Nazar Wu, Ruodai Qin, Wenjian Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China University of Chinese Academy of Sciences Beijing 100049 China Department of Radiology Shenzhen University General Hospital Shenzhen University Shenzhen 518000 China Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University (UAEU) Al Ain 15551 United Arab Emirates

Background and aims: Accurate preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for treatment planning. This study aimed to develop and validate a multi-phase magnetic resonance imaging (MRI)-based radiomics model for predicting MVI in HCC patients. Methods: This retrospective study included 110 HCC patients (training: n = 77;validation: n = 33) who underwent preoperative multi-phase MRI. Radiomics features were extracted from four MRI phases (non-contrast, arterial, portal, and hepatobiliary). Feature selection was performed using least absolute shrinkage and selection operator regression, and five machine learning classifiers were evaluated. Model performance was assessed using standard metrics including area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: The four-phase radiomics model with logistic regression classifier showed optimal performance in both the training (AUC = 0.896;95% confidence interval, 0.792–0.963) and validation cohorts (AUC = 0.889, 95% confidence interval, 0.781–0.982), outperforming the single-phase (AUC = 0.789), two-phase (AUC = 0.815), and three-phase models (AUC = 0.848) in the validation cohort. In the validation cohort, the model achieved balanced performance with sensitivity, specificity, accuracy, and precision all reaching 0.857. Conclusions: The multi-phase MRI-based radiomics model significantly improves MVI prediction accuracy in HCC patients. This non-invasive approach could enhance preoperative assessment and treatment planning. © 2025 The Authors

关键词： Hepatocellular carcinoma Microvascular invasion Multi-phase MRI

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Investigating Bi-level optimization for learning and vision from a unified perspective: A survey and beyond

arXiv

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arXiv 2021年

作者： Liu, Risheng Gao, Jiaxin Zhang, Jin Meng, Deyu Lin, Zhouchen DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116024 China Department of Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an Shaanxi China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. © 2021, CC BY.

关键词： Deep learning

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Gray Scale Image Coloring Method Based on GAN

Gray Scale Image Coloring Method Based on GAN

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作者： Long Guo Lu Liang Shi Zeng Ji He Gaoshang Dai Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences School of mathematics and science Fudan University School of Computer and Software Nanjing University of Information Science and Technology Lierda Science & Technology Group Co. Ltd. School of Materials Science and Engineering Huazhong University of Science and Technology

In order to solve the problem that the existing gray-scale image coloring model is not rich in color expression and the edge distinction between different objects is not obvious, this paper proposed a gray-scale image coloring method based on the generated adversarial network（GAN）, which adopts convolution neural network with the same size of the feature image as the generator and the cost function, based on its "end-to-end" feature, we trained the GAN model, takes the gray image as the model input, and colorizes the color image at the output of the generator. The method proposed in this paper is applied to the remote sensing image data set UCMercedandUse for experimental verification. According to the experimental results, it can be observed that the gray-scale image coloring method based on GAN proposed in this paper has better evaluation index than other methods with good coloring performance, the color expression of the image after coloring by the model is more abundant.

关键词： Gray-scale image Generated adversarial network Convolution neural network UCMercedLandUse Image coloring method

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Joint channel assignment and power allocation for multi-UAV communication

arXiv

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arXiv 2020年

作者： Zhou, Lingyun Chen, Xihan Hong, Mingyi Jin, Shi Shi, Qingjiang School of Software Engineering Tongji University Shanghai200092 China Department of Information Science and Electronic Engineering Zhejiang University Hangzhou310000 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation. To reap the potential gains of UAV swarm, the remote control signal sent by ground control unit (GCU) is essential, whereas the control signal quality are susceptible in practice due to the effect of the adjacent channel interference (ACI) and the external interference (EI) from radiation sources distributed across the region. To tackle these challenges, this paper considers priority-aware resource coordination in a multi-UAV communication system, where multiple UAVs are controlled by a GCU to perform certain tasks with a pre-defined trajectory. Specifically, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the UAVs by jointly optimizing channel assignment and power allocation strategy under stringent resource availability constraints. According to the intensity of ACI, we consider the corresponding problem in two scenarios, i.e., Null-ACI and ACI systems. By virtue of the particular problem structure in Null-ACI case, we first recast the formulation into an equivalent yet more tractable form and obtain the global optimal solution via Hungarian algorithm. For general ACI systems, we develop an efficient iterative algorithm for its solution based on the smooth approximation and alternating optimization methods. Extensive simulation results demonstrate that the proposed algorithms can significantly enhance the minimum SINR among all the UAVs and adapt the allocation of communication resources to diverse mission priority. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Ransp: Ranking Attention Network For Saliency Prediction On Omnidirectional Images

Ransp: Ranking Attention Network For Saliency Prediction On ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Dandan Zhu Yongqing Chen Tian Han Defang Zhao Yucheng Zhu Qiangqiang Zhou Guangtao Zhai Xiaokang Yang Artificial Intelligence Institute Shanghai Jiao Tong University Hainan Air Traffic Management Sub-Bureau Department of Computer Science Stevens Institute of Technology School of Software Engineering Tongji University Institute of Image Communication and Network Engineering Shanghai Jiao Tong University School of Information and Computer Shanghai Business School

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

Various convolutional neural network (CNN)-based methods have shown the ability to boost the performance of saliency prediction on omnidirectional images (ODIs). However, these methods are limited by sub-optimal accuracy, because not all the features extracted by the CNN model are not useful for the final fine-grained saliency prediction. Features are redundant and have negative impact on the final fine-grained saliency prediction. To tackle this problem, we propose a novel Ranking Attention Network for saliency prediction (RANSP) of head fixations on ODIs. Specifically, the part-guided attention (PA) module and channel-wise feature (CF) extraction module are integrated in a unified framework and are trained in an end-to-end manner for fine-grained saliency prediction. To better utilize the channel-wise feature map, we further propose a new Ranking Attention Module (RAM), which automatically ranks and selects these maps based on scores for fine-grained saliency prediction. Extensive experiments are conducted to show the effectiveness of our method for saliency prediction of ODIs.

关键词： Feature extraction Head Predictive models Random access memory Two dimensional displays Task analysis

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Modified indexing algorithm based on priority queue in metric space for MVP tree 4

Modified indexing algorithm based on priority queue in metri...

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4th International Conference on Future Networks and Distributed Systems, ICFNDS 2020

作者： Fomin, Vladimir Aleksandrov, Igor Gallyamov, Denis Kirichek, Ruslan Dept. of Information Systems Herzen State Pedagogical University of Russia Saint Petersburg Russia Dept. of Software Engineering and Computer Science Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781450388863

The Internet of Things (IoT) algorithms process huge amounts of heterogeneous data in real-time. One of the most computationally intensive tasks using cloud technologies is the task of clustering and classifying data. The authors propose to develop an approach to data classification within the "Query by Similarity"paradigm, which uses the technology of data indexing based on Metric Access Methods (MAM). To improve the performance of data indexing, this paper proposes a similar nearest neighbor search method combining a multiple vantage point tree (MVP) and improved algorithms for processing the priority queue of nodes. The following two algorithms for processing the priority queue of nodes were developed: 1) algorithm for all kinds of points-queries, which makes it possible to take into account parent nodes of all higher levels;2) algorithm for grouped based on clustering of points-queries by reusing previously obtained search results. Experimental results confirm the effectiveness of the proposed approaches and algorithms. © 2020 ACM.

关键词： Nearest neighbor search

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Generating superpixels for high-resolution images with Decoupled Patch Calibration

arXiv

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arXiv 2021年

作者： Wang, Yaxiong Wei, Yunchao Qian, Xueming Zhu, Li Yang, Yi The School of Software Engineering Xi'an Jiaotong University Xi'an710049 China School of Computer and Information Technology Beijing Jiaotong University Beijing100000 China The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi'an Jiaotong University Xi'an710049 China The SMILES Laboratory Xi'an Jiaotong University Xi'an710049 China Zhibian Technology Co. Ltd. Taizhou317000 China The School of Software Xi'an Jiaotong University Xi'an710049 China School of Computer Science and Technology Zhejiang University Hangzhou310000 China

Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning. However, the very high-resolution superpixel segmentation still remains challenging due to the expensive memory and computation cost, making the current advanced superpixel networks fail to process. In this paper, we devise Patch Calibration Networks (PCNet), aiming to efficiently and accurately implement high-resolution superpixel segmentation. PCNet follows the principle of producing high-resolution output from low-resolution input for saving GPU memory and relieving computation cost. To recall the fine details destroyed by the down-sampling operation, we propose a novel Decoupled Patch Calibration (DPC) branch for collaboratively augment the main superpixel generation branch. In particular, DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries. By sharing the parameters of DPC and main branches, the fine-detailed knowledge learned from high-resolution patches will be transferred to help calibrate the destroyed information. To the best of our knowledge, we make the first attempt to consider the deep-learning-based superpixel generation for high-resolution cases. To facilitate this research, we build evaluation benchmarks from two public datasets and one new constructed one, covering a wide range of diversities from fine-grained human parts to cityscapes. Extensive experiments demonstrate that our PCNet can not only perform favorably against the state-of-the-arts in the quantitative results but also improve the resolution upper bound from 3K to 5K on 1080Ti GPUs. © 2021, CC BY.

关键词： Image segmentation

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