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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Moïse Blanchard Junhui Zhang Patrick Jaillet Operations Research Center MIT Cambridge MA Department of Electrical Engineering and Computer Science MIT Cambridge MA

We propose a family of recursive cutting-plane algorithms to solve feasibility problems with constrained memory, which can also be used for first-order convex optimization. Precisely, in order to find a point within a ball of radius ε with a separation oracle in dimension d—or to minimize 1-Lipschitz convex functions to accuracy ε over the unit ball—our algorithms use O(d2/p ln 1/ε) bits of memory, and make O((C d/p ln 1/ε)p) oracle calls, for some universal constant C ≥ 1. The family is parametrized by p ∈ [d] and provides an oracle-complexity/memory trade-off in the sub-polynomial regime ln 1/ε ≫ ln d. While several works gave lower-bound trade-offs (impossibility results) [31, 5]—we explicit here their dependence with ln 1/ε, showing that these also hold in any sub-polynomial regime—to the best of our knowledge this is the first class of algorithms that provides a positive trade-off between gradient descent and cutting-plane methods in any regime with ε ≤ 1/√d. The algorithms divide the d variables into p blocks and optimize over blocks sequentially, with approximate separation vectors constructed using a variant of Vaidya's method. In the regime ε ≤ d-Ω(d), our algorithm with p = d achieves the information-theoretic optimal memory usage and improves the oracle-complexity of gradient descent.

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FogROS2-LS: A Location-Independent Fog robotics Framework for Latency Sensitive ROS2 Applications

FogROS2-LS: A Location-Independent Fog Robotics Framework fo...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Kaiyuan Chen Michael Wang Marcus Gualtieri Nan Tian Christian Juette Liu Ren Jeffrey Ichnowski John Kubiatowicz Ken Goldberg Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Robert Bosch Research and Technology Center North America Sunny-Vale CA USA Robotics Institute Carnegie Mellon University Department of Industrial Engineering and Operations Research University of California Berkeley CA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In Cloud robotics, long system latency due to varying network conditions can cause instability and collisions. However, this can be minimized in the almost univeral case where there are multiple sources available for cloud servers. By extending anycast routing, we introduce FogROS2-Latency-Sensitive, a Fog robotics framework that offers secure, location-independent connections between robots and latency-sensitive cloud-based servers. FogROS2-LS offloads conventional on-board state estimators and feedback controllers to Cloud and Edge compute hardware without modifying existing applications in ROS2. In the presence of multiple identical services, FogROS2-LS dynamically identifies and transitions to the optimal service deployment that meets latency requirements, thereby empowering robots with limited on-board computing capacity to safely and efficiently navigate dynamic, human-dense environments. We evaluate FogROS2-LS with two latency sensitive case studies: (1) Collision Avoidance: a robot arm guided by visual feedback from consistent distance estimation and collision checking on Cloud and Edge. FogROS2-LS reduces collision failures by up to 8.5x by selecting the best available server, and (2) Target Tracking: FogROS2-LS enables robust and continuous target following and can recover from network failures. Videos and code are available on the website https://***/view/fogros2-ls.

关键词： Visualization Target tracking Navigation Robot sensing systems Routing Manipulators Hardware

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A Machine Learning-Based Forecasting Model with Deep Learning Capabilities for Unbalanced Time Series Data 2

A Machine Learning-Based Forecasting Model with Deep Learnin...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Amudha, A. Suri, Sahil Sindhu, M. Goyal, Shubhi Kukreja, Mansi Pathak, Kishor R. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Information Technology Pune India

ISBN: (纸本)9798350388916

During the last several years, deep learning capabilities have surpassed those of more conventional models on a wide variety of machine learning-related tasks. Neural networks with deep layers have indeed been effectively used to time series forecasting issues, which are crucial to the field of data mining. Their ability to instantly understand the temporal connections contained in time series has made them a viable option. Yet, deciding on a deep neural network architecture and then optimizing its parameters calls for a high level of skill. Thus, it is essential to conduct comprehensive studies of the predictive capacities of all current architectural frameworks. The completion of this work is hampered by two primary factors: (1) a complete examination of the most recent research on depth learning's application to time series forecasting, and (2) an empirical inquiry analysing the effectiveness of the most commonly used architectures. Seven distinct deep learning models are evaluated and compared for their efficiency and accuracy. We present scores and distributions based on our analysis of the suggested models trained with various architectures and hyperparameter settings. The 50,000 time series in the databases are used to solve 12 unique forecasting issues. Using this data to train approximately 38,000 models, we present the largest deep learning research to date for forecasting time series. While LSTMs were shown to be the most significantly predictive, both LSTMs & CNNs showed promise in this research. Comparable performance is attained by CNNs, but with less variance in results as well as a more based on the best available overall. © 2024 IEEE.

关键词： Prediction models

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An Analysis of Image-Based Segmentation Algorithms for computer Vision Applications 15

An Analysis of Image-Based Segmentation Algorithms for Compu...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Deepak, J. Deshmukh, Vishwesh Anandhasilambarasan, D. Kandhari, Harsimrat Jayashankari, J. Goyal, Shitij School of Engineering and Technology Mechaical Engineering JAIN Deemed to be University Karnataka Bangalore India Electrical Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore641021 India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Quantum University Research Center Quantum University India

ISBN: (纸本)9798350370249

— Photo-based totally segmentation algorithms are an important place in trendy research in laptop vision for an extensive variety of trendy applications together with item popularity, facial recognition, and autonomous navigation. Such algorithms normally hire strategies that contain reading pix to understand edges, shapes, and different features in the surroundings. By means of making use of these techniques, scene objects may be diagnosed and segmented into distinct parts, making an allowance for further analysis and programs. This paper discusses the development and packages of brand-new photograph-based segmentation algorithms, with an emphasis on algorithms based on pattern recognition and machine learning. Examples are supplied to illustrate the present-day country's contemporary art and provide insights into the destiny development of those algorithms. This analysis helps to discover capacity areas for development and provides a top-level view state-of-the-art the overall performance of present-day current algorithms. ©20XX IEEE.

关键词： Image segmentation

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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhou, Yang Zhu, Shiqiang Huang, Huang Li, Yuehua Gu, Jason The Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China The Beijing Institute of Control Engineering Beijing100094 China The Beijing Research Institute Zhejiang Laboratory Beijing100094 China The Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory. © 2022 IEEE.

关键词： Motion planning

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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High-Performance Magnetic-core Coils for Targeted Rodent Brain Stimulations

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Biomedical engineering Frontiers 2022年第1期3卷 82-92页

作者： Hedyeh Bagherzadeh Qinglei Meng Hanbing Lu Elliott Hong Yihong Yang Fow-Sen Choa Department of Computer Science and Electrical Engineering University of MarylandBaltimore CountyBaltimoreMD 21250USA Neuroimaging Research Branch National Institute on Drug AbuseNational Institutes of HealthBaltimoreMD 21224USA Maryland Psychiatric Research Center Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMD 21201USA

Objective and Impact *** is a need to develop rodent coils capable of targeted brain stimulation for treating neuropsychiatric disorders and understanding brain *** describe a novel rodent coil design to improve the focality for targeted stimulations in small rodent *** magnetic stimulation(TMS)is becoming increasingly important for treating neuropsychiatric disorders and understanding brain *** studies permit invasive manipulations and are essential for the mechanistic understanding of TMS effects and explorations of therapeutic outcomes in disease ***,existing TMS tools lack focality for targeted ***,there has been limited fundamental research on developing coils capable of focal stimulation at deep brain regions on small animals like *** this study,ferromagnetic cores are added to a novel angle-tuned coil design to enhance the coil performance regarding penetration depth and *** simulations and experimental electric field measurements were conducted to optimize the coil *** proposed coil system demonstrated a significantly smaller stimulation spot size and enhanced electric field decay rate in comparison to existing *** the ferromagnetic core reduces the energy requirements up to 60%for rodent brain *** simulated results are validated with experimental measurements and demonstration of suprathreshold rodent limb excitation through targeted motor cortex *** newly developed coils are suitable tools for focal stimulations of the rodent brain due to their smaller stimulation spot size and improved electric field decay rate.

关键词： stimulation manipulation becoming

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MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutional Networks for 3D Human Pose Estimation

MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutiona...

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IEEE International Conference on robotics and Biomimetics

作者： Shanshan Ji Qiwei Meng Wen Wang Zheyuan Lin Te Li Minhong Wan Chunlong Zhang Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

In human-robot interaction (HRI), human pose estimation is a necessary technology for the robot to perceive the dynamic environment and make interactive actions. Recently, graph convolutional networks (GCNs) have been increasingly used for 2D to 3D pose estimation tasks since the skeleton topologies can be viewed as graph structures. In this paper, we propose a novel graph convolutional network architecture, Multi-scale Multi-branch Fusion Graph Convolutional Networks (MSMB-GCN), for 3D Human Pose Estimation(3D HPE) task. The proposed model consists of multiple GCN blocks with a multi-branch architecture. This multi-branch architecture enables the model to get multi-scale features for human skeletal representations. The group of GCN blocks, which has strong multi-level feature extraction capabilities, allows the model to learn global and local features, lower-level and higher-level features. Experiment results on the HumanPose benchmark demonstrate that our model outperforms the state-of-the-art and ablation studies validate the effectiveness of our approach.

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Anomaly Detection for Real-World Cyber-Physical Security Using Quantum Hybrid Support Vector Machines

Anomaly Detection for Real-World Cyber-Physical Security Usi...

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IEEE computer Society Annual Symposium on VLSI

作者： Tyler Cultice Md. Saif Hassan Onim Annarita Giani Himanshu Thapliyal Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States GE Vernova Advanced Research Center United States

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

Cyber-physical control systems are critical infrastructures designed around highly responsive feedback loops that are measured and manipulated by hundreds of sensors and controllers. Anomalous data, such as from cyber-attacks, greatly risk the safety of the infrastructure and human operators. With recent advances in the quantum computing paradigm, the application of quantum in anomaly detection can greatly improve identification of cyber-attacks in physical sensor data. In this paper, we explore the use of strong pre-processing methods and a quantum-hybrid Support Vector Machine (SVM) that takes advantage of fidelity in parameterized quantum circuits to efficiently and effectively flatten extremely high dimensional data. Our results show an F-1 Score of 0.86 and accuracy of 87% on the HAI CPS dataset using an 8-qubit, 16-feature quantum kernel, performing equally to existing work and 14% better than its classical counterpart.

关键词： Support vector machines Very large scale integration Vectors Sensor systems Sensors Safety Security

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E2SIFT: Neuromorphic SIFT via Direct Feature Pyramid Recovery from Events

E2SIFT: Neuromorphic SIFT via Direct Feature Pyramid Recover...

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IEEE International Conference on Image Processing

作者： Chris Henry Paras Maharjan Zhu Li George York Department of Computer Science and Electrical Engineering University of Missouri–Kansas City Academy Center for UAS Research United States Air Force Academy

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In recent years, event cameras have achieved significant attention due to their advantages over conventional cameras. Event cameras have high dynamic range, no motion blur, and high temporal resolution. Contrary to traditional cameras which generate intensity frames, event cameras output a stream of asynchronous events based on brightness change. There is extensive ongoing research on performing computer vision tasks like object detection, classification, etc via the event camera. However, due to the unconventional output format of the event camera, it is difficult to perform computer vision tasks directly on the event stream. Mostly, works reconstruct the intensity image from the event stream and then perform such tasks. An important and crucial task is feature detection and description. Scale-invariant feature transform (SIFT) is a widely-used scale-invariant keypoint detector and descriptor that is invariant to transformations like scale, rotation, noise, and illumination. In this work, given an event voxel, we directly generate the LoG pyramid for SIFT keypoint detection. We fit a 3rd-degree polynomial and calculate the polynomial roots to compute the scale-space extrema response for SIFT keypoint detection. Since the extrema computation is performed after LoG thresholding, the solution is computationally less expensive. Experimental results validate the effectiveness of our system.

关键词： Thresholding (Imaging) Neuromorphics Detectors Transforms Streaming media Vision sensors Cameras

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