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A maximum margin clustering algorithm based on indefinite kernels

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Frontiers of computer science 2019年第4期13卷 813-827页

作者： Hui XUE Sen LI Xiaohong CHEN Yunyun WANG School of Computer Science and Engineering Southeast University Nanjing 210096 Chin Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China College of Science Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Department of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046 China

Indefinite kernels have attracted more and more attentions in machine learning due to its wider application scope than usual positive definite kernels. However, the research about indefinite kernel clustering is relatively scarce. Furthermore, existing clustering methods are mainly designed based on positive definite kernels which are incapable in indefinite kernel scenarios. In this paper, we propose a novel indefinite kernel clustering algorithm termed as indefinite kernel maximum margin clustering (IKMMC) based on the state-of-the-art maximum margin clustering (MMC) model. IKMMC tries to find a proxy positive definite kernel to approximate the original indefinite one and thus embeds a new F-norm regularizer in the objective function to measure the diversity of the two kernels, which can be further optimized by an iterative approach. Concretely, at each iteration, given a set of initial class labels, IKMMC firstly transforms the clustering problem into a classification one solved by indefinite kernel support vector machine (IKSVM) with an extra class balance constraint and then the obtained prediction labels will be used as the new input class labels at next iteration until the error rate of prediction is smaller than a prespecified tolerance. Finally, IKMMC utilizes the prediction labels at the last iteration as the expected indices of ***, we further extend IKMMC from binary clustering problems to more complex multi-class scenarios. Experimental results have shown the superiority of our algorithms.

关键词： indefinite kernel maximum margin clustering support vector machine kernel method

Multimodal Knowledge Graph Vision-Language Models for Precision Retrieval and Inference in Embedded Systems

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Multimodal Knowledge Graph Vision-Language Models for Precis...

IEEE International Conference on Robotics and Biomimetics

作者： Mingyi Li Jiayin Li Hui Liu Lijin Han Dengting Liao Zekun Zhang Zong Gao Baoshuai Liu Zhongfeng Jin School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Shanghai Zhangjiang Institute of Mathematicas Zhangjiang Institute of Shanghai Shanghai China General Technology Department China North Vehicle Research Institute China GNational Computer Network Emergency Response Technical Team Coordination Center of China China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In light of the challenges associated with enhancing multimodal knowledge graph retrieval and improving embodied intelligence in dynamic environments, we propose the Multimodal Knowledge Graph Vision-Language System (MKGVL). This system is designed to enhance the reasoning and feedback capabilities of embodied intelligent systems by continuously updating the knowledge graph through real-time feedback from the Vision-Language Model (VLM). By integrating visual encoders, language models, and knowledge graph networks, MKGVL constructs a unified multimodal representation that enables adaptive decision-making and responsiveness to environmental changes. Experimental results show that MKGVL outperforms existing models in fine-grained retrieval tasks, achieving an 11.5% improvement in Rank1 accuracy and a mean Average Precision (mAP) of 97.49%. Further evaluations conducted on datasets such as ARKitScenes, MultiScan, and 3RScan highlight the model's robustness and adaptability. Additionally, MKGVL's deployment on embedded platforms like Jetson Orin demonstrates its efficiency in real-time multimodal tasks, particularly in resource-constrained environments. These findings underscore MKGVL's ability to deliver accurate and efficient multimodal processing, making it a strong solution for adaptive knowledge-based systems in complex settings.

关键词： Adaptation models Solid modeling Visualization Accuracy Three-dimensional displays Adaptive systems Knowledge graphs Real-time systems Cognition Intelligent systems

Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation

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arXiv 2023年

作者： Luo, Bing Feng, Yutong Wang, Shiqiang Huang, Jianwei Tassiulas, Leandros Electrical and Computer Engineering Division of Natural and Applied Sciences Duke Kunshan University Kunshan China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Ibm T. J. Watson Research Center Yorktown HeightsNY United States Department of Electrical Engineering Institute for Network Science Yale University United States

Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogeneity and limited budget, it is generally impractical for the server to incentivize all clients to participate in all training rounds (known as full participation). The existing FL incentive mechanisms are typically designed by stimulating a fixed subset of clients based on their data quantity or system resources. Hence, FL is performed only using this subset of clients throughout the entire training process, leading to a biased model because of data heterogeneity. This paper proposes a game-theoretic incentive mechanism for FL with randomized client participation, where the server adopts a customized pricing strategy that motivates different clients to join with different participation levels (probabilities) for obtaining an unbiased and high-performance model. Each client responds to the server's monetary incentive by choosing its best participation level, to maximize its profit based on not only the incurred local cost but also its intrinsic value for the global model. To effectively evaluate clients' contribution to the model performance, we derive a new convergence bound which analytically predicts how clients' arbitrary participation levels and their heterogeneous data affect the model performance. By solving a non-convex optimization problem, our analysis reveals that the intrinsic value leads to the interesting possibility of bidirectional payment between the server and clients. Experimental results using real datasets on a hardware prototype demonstrate the superiority of our mechanism in achieving higher model performance for the server as well as higher profits for the clients. © 2023, CC BY.

关键词： Profitability

ANDiE the Autonomous Neutron Diffraction Explorer

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Neutron News 2023年第2期34卷 6-7页

作者： Austin McDannald Matthias Frontzek Andrei T. Savici Mathieu Doucet Efrain E. Rodriguez Kate Meuse Jessica Opsahl-Ong Daniel Samarov Ichiro Takeuchi William Ratcliff A. Gilad Kusne 1 Materials Measurement Laboratory NIST Gaithersburg Maryland USA 2 Neutron Sciences Directorate ORNL Oak Ridge Tennessee USA 3 Department of Chemistry and Biochemistry University of Maryland College Park Maryland USA4 Maryland Quantum Materials Center College Park Maryland USA 5 Department of Computer Science Cornell University Ithaca New York USA 6 Department of Computer Science Rice University Houston Texas USA 7 Information Technology Laboratory NIST Gaithersburg Maryland USA 4 Maryland Quantum Materials Center College Park Maryland USA8 Department of Materials Science and Engineering University of Maryland College Park Maryland USA 8 Department of Materials Science and Engineering University of Maryland College Park Maryland USA9 NIST Center for Neutron Research NIST Gaithersburg Maryland USA 1 Materials Measurement Laboratory NIST Gaithersburg Maryland USA8 Department of Materials Science and Engineering University of Maryland College Park Maryland USA

Proof of Concept for Disaster Data Collection via Satellite-Based LoRa: Insights from in-Orbit Tests

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Proof of Concept for Disaster Data Collection via Satellite-...

IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

作者： Maulana Ali Arifin Rosza Madina Fahd Moumni Nova Maras Nurul Khamsah Wiwit Suryanto Agfianto Eko Putro Wahyudi Hasbi Mengu Cho Yoji Hirakata Department Computer Science and Electronics Gadjah Mada University Research Center for Satellite Technology National Research and Innovation Agency Bogor Indonesia MicroOrbiter Inc. Tokyo Japan Network Engineering Research Laboratory Kyushu Institute of Technology Fukuoka Japan Department of Physics Gadjah Mada Univesity Yogyakarta Indonesia Laboratory of Lean Satellite Enterprises and In-Orbit Experiments (LaSEINE) Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798331542016

ISBN: (纸本)9798331542023

Indonesia's earthquake vulnerability demands effective data collection, especially in remote areas with communication challenges. While seismometers are widely deployed, critical data points are essential during disasters. Low Earth Orbit (LEO) satellites were selected for this study due to their lower latency and reduced transmission power requirements compared to geostationary satellites, making them ideal for lowpower IoT applications such as LoRa. The combination of LEO satellites and LoRa technology provides a cost-effective, longrange solution for disaster data transmission in remote areas where traditional communication infrastructure may not be feasible. Given LoRa's limited data transfer capacity, preprocessing seismometer minimal data points before transmission is sufficient to optimize data monitoring. This paper demonstrates the feasibility of using LoRa for seismometer data collection. In collaboration with the MicroOrbiter-1 satellite, the in-orbit test used a spreading factor of 7, a bandwidth of 125 kHz, and an RF power output of approximately 38 dBm, successfully transmitted key sensor parameters. Two transmission modes were tested: a 16-byte data packet and a 32-byte data packet for the LoRa transmission. The results indicated that the 32-byte mode provided more reliable performance than the 16-byte mode. This successful test proves that the concept of satellite-based LoRa for seismic data collection is viable and can be applied to disaster data collection in Indonesia with notable considerations.

关键词： Radio frequency Satellites Disasters LoRa Low earth orbit satellites Position control Data collection Reliability engineering System analysis and design Remote sensing

Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation with Heterogeneous Changes

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arXiv 2024年

作者： Yazdani, Danial Branke, Juergen Khorshidi, Mohammad Sadegh Omidvar, Mohammad Nabi Li, Xiaodong Gandomi, Amir H. Yao, Xin Faculty of Engineering & Information Technology University of Technology Sydney Australia Warwick Business school University of Warwick United Kingdom School of Computing Leeds University Business School University of Leeds United Kingdom RMIT University Australia Faculty of Engineering & Information Technology University of Technology Sydney Australia University Research and Innovation Center [EKIK Obuda University Hungary Department of Computing and Decision Sciences Lingnan University China and CERCIA School of Computer Science University of Birmingham United Kingdom

Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiveness in static clustering tasks, their application for tracking optimal clustering solutions or robust clustering over time in dynamic environments remains largely under-explored. This is partly due to a lack of dynamic datasets with diverse, controllable, and realistic dynamic characteristics, hindering systematic performance evaluations of clustering algorithms in various dynamic scenarios. This deficiency leads to a gap in our understanding and capability to effectively design algorithms for clustering in dynamic environments. To bridge this gap, this paper introduces the Dynamic Dataset Generator (DDG). DDG features multiple dynamic Gaussian components integrated with a range of heterogeneous, local, and global changes. These changes vary in spatial and temporal severity, patterns, and domain of influence, providing a comprehensive tool for simulating a wide range of dynamic scenarios. © 2024, CC BY-NC-SA.

关键词： Clustering algorithms

Phase transition in magic with random quantum circuits

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arXiv 2023年

作者： Niroula, Pradeep White, Christopher David Wang, Qingfeng Johri, Sonika Zhu, Daiwei Monroe, Christopher Noel, Crystal Gullans, Michael J. Joint Center for Quantum Information and Computer Science University of Maryland and NIST College ParkMD20742 United States Joint Quantum Institute University of Maryland and NIST College ParkMD20742 United States Chemical Physics Program Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States IonQ Inc. College ParkMD20740 United States Duke Quantum Center Department of Electrical & Computer Engineering Department of Physics Duke University DurhamNC27701 United States

Magic is a property of quantum states that enables universal fault-tolerant quantum computing using simple sets of gate operations. Understanding the mechanisms by which magic is created or destroyed is, therefore, a crucial step towards efficient and practical fault-tolerant computation. We observe that a random stabilizer code subject to coherent errors exhibits a phase transition in magic, which we characterize through analytic, numeric and experimental probes. Below a critical error rate, stabilizer syndrome measurements remove the accumulated magic in the circuit, effectively protecting against coherent errors;above the critical error rate syndrome measurements concentrate magic. A better understanding of such rich behavior in the resource theory of magic could shed more light on origins of quantum speedup and pave pathways for more efficient magic state generation. © 2023, CC BY.

关键词： Random errors

Video-based person re-identification without bells and whistles

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arXiv 2021年

作者： Liu, Chih-Ting Chen, Jun-Cheng Chen, Chu-Song Chien, Shao-Yi Graduate Institute of Electronics and Engineering National Taiwan University Research Center for Information Technology Innovation Academia Sinica Department of Computer Science and Information Engineering National Taiwan University

Video-based person re-identification (Re-ID) aims at matching the video tracklets with cropped video frames for identifying the pedestrians under different cameras. However, there exists severe spatial and temporal misalignment for those cropped tracklets due to the imperfect detection and tracking results generated with obsolete methods. To address this issue, we present a simple re-Detect and Link (DL) module which can effectively reduce those unexpected noise through applying the deep learning-based detection and tracking on the cropped tracklets. Furthermore, we introduce an improved model called Coarse-to-Fine Axial-Attention network (CF-AAN). Based on the typical Non-local network, we replace the non-local module with three 1-D position-sensitive axial attentions, in addition to our proposed coarse-to-fine structure. With the developed CF-AAN, compared to the original non-local operation, we can not only significantly reduce the computation cost but also obtain the state-of-the-art performance (91.3% in rank-1 and 86.5% in mAP) on the large-scale MARS dataset. Meanwhile, by simply adopting our DL module for data alignment, to our surprise, several baseline models can achieve better or comparable results with the current state-of-the-arts. Besides, we discover the errors not only for the identity labels of tracklets but also for the evaluation protocol for the test data of MARS. We hope that our work can help the community for the further development of invariant representation without the hassle of the spatial and temporal alignment and dataset noise. The code, corrected labels, evaluation protocol, and the aligned data will be available at https://***/jackie840129/CF-AAN. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

Monolithic Integration of Quantum Emitters with Silicon Nitride Photonic Platform

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Monolithic Integration of Quantum Emitters with Silicon Nitr...

Conference on Lasers and Electro-Optics (CLEO)

作者： Alexander Senichev Samuel Peana Zachariah O. Martin Omer Yesilyurt Demid Sychev Alexei S. Lagutchev Alexandra Boltasseva Vladimir M. Shalaev Birck Nanotechnology Center Purdue University West Lafayette IN USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA A National Quantum Information Science Research Center of the U.S. Department of Energy Quantum Science Center Oak Ridge TN USA

Silicon nitride has great potential for integrated quantum photonics. We demonstrate monolithic integration of intrinsic quantum emitters in SiN with waveguides which show a room-temperature off-chip count rate of ~10... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Histograms Monolithic integrated circuits Refractive index Silicon nitride Quality control Plasmons Behavioral sciences