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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multi-player non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

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Pressure induced transition from chiral charge order to time-reversal symmetry-breaking superconducting state in Nb-doped CsV3Sb5

arXiv

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arXiv 2024年

作者： Graham, J.N. Islam, S.S. Sazgari, V. Li, Y. Deng, H. Janka, G. Zhong, Y. Gerguri, O. Král, P. Doll, A. Bialo, I. Chang, J. Salman, Z. Suter, A. Prokscha, T. Yao, Y. Okazaki, K. Luetkens, H. Khasanov, R. Wang, Z. Yin, J.-X. Guguchia, Z. PSI Center for Neutron and Muon Sciences CNM Villigen5232 Switzerland Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement Ministry of Education School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China Department of Physics Southern University of Science and Technology Guangdong Shenzhen518055 China Institute for Solid States Physics The University of Tokyo Kashiwa Japan Physik-Institut Universität Zürich Winterthurerstrasse 190 ZürichCH-8057 Switzerland AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków30-059 Poland International Center for Quantum Materials Beijing Institute of Technology Zhuhai319000 China Trans-scale Quantum Science Institute The University of Tokyo Tokyo Japan Material Innovation Research Center The University of Tokyo Kashiwa Japan

The experimental realisation of unconventional superconductivity and charge order in kagome systems AV3Sb5 is of critical importance. We conducted a highly systematic study of Cs(V1−xNbx)3Sb5 with x=0.07 (Nb0.07-CVS) by employing a unique combination of tuning parameters such as doping, hydrostatic pressure, magnetic fields, and depth, using muon spin rotation, AC susceptibility, and STM. We uncovered tunable magnetism in the normal state of Nb0.07-CVS, which transitions to a time-reversal symmetry (TRS) breaking superconducting state under pressure. Specifically, our findings reveal that the bulk of Nb0.07-CVS (at depths >20 nm from the surface) experiences TRS breaking below T∗ = 40 K, lower than the charge order onset temperature, TCO = 58 K. However, near the surface ( © 2024, CC BY.

关键词： Magnetic susceptibility

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Enhanced geometric properties prediction for carrots in motion using a Multi-task YOLO-based Linked Network (YL-Net)

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computers and Electronics in Agriculture 2025年 237卷

作者： Yi-Liang Wu Sze-Teng Liong Gen-Bing Liong Jun-Hui Liang Y.S. Gan Department of Electronic Engineering Feng Chia University Taichung 40724 Taiwan ROC Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia School of Architecture Feng Chia University Taichung 40724 Taiwan ROC

With a growing shortage of manual labor in agriculture, there is an urgent need for efficient and automated solutions to address the complex challenges of inspecting and classifying agricultural products, such as carrots, which exhibit irregular shapes, occlusions, and high variability in geometric properties. This study introduces a novel Multi-task YOLO-based Linked Network (YL-Net) designed to estimate the geometric properties of carrots in motion, including width, length, volume, and mass. The proposed network integrates RGB-D input with decoupled multi-task learning to simultaneously perform instance segmentation and regression. Building upon our previous work, the enhanced framework presented herein achieves outstanding performance, with MAPE values below 2.5% for all estimated properties. When aggregating multi-view data from a rolling conveyor system, the accuracy further improves, yielding MAPE values below 2%. In terms of detection, the model demonstrates excellent performance, achieving a mean F1-score of 98.78% and an instance segmentation IoU of 89.25%. To evaluate its scalability, the system was deployed on an NVIDIA Jetson Orin Nano, where it achieved a real-time processing speed of 80 FPS. Beyond carrots, the proposed approach can be extended to inspect other agricultural products, such as potatoes and sweet potatoes, where geometric properties are essential for sorting and grading. This work provides a scalable and transferable solution for automated agricultural inspection, laying a robust foundation for broader applications in smart farming, industrial automation, and food quality control.

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RETRACTED ARTICLE: A research study on the cervical cerclage to deal with cervical insufficiency using machine learning

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Soft Computing 2023年第2期28卷 643-643页

作者： Kaur, Mandeep Khedkar, Ganesh Sakhare, Sachin Rogulj, Katarina Department of Computer Science Savitribai Phule Pune University Pune India Siddhant College of Engineering Pune India Computer Engineering Department Vishwakarma Institute of Information Technology Pune India Faculty of Civil Engineering Architecture and Geodesy University of Split Split Croatia

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Wind Comfort Analysis Using an Immersed Boundary Method for Early-Stage Building Design

SSRN

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SSRN 2024年

作者： Mitkov, Radostin Hunger, Franziska Pantusheva, Mariya Naserentin, Vasilis Gösta, Alexander Mark, Andreas Petrova-Antonova, Dessislava Logg, Anders GATE Institute Sofia University "St. Kliment Ohridski" Sofia1113 Bulgaria Fraunhofer-Chalmers Centre for Industrial Mathematics Computational Engineering and Design Chalmers Science Park GothenburgSE-412 88 Sweden Department of Mathematical Sciences Chalmers University of Technology GothenburgSE-412 969 Sweden Department of Electrical and Computer Engineering Aristotle University of Thessaloniki ThessalonikiGR- 54124 Greece RISE - Research Institutes of Sweden Architecture and spatial planning Drottning Kristinas Väg 61 StockholmSE-114 28 Sweden Liljewall Arkitekter Odinsplatsen 1 GothenburgSE-411 02 Sweden

This paper presents a thorough exploration of wind comfort dynamics in urban environments through the application of computational fluid dynamics simulations, with a specific emphasis on the Immersed Boundary Method. The study validates the Immersed Boundary Octree Flow solver, IBOFlow, against experimental data and then simulates the flow in a real-world case study of an area in Mölndal, Sweden. The research examines the impact of varying the number of simulated wind directions on the wind comfort maps, revealing that employing 16 wind directions is a reliable balance between computational efficiency and precision. Further, the investigation delves into the implications of using a simplified building geometry as available in early-stage design, demonstrating that computational simulations can provide reliable information about the pedestrian wind comfort early in the design process. While underscoring the significance of early-stage CFD simulations in informing design decisions, the paper concludes by advocating for a holistic approach. It emphasizes the relevance of simulation testing in later design iterations, particularly as architectural details continue to be refined. © 2024, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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Identifying Hierarchical Individual Functional Network under Naturalistic Paradigm via Two-stage DBN with Neural architecture Search 4

Identifying Hierarchical Individual Functional Network under...

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4th International Symposium on Image Computing and Digital Medicine, ISICDM 2020

作者： Tao, Zeyang Ren, Yudan Zhang, Wei Liu, Tianming School of Information Science AND Technology Northwest University Xi'an China Department of Radiology and Biomedical Imaging University of California San FranciscoCA United States Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States

ISBN: (纸本)9781450389686

Functional magnetic resonance imaging under naturalistic paradigm (NfMRI) is gaining increasing attraction, as it offers an ecologically-valid condition to understand brain function in real life. Characterizing the hierarchical organization of brain function while taking the nature of fMRI activities under naturalistic condition into account has been a critical issue in identifying naturalistic functional networks. Recent studies have made efforts on characterizing the brain's hierarchical organizations from fMRI data via a variety of deep learning models. However, most of those models have ignored the properties of group-wise consistency and inter-subject difference in brain function under naturalistic paradigm. Another critical issue is how to determine the optimal neural architecture of deep learning models, as manual design of neural architecture is time-consuming and less reliable. To tackle these problems, we proposed a two-stage deep belief network (DBN) with neural architecture search (NAS) combined framework (two-stage NAS-DBN framework) to model both the group-consistent and individual-specific naturalistic functional brain networks. Our results demonstrated that the optimized DBN-based framework can characterize meaningful group-wise and individual-level naturalistic functional networks, which reflected the hierarchical organization of brain function and the properties of brain functional activities under naturalistic paradigm. © 2020 ACM.

关键词： Brain

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A Critical Analysis on Neural Networks and Deep Learning based Techniques for the Cloud Computing System and its Impact on Industrial Management

A Critical Analysis on Neural Networks and Deep Learning bas...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Akhilesh Latoria S. K. Joshi A. Priya M. Kalyan Chakravarthi Radha Raman Chandan Bhasker Pant School of Information Technology Auro University Surat Gujarat Uttaranchal University Dehradun Uttarakhand India School of Architecture KoneruLakshmaiah Educational Foundation Guntur Andhra Pradesh India School of Electronics Engineering VIT-AP University Amaravathi India Department of Computer Science School of Management Sciences (SMS) Khushipur Bachhaon Varanasi UP Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

Due to the rising use of the internet for communication, commercial financial transactions, and other purposes, cloud computing is described as a crucial feature that has risen lately. Through network systems, this entails providing a flexible growth of different services. The fog is often used as a concept to describe different *** often trust cloud service providers to store enormous amounts of data and information with the intention of accessing it later. This paper is mainly concerned with doing a critical examination of the usage of supervised learning and deep learning in cloud applications, as well as their effects on production *** businesses are concentrating on using technology to increase returns on investments, assemble vast quantities of data, and archive it securely so that it may be utilised for organisational decision-making. Notwithstanding, they are more concerned with the cost and security of storing the information. so in order to accommodate the customers, the cloud services have used a wide range of approaches, including human brains, supervised learning, advanced analytics, and others. This helps to increase the users’ credibility and help them use cloud storage to achieve their *** order to understand the overall efficacy of employing neural networks and advanced learning processes connected to cloud technology, this research is capable of using both secondary and primary information. The efficacy will be assessed by the researchers using correlation matrix and other non-parametric approaches. The conclusion is reached after thorough data examination and interpretation.

关键词： Deep learning Cloud computing Correlation Statistical analysis Supervised learning Production Syntactics

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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SpreadSketch: Toward Invertible and Network-Wide Detection of Superspreaders

SpreadSketch: Toward Invertible and Network-Wide Detection o...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lu Tang Qun Huang Patrick P. C. Lee Department of Computer Science and Engineering The Chinese University of Hong Kong State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Superspreaders (i.e., hosts with numerous distinct connections) remain severe threats to production networks. How to accurately detect superspreaders in real-time at scale remains a non-trivial yet challenging issue. We present SpreadSketch, an invertible sketch data structure for network-wide superspreader detection with the theoretical guarantees on memory space, performance, and accuracy. SpreadSketch tracks candidate super-spreaders and embeds estimated fan-outs in binary hash strings inside small and static memory space, such that multiple SpreadSketch instances can be merged to provide a network-wide measurement view for recovering superspreaders and their estimated fan-outs. We present formal theoretical analysis on SpreadSketch in terms of space and time complexities as well as error bounds. Trace-driven evaluation shows that SpreadSketch achieves higher accuracy and performance over state-of-the-art sketches. Furthermore, we prototype SpreadSketch in P4 and show its feasible deployment in commodity hardware switches.

关键词： Data structures computer crime Real-time systems Hardware Memory management Estimation IP networks

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Sequence Triggered Hardware Trojan in Neural Network Accelerator

Sequence Triggered Hardware Trojan in Neural Network Acceler...

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VLSI Test Symposium

作者： Zizhen Liu Jing Ye Xing Hu Huawei Li Xiaowei Li Yu Hu State Key Laboratory of Computer Architecture Institution of Computing Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering University of California Santa Barbara

ISBN: (数字)9781728153599

ISBN: (纸本)9781728153605

With the rapid development of deep learning techniques, the security issue for Neural Network (NN) systems has emerged as an urgent and severe problem. Hardware Trojan attack is one of the threatens, which provides attackers backdoors to control the prediction results of NN systems. This paper proposes a sequence triggered hardware Trojan. Normal images but with specific sequence are used to trigger the hardware Trojan and let attackers fully control the prediction results. This kind of trigger is not only robust to image pre-processing, but also unrecognizable by human beings. In comparison with existing hardware Trojan design, it is more practical and less hardware overhead. The experiments on MNIST, CIFAR100, and ISLVRC show that the proposed hardware Trojan is rarely triggered in normal working status while the hardware cost is reduced by 19X.

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