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IEEE International Conference on Big Data

作者： Nathan Blackthorn Andrew Arash Mahyari Ashok Srinivasan Department of Computer Science University of West Florida Pensacola FL USA Florida Institute For Human and Machine Cognition (IHMC) Pensacola FL USA Department of Intelligent Systems and Robotics University of West Florida Pensacola FL USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The importance of social determinants of health in shaping equitable public health policies is gaining increasing recognition. Emerging data sources, such as mobility and social media data, are becoming key to public health models. However, privacy concerns often limit access to these sensitive data, as even anonymized datasets are vulnerable to deductive disclosure. A promising solution to this challenge is the use of synthetic populations, which not only safeguard privacy but also enable the exploration of various "what-if" scenarios. Most existing studies on synthetic populations assume full access to complete datasets for training. However, in many public health applications, such as census data, this assumption is unrealistic due to incomplete responses, especially regarding sensitive questions like wealth or education. This paper introduces a novel approach for training Variational AutoEncoders (VAEs) with incomplete data, without resorting to missing value imputation. Instead, the VAEs are trained solely on the observed data. Using the 2019 PUMS dataset for Florida, we successfully train VAEs to generate diverse and flexible synthetic populations. By comparing marginal distributions and utilizing t-SNE for analysis, the results highlight the effectiveness of this method in addressing missing data challenges. This work demonstrates the potential of VAEs in generating synthetic populations for health research, even when complete datasets are unavailable, thereby offering a robust solution to advance public health studies while preserving data privacy.

关键词： Training Data privacy Privacy Social networking (online) Soft sensors Autoencoders Refining Imputation Data models Public healthcare

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Tableaux for Automated Reasoning in Dependently-Typed Higher-Order Logic

arXiv

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arXiv 2024年

作者： Niederhauser, Johannes Brown, Chad E. Kaliszyk, Cezary Department of Computer Science University of Innsbruck Innsbruck Austria Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague Czech Republic School of Computing and Information Systems University of Melbourne Melbourne Australia

Dependent type theory gives an expressive type system facilitating succinct formalizations of mathematical concepts. In practice, it is mainly used for interactive theorem proving with intensional type theories, with PVS being a notable exception. In this paper, we present native rules for automated reasoning in a dependently-typed version (DHOL) of classical higher-order logic (HOL). DHOL has an extensional type theory with an undecidable type checking problem which contains theorem proving. We implemented the inference rules as well as an automatic type checking mode in Lash, a fork of Satallax, the leading tableaux-based prover for HOL. Our method is sound and complete with respect to provability in DHOL. Completeness is guaranteed by the incorporation of a sound and complete translation from DHOL to HOL recently proposed by Rothgang et al. While this translation can already be used as a preprocessing step to any HOL prover, to achieve better performance, our system directly works in DHOL. Moreover, experimental results show that the DHOL version of Lash can outperform all major HOL provers executed on the translation. © 2024, CC BY.

关键词： computer circuits

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Spatio-temporal Motion Planning for Autonomous Vehicles with Trapezoidal Prism Corridors and Bézier Curves

Spatio-temporal Motion Planning for Autonomous Vehicles with...

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American Control Conference (ACC)

作者： Srujan Deolasee Qin Lin Jialun Li John M. Dolan Department of Computer Science & Information Systems Birla Institute of Technology and Science Pilani Rajasthan India Department of Electrical Engineering and Computer Science Cleveland State University Cleveland OH USA Dajiang Innovation Technology Co. Ltd Shenzhen China Robotics Institute Carnegie Mellon University Pittsburgh PA USA

Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free trajectories. A layered motion planning approach with decoupled path and speed planning is widely used for this purpose. This approach is prone to be suboptimal in the presence of dynamic obstacles. Spatial-temporal approaches deal with path planning and speed planning simultaneously; however, the existing methods only support simple-shaped corridors like cuboids, which restrict the search space for optimization in complex scenarios. We propose to use trapezoidal prism-shaped corridors for optimization, which significantly enlarges the solution space compared to the existing cuboidal corridors-based method. Finally, a piecewise Bézier curve optimization is conducted in our proposed corridors. This formulation theoretically guarantees the safety of the continuous-time trajectory. We validate the efficiency and effectiveness of the proposed approach in numerical and CommonRoad simulations.

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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic systems over a Lossy Communication Channel

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Journal of systems science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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Generative Multi-View HRRP Recognition Based on Cascade Generation and Fusion Network

Generative Multi-View HRRP Recognition Based on Cascade Gene...

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IEEE International Conference on Radar

作者： Qiang Zhou Bingqian Yu Yanhua Wang Liang Zhang Le Zheng Difan Zou Xin Zhang School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Department of Computer Science University of Hong Kong Hongkong China HKU Musketeers Foundation Institute of Data Science University of Hong Kong Hongkong China

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

High-resolution range profile (HRRP) is critical for radar target recognition. However, HRRP data for non-cooperative targets are often sparsely collected, leading to limited performance of HRRP target recognition methods. To address the issue of sparse view target recognition, we propose a generative multi-view HRRP recognition (GMVR) model. Firstly, by leveraging the pattern learning and generation ability of deep generative models (DGMs), we propose a viewpoint feature expansion module (VFEM) to generate multi-view features adjacent to sparse viewpoints. Subsequently, based on feature-level fusion, we proposed a multi-view feature fusion module (MVFM). Aided by attention mechanisms, the generated features can be adaptively integrated, thereby reducing the decision bias caused by single-viewpoint HRRP samples. Additionally, we employ transfer learning to mitigate the small sample problem caused by sparse viewpoints. Experimental results demonstrate that the proposed method outperforms various comparison methods, achieving optimal performance.

关键词： Adaptation models Attention mechanisms Target recognition Transfer learning Radar Pattern recognition

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One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion

arXiv

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arXiv 2024年

作者： Bohlinger, Nico Czechmanowski, Grzegorz Krupka, Maciej Kicki, Piotr Walas, Krzysztof Peters, Jan Tateo, Davide Department of Computer Science Technical University of Darmstadt Germany Institute of Robotics and Machine Intelligence Poznan University of Technology Poland Research Department: Systems AI for Robot Learning Germany IDEAS NCBR Warsaw Poland Hessian.AI Centre for Cognitive Science

Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and effectively and possibly transfer, zero or few-shot, to unseen robot embodiments. We introduce URMA, the Unified Robot Morphology Architecture, to close this gap. Our framework brings the end-to-end Multi-Task Reinforcement Learning approach to the realm of legged robots, enabling the learned policy to control any type of robot morphology. The key idea of our method is to allow the network to learn an abstract locomotion controller that can be seamlessly shared between embodiments thanks to our morphology-agnostic encoders and decoders. This flexible architecture can be seen as a potential first step in building a foundation model for legged robot locomotion. Our experiments show that URMA can learn a locomotion policy on multiple embodiments that can be easily transferred to unseen robot platforms in simulation and the real world. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning

arXiv

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arXiv 2023年

作者： Valdeira, Pedro Chi, Yuejie Soares, Cláudia Xavier, João CMU IST United States CMU United States NOVA IST Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Systems and Robotics Instituto Superior Técnico Portugal Department of Computer Science NOVA School of Science and Technology Portugal

Communication efficiency is a major challenge in federated learning (FL). In client-server schemes, the server constitutes a bottleneck, and while decentralized setups spread communications, they do not necessarily reduce them due to slower convergence. We propose Multi-Token Coordinate Descent (MTCD), a communication-efficient algorithm for semi-decentralized vertical federated learning, exploiting both client-server and client-client communications when each client holds a small subset of features. Our multi-token method can be seen as a parallel Markov chain (block) coordinate descent algorithm and it subsumes the client-server and decentralized setups as special cases. We obtain a convergence rate of O(1/T) for nonconvex objectives when tokens roam over disjoint subsets of clients and for convex objectives when they roam over possibly overlapping subsets. Numerical results show that MTCD improves the state-of-the-art communication efficiency and allows for a tunable amount of parallel communications. © 2023, CC BY.

关键词： Efficiency

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Radar HRRP Open Set Recognition Using Hierarchical Prototype Learning

Radar HRRP Open Set Recognition Using Hierarchical Prototype...

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IEEE International Conference on Radar

作者： Yichen Liu Yanhua Wang Liang Zhang Binhua Yu Xiaojing Yang Yu-ang Zhang Le Zheng Difan Zou School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Department of Computer Science University of Hong Kong Hongkong China HKU Musketeers Foundation Institute of Data Science University of Hong Kong Hongkong China

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

High-resolution range profiles (HRRP) are increasingly employed in radar automatic target recognition (RATR). In practical applications of RATR, the environment is open and dynamic, and the recognition model is likely to encounter targets of unseen categories. However, traditional methods focus on a close-set scenario, where all categories in the test set are known during training. To bridge this gap, this paper proposes an open-set recognition (OSR) method based on hierarchical classification, developed from prototype learning. This method improves the generalization performance for recognizing known categories and identifies unknown categories while providing information about how the unknown categories relate to the known ones. Specifically, first, a four-layer category hierarchy is built based on prior knowledge to guide the training and testing stages. Next, we propose a hierarchical prototype loss (HPL) to constrain the feature space extracted by the prototype network so that the feature distribution of objects is consistent with the hierarchical category structure. Lastly, the trained prototype network makes predictions following the hierarchical structure. This operation could provide relationship information between the unknown and known categories. Extensive experiments on measured HRRP data validate the effectiveness of our proposed method for open-set recognition.

关键词： Training Bridges Target recognition Prototypes Radar Feature extraction Testing

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Combinatorial optimization and reasoning with graph neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 5922-5982页

作者： Quentin Cappart Didier Chételat Elias B. Khalil Andrea Lodi Christopher Morris Petar Veličković Department of Computer Engineering and Software Engineering Polytechnique Montréal Montréal Canada CERC in Data Science for Real-Time Decision-Making Polytechnique Montréal Montréal Canada Department of Mechanical & Industrial Engineering University of Toronto Toronto Canada Jacobs Technion-Cornell Institute Cornell Tech and Technion - IIT New York Department of Computer Science RWTH Aachen University Aachen Germany DeepMind London UK

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers.

关键词： combinatorial optimization graph neural networks reasoning

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