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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Johannes Knödtel Markus Fritscher Daniel Reiser Dietmar Fey Marco Breiling Marc Reichenbach Chair for Computer Architecture Friedrich-Alexander-Universität Erlagen-Nürnberg Fraunhofer Institute for Integrated Circuits

ISBN: (数字)9781728166872

ISBN: (纸本)9781728166889

With the emergence of DNN accelerators the main focus of such systems usually lies on utilizing local memories and reducing the size of the processed data, since delay and energy consumption are dominated by data transfer. Utilizig emerging memory technologies, such as ReRAMs, these goals might be attained much easier, due to advantageous non-functional and functional properties. One of the key drawbacks of such systems are reliability and variability of devices of such technologies. To certain degree, DNNs are resilient to soft and hard errors in their memory cells, so these issues might be surmountable depending on the device properties, but eludes trivial analyses known from the digital domain. Here the dynamic behavior of the devices comes into play and must be simulated in order to get a decent degree of confidence on the reliability of the hardware and therefore also yield. In order to tackle this issue we present an accelerator architecture and matching analysis pipeline that allows the user to specify and train a net topology and then test the design against some input activations with different randomized device properties. Using this approach we can estimate the inference results and other system and algorithm level properties in presence of different device level properties which might for example be extracted from real world measurements. Such a system can help the user in the design of their net or give them hints on the required device properties for a given net or aid them in evaluating existing designs.

关键词： Hardware Resistance Integrated circuit modeling Biological neural networks Electrodes computer architecture Task analysis

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Capture of population heterogeneity in trefoil grass with the aid of drone-supported RGB and multispectral data 41

Capture of population heterogeneity in trefoil grass with th...

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41. Jahrestagung 2021 der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft (GIL) - Fokus: "Informations- und Kommunikationstechnologien in kritischen Zeiten" - 2021 41st Annual Meeting of the Society for computer science in Agriculture, Forestry and the Food Industry (GIL) - Focus: "Information and Communication Technologies in Critical Times"

作者： Reuter, Tobias Nahrstedt, Konstantin Wittstruck, Lucas Jarmer, Thomas Kühling, Insa Trautz, Dieter Hochschule Osnabrück University of Applied Sciences Faculty of Agricultural Sciences and Landscape Architecture Am Krümpel 31 Osnabrück49090 Germany Universität Osnabrück Institute of Computer Science Remote Sensing Group Wachsbleiche 27 Osnabrück49090 Germany Kiel University Agronomy and Crop Science Hermann-Rodewald-Str. 9 Kiel24118 Germany

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

arXiv

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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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Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?

arXiv

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arXiv 2022年

作者： Santana, Ítalo Lodi, Andrea Vidal, Thibaut Department of Computer Science Pontifical Catholic University of Rio de Janeiro Brazil Jacobs Technion-Cornell Institute Cornell Tech and Technion - IIT Canada CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains Department of Mathematical and Industrial Engineering Polytechnique Montréal Canada

Extensive research has been conducted, over recent years, on various ways of enhancing heuristic search for combinatorial optimization problems with machine learning algorithms. In this study, we investigate the use of predictions from graph neural networks (GNNs) in the form of heatmaps to improve the Hybrid Genetic Search (HGS), a state-of-the-art algorithm for the Capacitated Vehicle Routing Problem (CVRP). The crossover and local-search components of HGS are instrumental in finding improved solutions, yet these components essentially rely on simple greedy or random choices. It seems intuitive to attempt to incorporate additional knowledge at these levels. Throughout a vast experimental campaign on more than 10,000 problem instances, we show that exploiting more sophisticated strategies using measures of node relatedness (heatmaps, or simply distance) within these algorithmic components can significantly enhance performance. However, contrary to initial expectations, we also observed that heatmaps did not present significant advantages over simpler distance measures for these purposes. Therefore, we faced a common —though rarely documented— situation of overkill: GNNs can indeed improve performance on an important optimization task, but an ablation analysis demonstrated that simpler alternatives perform equally well. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Scalable Deep Learning for Categorization of Satellite Images

Scalable Deep Learning for Categorization of Satellite Image...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： B Swapna Radhika Venkatessan Fathima Taskeen K IndraPriya D Manjula D Surendiran Muthukumar Department of Electronics and Communication Engineering Dr. M.G.R. Educational and Research Institute Chennai Tamil Nadu India the Department Faculty of Architecture Dr. M.G.R. Educational and Research Institute Chennai Tamil Nadu India Faculty of Architecture Dr. M.G.R. Educational and Research Institute Chennai Tamil Nadu India Department of Community Medicine Karpagam Faculty of Medical Sciences and Research Coimbatore Tamil Nadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India

Satellite imagery serves a crucial role in diverse applications like disaster response, law enforcement, and environmental monitoring. These tasks often require manual identification of objects and structures within the images. However, due to the extensive geographic regions involved and a limited number of analysts available, there’s a growing need for automation. Traditional methods of object detection and classification are proving insufficient and unreliable for addressing this challenge. This is where deep learning comes into play—a collection of machine learning techniques that holds the potential to automate these tasks effectively. In particular, convolutional neural networks (CNNs), a subset of deep learning, have shown remarkable promise in understanding images. This study focuses on applying CNNs to the complex task of recognizing various objects and facilities in high-resolution, multi-spectral satellite images. The research introduces a deep learning system that takes advantage of both satellite metadata and image features to accurately classify the dataset into different categories. The implementation of this system is carried out using Python, leveraging the Keras and TensorFlow libraries. Comparative analysis against existing systems reveals a significant enhancement in overall accuracy offered by this approach.

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What Not to Do in Facial Infrared Thermographic Measurements: A Post Data Enhancement

SSRN

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SSRN 2023年

作者： Pike, Matthew Yeboah, Siegfried Fu, Xianyun School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of the Built Environment and Architecture London South Bank University 103 Borough Road LondonSE1 0AA United Kingdom Department of Architecture and Built Environment The University of Nottingham Ningbo China 199 Taikang East Road Ningbo315100 China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute Ningbo315040 China

In this study, measurement errors associated with nose tip temperature data collected from participants in a mental workload assessment using a thermal imaging camera have been evaluated. Facial landmark identification techniques involving manual data labelling software and an automated deep learning-based approach for landmark tracking were compared to the inherent tracking features of the thermal camera. The results show that the average nose tip temperatures using Manual Thermal Landmark Annotation (MTLA) measurement, Automated Facial Feature Tracking (AFFT) approach and Thermal Spot Tracking (TST) were 29.93°C, 30.19°C and 31.06°C respectively. Mean relative error obtained between MTLA measurements and the TST was 0.05°C while between the MTLA and the AFFT approach it was 0.013°C. The manual landmark annotation enabled accurate nose tip coordinates to be obtained to establish a ground truth, which allowed comparison between the deep learning-based approach and the camera's facial spot tracking feature. © 2023, The Authors. All rights reserved.

关键词： Temperature measurement

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Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding

arXiv

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arXiv 2022年

作者： Xiu, Haoyi Liu, Xin Wang, Weimin Kim, Kyoung-Sook Shinohara, Takayuki Chang, Qiong Matsuoka, Masashi Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Artificial Intelligence Research Center AIST Tokyo Japan DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China Innovation Technology Office Research Center PASCO Corporation Tokyo Japan Department of Computer Science Tokyo Institute of Technology Tokyo Japan

Learning point clouds is challenging due to the lack of connectivity information, i.e., edges. Although existing edge-aware methods can improve the performance by modeling edges, how edges contribute to the improvement is unclear. In this study, we propose a method that automatically learns to enhance/suppress edges while keeping the its working mechanism clear. First, we theoretically figure out how edge enhancement/suppression works. Second, we experimentally verify the edge enhancement/suppression behavior. Third, we empirically show that this behavior improves the performance. In general, we observe that the proposed method achieves competitive performance in point cloud classification and segmentation tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Evaluating Large Language Models on Spatial Tasks: A Multi-Task Benchmarking Study

arXiv

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arXiv 2024年

作者： Xu, Liuchang Zhao, Shuo Lin, Qingming Chen, Luyao Luo, Qianqian Wu, Sensen Ye, Xinyue Feng, Hailin Du, Zhenhong School of Mathematics and Computer Science Zhejiang Agriculture and Forestry University Hangzhou311300 China School of Earth Sciences Zhejiang University Hangzhou310058 China Department of Landscape Architecture and Urban Planning Center for Geospatial Sciences Applications and Technology Texas A&M University College StationTX77843 United States Financial Big Data Research Institute Sunyard Technology Co. Ltd Hangzhou310053 China

The advent of large language models such as ChatGPT, Gemini, and others has underscored the importance of evaluating their diverse capabilities, ranging from natural language understanding to code generation. However, their performance on spatial tasks has not been comprehensively assessed. This study addresses this gap by introducing a novel multi-task spatial evaluation dataset, designed to systematically explore and compare the performance of several advanced models on spatial tasks. The dataset encompasses twelve distinct task types, including spatial understanding and simple route planning, each with verified, accurate answers. We evaluated multiple models, including OpenAI’s gpt-3.5-turbo, gpt-4-turbo, gpt-4o, ZhipuAI’s glm-4, Anthropic’s claude-3-sonnet-20240229, and MoonShot’s moonshot-v1-8k, through a two-phase testing approach. Initially, we conducted zero-shot testing, followed by categorizing the dataset by difficulty and performing prompt tuning tests. Results indicate that gpt-4o achieved the highest overall accuracy in the first phase, with an average of 71.3%. Although moonshotv1-8k slightly underperformed overall, it surpassed gpt-4o in place name recognition tasks. The study also highlights the impact of prompt strategies on model performance in specific tasks. For example, the Chain-of-Thought (CoT) strategy increased gpt-4o’s accuracy in simple route planning from 12.4% to 87.5%, while a one-shot strategy enhanced moonshot-v1-8k’s accuracy in mapping tasks from 10.1% to 76.3%. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

CertainOdom: Uncertainty Weighted Multi-task Learning Model ...

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IEEE International Conference on Robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

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Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution

arXiv

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arXiv 2022年

作者： Xiu, Haoyi Liu, Xin Wang, Weimin Kim, Kyoung-Sook Shinohara, Takayuki Chang, Qiong Matsuoka, Masashi Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Artificial Intelligence Research Center AIST Tokyo Japan DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science Tokyo Institute of Technology Tokyo Japan Innovation Technology Office Research Center PASCO Corporation Tokyo Japan

Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information. Most prior works model local geometry using various convolution operations. We observe that the convolution can be equivalently decomposed as a weighted combination of a local and a global component. With this observation, we explicitly decouple these two components so that the local one can be enhanced and facilitate the learning of local surface geometry. Specifically, we propose Laplacian Unit (LU), a simple yet effective architectural unit that can enhance the learning of local geometry. Extensive experiments demonstrate that networks equipped with LUs achieve competitive or superior performance on typical point cloud understanding tasks. Moreover, through establishing connections between the mean curvature flow, a further investigation of LU based on curvatures is made to interpret the adaptive smoothing and sharpening effect of LU. The code will be available. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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