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Structural Robustness of Complex Networks: A Survey of A Posteriori Measures

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arXiv 2023年

作者： Lou, Yang Wang, Lin Chen, Guanrong The Graduate School of Information Science and Technology Osaka University Osaka Suita565-0871 Japan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various industrial and social networks against malicious attacks, which has various meanings in different research contexts and here it refers to the ability of a network to sustain its functionality when a fraction of the network fail to work due to attacks. The rapid development of complex networks research indicates special interest and great concern about the network robustness, which is essential for further analyzing and optimizing network structures towards engineering applications. This comprehensive survey distills the important findings and developments of network robustness research, focusing on the a posteriori structural robustness measures for single-layer static networks. Specifically, the a posteriori robustness measures are reviewed from four perspectives: 1) network functionality, including connectivity, controllability and communication ability, as well as their extensions;2) malicious attacks, including conventional and computation-based attack strategies;3) robustness estimation methods using either analytical approximation or machine learning-based prediction;4) network robustness optimization. Based on the existing measures, a practical threshold of network destruction is introduced, with the suggestion that network robustness should be measured only before reaching the threshold of destruction. Then, a posteriori and a priori measures are compared experimentally, revealing the advantages of the a posteriori measures. Finally, prospective research directions with respect to a posteriori robustness measures are recommended. © 2023, CC BY.

关键词： Complex networks

Advancements in Superconducting Microwave Cavities and Qubits for Quantum Information systems

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arXiv 2023年

作者： Krasnok, Alex Dhakal, Pashupati Fedorov, Arkady Frigola, Pedro Kelly, Michael Kutsaev, Sergey Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States ARC Centre for Engineered Quantum System School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia RadiaBeam LLC Santa MonicaCA90403 United States Argonne National Laboratory LemontIL60439 United States

Superconducting microwave cavities with ultra-high Q-factors are revolutionizing the field of quantum computing, offering long coherence times exceeding 1 ms, which is critical for realizing scalable multi-qubit quantum systems with low error rates. In this work, we provide an in-depth analysis of recent advances in ultra-high Q-factor cavities, integration of Josephson junction-based qubits, and bosonic-encoded qubits in 3D cavities. We carefully examine the sources of quantum state dephasing caused by damping and noise mechanisms in cavities and qubits, highlighting the critical challenges that need to be addressed to achieve even higher coherence times. We critically survey the latest progress made in implementing single 3D qubits using superconducting materials, normal metals, and multi-qubit and multi-state quantum systems. Our work sheds light on the promising future of this research area, including novel materials for cavities and qubits, modes with nontrivial topological properties, error correction techniques for bosonic qubits, and new light-matter interaction effects. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

Hierarchical Nb2o5@Zno Hetero-Branched Nanorods for Enhanced H2s Gas Sensing

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SSRN

SSRN 2022年

作者： Wu, Tao Tao Zhu, Liyuan Wu, Xue-Yan Miao, Xiao-Yong Mao, Li-Wen Jin, Xuan-Hong Lu, Hong-Liang State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai 200433 China School of Optical-Electrical & Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China Yiwu Research Institute of Fudan University Chengbei Road Zhejiang Yiwu City322000 China

Hierarchical Nb 2 O 5 @ZnO hetero-branched nanorods used for highly responsive, selective, and stable hydrogen sulfide (H 2 S) sensors were synthesized through a sequential process combining a hydrothermal method, atomic-layer-deposition processing, and modified hydrothermal processing for branching ZnO nanowires. The Nb 2 O 5 @ZnO hetero-branched nanorod sensors exhibited a great response (R a /R g ) of 6.10 towards 20 ppm H 2 S at an optimal working temperature of 400 o C, whose resistance changing rate (~5.10) was 39.2- and 1.9-fold those of Nb 2 O 5 (~0.13) and Nb 2 O 5 @ZnO core-shell nanorod (~2.74) sensors, respectively. The heterojunction formed at the Nb 2 O 5 @ZnO interface and the characteristic reaction between ZnO and H 2 S effectively improved H 2 S sensing performance. Moreover, the branching of ZnO nanowires introduced abundant homojunctions and prominently enlarged the specific surface area, further increasing the response of hetero-branched sensors. Meanwhile, the Nb 2 O 5 @ZnO hetero-branched sensors maintained steady performance over a month, indicating good stability and repeatability. In a word, such hierarchical Nb 2 O 5 @ZnO hetero-branched nanorod gas sensors are promising for the development of sensitive and selective H 2 S detection. © 2022, The Authors. All rights reserved.

关键词： Zinc oxide

Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments

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arXiv 2022年

作者： Dai, Ming Zheng, Enhui Feng, Zhenhua Zhuang, Jiedong Yang, Wankou Qi, Lei The School of Automation Southeast University Nanjing210096 China The Unmanned System Application Technology Research Institute China Jiliang University Hangzhou310018 China The School of Computer Science and Electronic Engineering University of Surrey GuildfordGU2 7XH United Kingdom Zhejiang University Hangzhou310063 China

Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. However, due to limited satellite coverage or communication disruptions, UAVs may lose signals from satellite-based positioning systems. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. However, most of the existing datasets are developed for the geo-localization tasks of the objects identified by UAVs, rather than the self-positioning task of UAVs. Furthermore, the current UAV datasets use discrete sampling on synthetic data, such as Google Maps, thereby neglecting the crucial aspects of dense sampling and the uncertainties commonly experienced in real-world scenarios. To address these issues, this paper presents a new dataset, DenseUAV, which is the first publicly available dataset designed for the UAV self-positioning task. DenseUAV adopts dense sampling on UAV images obtained in low-altitude urban settings. In total, over 27K UAV-view and satellite-view images of 14 university campuses are collected and annotated, establishing a new benchmark. In terms of model development, we first verify the superiority of Transformers over CNNs in this task. Then, we incorporate metric learning into representation learning to enhance the discriminative capacity of the model and to lessen the modality discrepancy. Besides, to facilitate joint learning from both perspectives, we propose a mutually supervised learning approach. Last, we enhance the Recall@K metric and introduce a new measurement, SDM@K, to evaluate the performance of a trained model from both the retrieval and localization perspectives simultaneously. As a result, the proposed baseline method achieves a remarkable Recall@1 score of 83.05% and an SDM@1 score of 86.24% on DenseUAV. The dataset and code will be made publicly available on https://***/Dmmm1997/DenseUAV. © 2022, CC BY.

关键词： Satellites

Crsanet: Class Representations Self-Attention Network for the Segmentation of Thyroid Nodules

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SSRN

SSRN 2023年

作者： Sun, Shiyao Fu, Chong Xu, Sen Wen, Yingyou Ma, Tao School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China General Hospital of Northern Theatre Command Shenyang110016 China Medical Imaging Research Department Neusoft Research of Intelligent Healthcare Technology Co. Ltd. Shenyang China Dopamine Group Ltd. Auckland1542 New Zealand

Segmenting accurate thyroid nodules in medical ultrasound images is always non-trivial due to the large variation in size, shape and texture of nodules, and also the presence of surrounding tissues and organs with similar intensity values. Existing algorithms solve this by designing multi-scale spatial context modeling modules and introducing the emerging self-attention based paradigm, while failing to learn the class-wise relational context around nodules. To this end, we propose a novel coarse-to-fine framework, namely, CRSANet, which leverages both class-wise relational context and spatial multi-scale context for comprehensive context perception. Specifically, we first introduce the concept of Class Representations, which describe the overall representation of thyroid nodules in ultrasound images from a categorical perspective. Secondly, we propose a backbone-free dual-branch Class Representation Self-Attention Module (CRSA) to refine the coarse segmentation results from segmentation backbones. Concretely, Fine Class Region (FCR) branch is proposed to impose consistency between pixels and their corresponding Class Representations to correct the wrongly classified pixels. Meanwhile, Class Contextual Representation (CCR) branch is introduced to enhance the ability of pixel representations with the corresponding Class Representations. We evaluate the effectiveness and generality of CRSANet using three types of backbone networks. In the experiments, our proposed U-CRSANet achieves a mIoU of 78.86% and 81.35%, an accuracy of 97.11% and 98.63%, a Dice value of 74.26% and 87.57%, respectively, which outperforms most state-of-the-art methods on the public DDTI and TN-SCUI datasets. © 2023, The Authors. All rights reserved.

关键词： Pixels

The data synergy effects of time-series deep learning models in hydrology

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arXiv 2021年

作者： Fang, Kuai Kifer, Daniel Lawson, Kathryn Feng, Dapeng Shen, Chaopeng Department of Earth System Science Stanford University Stanford United States Department of Civil and Environmental Engineering Pennsylvania State University University ParkPA United States Department of Computer Science and Engineering Pennsylvania State University University ParkPA United States

When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to regionalize - to divide a large spatial domain into multiple regions and study each region separately - instead of fitting a single model on the entire data (also known as unification). Traditional wisdom in these fields suggests that models built for each region separately will have higher performance because of homogeneity within each region. However, by partitioning the training data, each model has access to fewer data points and cannot learn from commonalities between regions. Here, through two hydrologic examples (soil moisture and streamflow), we argue that unification can often significantly outperform regionalization in the era of big data and deep learning (DL). Common DL architectures, even without bespoke customization, can automatically build models that benefit from regional commonality while accurately learning region-specific differences. We highlight an effect we call data synergy, where the results of the DL models improved when data were pooled together from characteristically different regions. In fact, the performance of the DL models benefited from more diverse rather than more homogeneous training data. We hypothesize that DL models automatically adjust their internal representations to identify commonalities while also providing sufficient discriminatory information to the model. The results here advocate for pooling together larger datasets, and suggest the academic community should place greater emphasis on data sharing and compilation. Copyright © 2021, The Authors. All rights reserved.

关键词： Soil moisture

Innovative integrated operation planning tool for the current and future 2030+ European power grid

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Innovative integrated operation planning tool for the curren...

CIRED 2020 Berlin Workshop, CIRED 2020

作者： Di Somma, Marialaura Ciavarella, Roberto Graditi, Giorgio Khavari, Ata M. Kosmecki, Michal Papadimitriou, Christina N. Valenti, Maria ENEA Department of Energy Technologies and Renewable Sources Rome Italy DERlab – European Distributed Energy Resources Laboratories Kassel Germany System Analysis Department Institute of Power Engineering Gdansk Poland FOSS Research Centre for Sustainable Energy Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (纸本)9781839535635

To deal with the evolution in place of the power system, innovative solutions are needed to support grid operators for the operation of the electricity system to increase the security of supply accounting for the increasing share of renewable energy sources (RES). The goal of the H2020 INTERPLAN project is to develop an innovative tool for transmission system operators (TSOs) and distribution system operators (DSOs) addressing the operation planning challenges of the ongoing pan-European network, while also fostering the achievement of the low-carbon EU objectives. The contribution of this study is to present the tool, defined as a methodology consisting of a set of functions (grid equivalents, control functions) for the operation planning of the current and future 2030+ EU power grid, by addressing a significant number of operation challenges, from the perspective of the transmission system, the distribution system, and with a focus on the transmission–distribution interface. The tool is aimed to plan the operation of an integrated grid from TSOs and DSOs perspectives, through efficiently handling intermittent RES and emerging technologies such as storage and demand response, by supporting the usage of the flexibility potential coming from these technologies for system services in all network control levels. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Renewable energy resources

Design of concentrated solar power water desalination system (CSPWDS)

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AIP Conference Proceedings 2023年第1期2591卷

作者： Abdulkareem Naser Mahmood Syahrul Ashikin Binti Azmi Reyad El-Khazali 1School of Electrical System Engineering University of Malaysia Perlis (UniMAP) Arau Perlis Malaysia. 2Electrical and Computer Engineering Department Khalifa University Abu Dhabi UAE.

This work focuses on designing a concentrated solar power water desalination system (CSPWDS). A set of four Fresnel lenses (FRL) are used to concentrate the solar power at their corresponding focal points. The objective is to focus the irradiance power at a small area, where the temperature could reach over a 1000 Cͦ at its focal point. This study investigates a design method of a CSPWDS that includes an investigation of the effectiveness of using FRLs for water desalination. The system automatically tracks the sun to improve its production rate. Four spiral copper pipes receivers are designed to convert the saline water into a steam that is directed upward to benefit from the gravitational force. The salt residue in the steam is then filtered out due to both using a special membrane and due to gravity force. A condensation process is then followed to condense the steam into water. The salinity of the condensed steam is tested to ensure a good quality of the desalinated water. The process is continuously monitored to improve the quality of the generated water. If the level of water salinity is still high, a feedback path is implemented to repeat the process until the quality of the desalination is met.

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Portfolio Selection Utilizing Electronic Company Stocks During the Enhance Community Quarantine Period in the Philippines

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Portfolio Selection Utilizing Electronic Company Stocks Duri...

IEEE International Conference on Industrial engineering and engineering Management

作者： M. N. Young G. S. Arevalo E. C. Mallari School of Industrial and System Engineering Mapúa University Manila Philippines School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9781538672204

ISBN: (纸本)9781538672211

This study presents a portfolio selection framework utilizing electric company stocks during the COVID-19 pandemic in the Philippines. A set of criteria is presented to identify the investment pool composed of electric companies. Returns are estimated through historical returns and assumed to be equally likely. Then, equally weighted portfolio strategy is applied to identify the optimal portfolio. The portfolio is then compared to a counterpart portfolio, a benchmark and the market. Back-test shows that the electric company portfolio outperforms its counterpart portfolio in all aspect, it can also outperform the market in terms of returns and standard deviations and be at par with the benchmark. This may lead to a generic portfolio selection framework for individual investors.

关键词： Companies Portfolios Investment History Market research Indexes Electric potential