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Quality Ranking for Synthetically Generated Images

Quality Ranking for Synthetically Generated Images

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Sekmen, Ali Bilgin, Bahadir Sereff, Guy Tennessee State University Department of Computer Science NashvilleTN37209 United States Amazon Robotics Ai Computer Vision Software Development NashvilleTN37209 United States

ISBN: (纸本)9798350324457

This paper proposes a novel metric for evaluating the quality of images of the same category generated by a synthetic image generator. The proposed method assumes that the points representing synthetic images in a feature space lie on a manifold, and it aims to determine the curvatures of the manifold at those points. The lower the angle, the higher the quality of the generated images is considered to be. Let F be the set of data points in a D-dimensional feature space where each point represents one synthetically generated image. Let M be the corresponding manifold to match F. For each data point y inRD on M, a number of neighboring points are determined. A local subspace S is matched for y and a local subspace is also matched for each of its neighboring points. Then, a set of weighted angles between S and each neighboring local subspace are computed. The average of those weighted angles is used as a measure of curvature of M at y. Experimental results demonstrate the effectiveness of the proposed metric in evaluating the quality of synthetically generated images. © 2023 IEEE.

关键词： synthetic image generation Synthetic image quality ranking

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

引用

30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Optimization of the Storage Location Assignment Problem Using Nested Annealing 12th

Optimization of the Storage Location Assignment Problem Us...

引用

11th and 12th International Conferences on Operations Research and Enterprise Systems, ICORES 2022 and 2023

作者： Oxenstierna, Johan van Rensburg, Louis Janse Stuckey, Peter J. Krueger, Volker Department of Computer Science Lund University Lund Sweden Faculty of Information Technology Monash University Melbourne Australia Flux Robotics Caltowie Australia Kairos Logic AB Lund Sweden

ISBN: (纸本)9783031496615

The Storage Location Assignment Problem (SLAP) has a significant impact on the efficiency of warehouse operations. We propose a multi-phase optimizer for the SLAP, where the quality of an assignment is based on distance estimates of future-forecasted order-picking. Candidate assignments are first sampled using a Markov Chain accept/reject method. Order-picking Traveling Salesman Problems (TSPs) are then modified according to the assignments and solved. The model is graph-based and generalizes to any obstacle layout in two dimensions. We investigate whether optimization speed-ups are possible using methods such as cost approximation, rejection of samples with low approximate cost and restarts from local minima. Results demonstrate that these methods improve performance, with total travel-cost reductions of up to 30% within 8 h of CPU-time. We share a public repository with SLAP instances and corresponding benchmark results on the generalizable TSPLIB format. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hamming distance

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Deep Transformer Network and CNN Model with About 200k Parameters to Classify P300 EEG Signal

Deep Transformer Network and CNN Model with About 200k Param...

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Otarbay, Zhenis Orazaly, Merey Assaiyn, Yerkegul Chagirov, Sanzhar Pernebayev, Sanzhar Tleuzhan, Amir Seds Nazarbayev University Department of Robotics Astana Kazakhstan Astana It University Department of Computer Science Astana Kazakhstan Astana It University Dept of Is&cs Astana Kazakhstan

ISBN: (纸本)9798350335040

This research gathers participants' electroencephalographic (EEG) data to build classifiers that can decode users' mental states. Although deep learning models can incorporate domain-dependent feature extraction into the classifier design, the architecture selection process for BCIs is often based on domain expertise. This paper examines whether it can build reliable classifiers for interpreting P300 event-related potentials using a systematic model selection mixed with common deep learning architectures. We report the findings of a CNNTransformer network model with 50k and 200k parameters in particular. We systematically investigate if a hybrid architecture which is Transformer and CNN, can improve the classification performance of P300 datasets. Because we would like to know how to improve the model selection process for P300 signals data analysis. To help the readers understand that the best model selection strategy with more muscular models than the existing ones may make the lives of stroke patients more convenient. If our readers are targeting to know the modern muscular deep learning architectures to analyze their recorded P300 dataset, then we recommend reading this paper. © 2023 IEEE.

关键词： Deep neural networks

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Deep Compliant Control for Legged Robots

Deep Compliant Control for Legged Robots

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IEEE International Conference on robotics and Automation (ICRA)

作者： Adrian Hartmann Dongho Kang Fatemeh Zargarbashi Miguel Zamora Stelian Coros Computational Robotics Lab in the Department of Computer Science ETH Zurich Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Control policies trained using deep reinforcement learning often generate stiff, high-frequency motions in response to unexpected disturbances. To promote more natural and compliant balance recovery strategies, we propose a simple modification to the typical reinforcement learning training process. Our key insight is that stiff responses to perturbations are due to an agent’s incentive to maximize task rewards at all times, even as perturbations are being applied. As an alternative, we introduce an explicit recovery stage where tracking rewards are given irrespective of the motions generated by the control policy. This allows agents a chance to gradually recover from disturbances before attempting to carry out their main tasks. Through an in-depth analysis, we highlight both the compliant nature of the resulting control policies, as well as the benefits that compliance brings to legged locomotion. In our simulation and hardware experiments, the compliant policy achieves more robust, energy-efficient, and safe interactions with the environment.

关键词： Training Legged locomotion Uncertainty Tracking Perturbation methods Process control Energy efficiency

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Retrospective of Kazakh-Russian Sign Language Corpus Formation 11

Retrospective of Kazakh-Russian Sign Language Corpus Formati...

引用

11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources, sign-lang@LREC-COLING 2024

作者： Imashev, Alfarabi Kydyrbekova, Aigerim Mukushev, Medet Sandygulova, Anara Islam, Shynggys Israilov, Khassan Makazhanov, Aibek Yessenbayev, Zhandos Department of Robotics Engineering Nazarbayev University Kazakhstan Computer Science Lab National Laboratory Astana Nazarbayev University Kazakhstan Public Association "Kazakh Deaf Society" Astana Branch Kazakhstan

ISBN: (纸本)9782493814302

Sign language (SL) is a mode of communication that, in most cases, relies on visual perception exclusively and uses the visual-gestural modality. The advent of machine learning techniques has expanded the range of potential applications, not only in industry but also in addressing societal needs. Previous research has already demonstrated encouraging outcomes in developing sign language recognition systems that are both quite accurate and resilient. Nevertheless, the effectiveness and use of algorithms are impacted not only by their accessibility but also, at times to a greater extent, by the presence of substantial quantities of pertinent data. At the start of the local sign language corpus collection in 2015, there was a notable deficit of local Kazakh-Russian Sign Language data available for computer vision and machine-learning tasks. There were already corpora of another lexically close language, Russian Sign Language, but they were aimed at and tailored for lingustic research. We initiated the procedure by collecting data appropriate for machine-learning purposes. The subsets have been incorporated into the principal corpus and will be subject to future enhancements and refinements. This paper provides an overview of the collected components of the Kazakh-Russian Sign Language Corpus and the resulting outcomes derived from them. © 2024 ELRA Language Resources Association: CC BY-NC 4.0.

关键词： Machine learning

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Intersection Coordination with Priority-Based Search for Autonomous Vehicles 37

Intersection Coordination with Priority-Based Search for Aut...

引用

37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Li, Jiaoyang Hoang, The Anh Lin, Eugene Vu, Hai L. Koenig, Sven Robotics Institute Carnegie Mellon University United States Department of Civil Engineering Monash University Australia Computer Science Department University of Southern California United States

ISBN: (纸本)9781577358800

The development of connected and autonomous vehicles opens an opportunity to manage intersections without signals. One promising approach is to use a central autonomous intersection manager to optimize the movement of the vehicles in the intersection. Existing work uses Mixed Integer Linear Programming (MILP) to find optimal solutions for this problem but is time-consuming and cannot be applied in real-time. On the other hand, the coordination of the vehicles is essentially a Multi-Agent Path Finding (MAPF) problem, for which dozens of efficient algorithms have been proposed in recent years. Inspired by these MAPF algorithms, we propose a three-level algorithm called PSL to solve the intersection coordination problem. Theoretically, PSL is complete and polynomial-time in the number of vehicles. Empirically, PSL runs significantly faster with only a slight compromise in the solution quality than the optimal MILP method. It also generates significantly better solutions with a slightly larger runtime than the traditional First-Come-First-Served strategy. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Autonomous vehicles

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Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning 38

Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Rep...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Paolino, Raffaele Maskey, Sohir Welke, Pascal Kutyniok, Gitta Department of Mathematics LMU Munich Germany Germany Faculty of Computer Science TU Wien Austria Institute for Robotics and Mechatronics DLR-German Aerospace Center Germany Department of Physics and Technology University of Tromsø Norway

We introduce r-loopy Weisfeiler-Leman (r-`WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, r-`MPNN, that can count cycles up to length r+2. Most notably, we show that r-`WL can count homomorphisms of cactus graphs. This extends 1-WL, which can only count homomorphisms of trees and, in fact, we prove that r-`WL is incomparable to k-WL for any fixed k. We empirically validate the expressive and counting power of r-`MPNN on several synthetic datasets and demonstrate the scalability and strong performance on various real-world datasets, particularly on sparse graphs. Our code is available on GitHub. © 2024 Neural information processing systems foundation. All rights reserved.

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SceneSense: Diffusion Models for 3D Occupancy Synthesis from Partial Observation

SceneSense: Diffusion Models for 3D Occupancy Synthesis from...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Alec Reed Brendan Crowe Doncey Albin Lorin Achey Bradley Hayes Christoffer Heckman Department of Computer Science Intelligent Robotics Laboratory University of Colorado Boulder

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. This planning paradigm can lead to unintuitive exploration or replanning latency when entering areas that were previous obstructed from view. To address this we present SceneSense, a real-time 3D diffusion model for synthesizing 3D occupancy information from partial observations that effectively predicts these occluded or out of view geometries for use in future planning and control frameworks. SceneSense uses a running occupancy map and a single RGB-D camera to generate predicted geometry around the platform at runtime, even when the geometry is occluded or out of view. Our architecture ensures that SceneSense never overwrites observed free or occupied space. By preserving the integrity of the observed map, SceneSense mitigates the risk of corrupting the observed space with generative predictions. While SceneSense is shown to operate well using a single RGB-D camera, the framework is flexible enough to extend to additional modalities. Unlike existing models that necessitate multiple views and offline scene synthesis, or are focused on filling gaps in observed data, our findings demonstrate that SceneSense is an effective approach to estimating unobserved local occupancy information at runtime. Local occupancy predictions from SceneSense are shown to better represent the ground truth occupancy distribution during the test exploration trajectories than the running occupancy map. The source code can be found on our website: https://***/scenesense/

关键词： Geometry Three-dimensional displays Runtime Source coding Diffusion models Cameras Real-time systems Planning Trajectory Intelligent robots

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Numerical Method for Complete Solution of the Optimal Control Problem 9

Numerical Method for Complete Solution of the Optimal Contro...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Diveev, Askhat Federal Research Center 'Computer Science and Control' The Russian Academy of Sciences Department of Robotics Control Vavilova str. 44 Moscow119333 Russia

ISBN: (纸本)9798350311402

The work is devoted to the numerical complete solution of the optimal control problem. The complete solution means the solution of the optimal control problem together with the solution of the control synthesis problem to stabilize the movement of the control object along the found optimal trajectory. To solve this problem, evolutionary computations and symbolic regression are used. First, the optimal control problem by an evolutionary algorithm in the classical formulation is solved, after that the control synthesis problem by a method of symbolic regression is solved. The statement of the complete optimal control problem is presented. The computational experiment considers the solution of the complete optimal control problem for a quadcopter. © 2023 IEEE.

关键词： Numerical methods

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