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arXiv 2022年

作者： Jauregui, Jeffrey L. Perales, Raquel Portegies, Jacobus W. Dept. of Mathematics Union College 807 Union St. SchenectadyNY12308 United States CONACyT Research The Math Institute The National Autonomous University of Mexico Oaxaca Mexico Dept. of Mathematics and Computer Science Eindhoven University of Technology De Zaale Eindhoven Netherlands

The concept of the capacity of a compact set in Rn generalizes readily to noncompact Riemannian manifolds and, with more substantial work, to metric spaces (where multiple natural definitions of capacity are possible). Motivated by analytic and geometric considerations, and in particular Jauregui's definition of capacity-volume mass and Jauregui and Lee's results on the lower semicontinuity of the ADM mass and Huisken's isoperimetric mass, we investigate how the capacity functional behaves when the background spaces vary. Specifically, we allow the background spaces to consist of a sequence of local integral current spaces converging in the pointed Sormani-Wenger intrinsic flat sense. For the case of volume-preserving (VF) convergence, we prove two theorems that demonstrate an upper semicontinuity phenomenon for the capacity: one version is for balls of a fixed radius centered about converging points;the other is for Lipschitz sublevel sets. Our approach is motivated by Portegies' investigation of the semicontinuity of eigenvalues under VF convergence. We include examples to show the semicontinuity may be strict, and that the volume-preserving hypothesis is necessary. Finally, there is a discussion on how capacity and our results may be used towards understanding the general relativistic total mass in non-smooth settings. Copyright © 2022, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Threshold Decision-Making Dynamics Adaptive to Physical Constraints and Changing Environment

arXiv

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arXiv 2023年

作者： Amorim, Giovanna Santos, María Park, Shinkyu Franci, Alessio Leonard, Naomi Ehrich The Dept. of Mechanical and Aerospace Engineering Princeton Univ. PrincetonNJ08544 United States Computer Electrical and Math. Science and Eng. Div. Thuwal23955-6900 Saudi Arabia The Department of Electrical Engineering and Computer Science University of Liege 10 Allée de la Découverte Liège4000 Belgium The WEL Research Institute Wavre Belgium

We propose a threshold decision-making framework for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dynamics model that represents the evolution of an agent’s preference for a particular task with the physical dynamics of the agent. We prove the bifurcation that governs the behavior of the coupled dynamics. We show by means of the bifurcation behavior how the coupled dynamics are adaptive to the physical constraints of the agent. We also show how the bifurcation can be modulated to allow the agent to switch tasks based on thresholds adaptive to environmental conditions. We illustrate the benefits of the approach through a multi-robot task allocation application for trash collection. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

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An Automation Perception for Cotton Crop Disease Detection Using Machine Learning

An Automation Perception for Cotton Crop Disease Detection U...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Ritu Aggarwal Eshaan Aggarwal Anurag Jain Tanupriya Choudhury Ketan Kotecha Computer Science and Engineering Seth Jai Prakash Mukand lal Institute of Engineering and Technology Radaur India Computer Science and Engineering (MMDU) Maharishi Markandeshwar Engineering College Mullana MMU India School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India CSE Dept. Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University)India Pune Maharashtra India

Cotton crop disease detection and classification based on images of leaves is a significant objective in agriculture. Cotton crops play a vital role in India. Every year, due to the attack of diseases, cotton production is decreasing. The main reasons for disease in plants are the pests, insects, and pathogens used; if they are not controlled on time, they affect productivity. In advancing digital image processing technologies, machine learning and other techniques that identify early disease detection in plants are proposed. This paper focuses on improving disease detection in cotton crop plants and leaves. Machine learning is used to identify and predict cotton plant disease using images and leaves collected in an uncontrolled environment. This study uses machine learning to review the different problems identified regarding plant disease. Various experimental configurations are investigated to analyze the impacts of different leaf classes, plant disease combinations, and their categories. Cotton crops are detected to classify the various types of cotton plants. Cotton plant diseases have a wide range of illnesses, from bacterial deficiency to bacterial, fungal, viral, and vitamin and nutrient deficiency. The proposed approach indicates the cotton crop plant in the leaf disease by their implementation results.

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Automated Bell Inequality Violation using Uncalibrated Fiber Squeezing Polarization Controllers

Automated Bell Inequality Violation using Uncalibrated Fiber...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Evan Dowling Mark Morris Gerald Baumgartner Rajarshi Roy Thomas E. Murphy Inst. for Research in Electronics & Applied Physics University of Maryland College Park Maryland USA Dept. of Physics University of Maryland College Park Maryland USA Laboratory for Telecommunication Sciences College Park Maryland USA Inst. for Physical Science & Technology University of Maryland College Park Maryland USA Dept. of Electrical & Computer Engineering University of Maryland College Park Maryland USA

We experimentally report on a real-time self-guided method to search for maximal CHSH violations between two observers sharing polarization entangled photon pairs using uncalibrated piezoelectric fiber squeezers as po... 详细信息

ISBN: (纸本)9781957171258

关键词： Photon polarization Polarization Polarization control Quantum communications Quantum key distribution Single photon detectors

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Efficient lightweight image compression approach for Multimedia Internet of Things

Efficient lightweight image compression approach for Multime...

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IEEE Middle East and North Africa Communications Conference (MENACOMM)

作者： Sara Kadhum Idrees Raphaël Couturier Joseph Azar Ali Kadhum Idrees Franck Gechter Université Marie et Louis Pasteur-UTBM-CIAD (UR 7533) Belfort France Dept. of Information Networks College of Information Technology University of Babylon Babylon Iraq CNRS Institut FEMTO-ST Université Marie et Louis Pasteur Belfort France Smart Edge Lab Faculty of Computer Science University of Applied Science and Arts Dortmund Germany SIMBIOT Team LORIA (UMR 7503) Vandoeuvre-lès-Nancy France

ISBN: (数字)9798331519957

ISBN: (纸本)9798331519964

The development of the Internet of Things (IoT) and communication technologies has led to the emergence of the multimedia IoT. The multimedia IoT contributes to a variety of multimedia applications, that can capture and transmit large amounts of images or video sequences over an IoT network. Due to restricted resources, multimedia IoT devices must transmit less multimedia data while retaining accuracy. This can save energy and memory use and increase multimedia IoT network performance. This paper proposes an efficient lightweight image compression approach (ELiCA) for Multimedia Internet of Things. The proposed ELiCA starts dividing the captured image into equal-sized blocks and the Discrete Cosine Transform (DCT) is applied to each of them to produce the DCT coefficients. Then, the DCT coefficients are quantized. The output of the quantization process is reordered using zigzag process. Finally, the output files of the zigzag are compressed using the error-bounded lossy compression technique known as SZ developed specifically for High-Performance Computing (HPC) systems. The compressed file will be transmitted by the MIoT device. The proposed ELiCA is evaluated using real sensed images and based on real IoT devices to show the efficiency of the proposed approach. The ELiCA presents better results compared with JPEG in terms of compression ratio, PSNR, and SSIM.

关键词： Performance evaluation Image coding Quantization (signal) High performance computing Video sequences Transform coding Streaming media Internet of Things Discrete cosine transforms Multimedia communication

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Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network

arXiv

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arXiv 2022年

作者： Wang, Xiang Yang, Yimin Pang, Qixiang Lu, Xiao Liu, Yu Du, Shan Dept. of Computer Science Lakehead University Thunder BayON Canada Dept. of Electrical and Computer Engineering Western University LondonON Canada Okanagan College KelownaBC Canada College of Engineering and Design Hunan Normal University Hunan China School of Microelectronics Tianjin University Tianjin China Dept. of Comp. Sci. Math Physics & Statistics The University of British Columbia OkanaganBC Canada

Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e.g., 4× and 8×). The proposed network is composed of a novel semantic encoder that has the ability to capture the embedded semantics to guide adversarial learning and a novel generator that uses a hierarchical architecture named Residual in Internal Dense Block (RIDB). Moreover, we propose a joint discriminator which discriminates both image data and embedded semantics. The joint discriminator learns the joint probability distribution of the image space and latent space. We also use a Relativistic average Least Squares loss (RaLS) as the adversarial loss to alleviate the gradient vanishing problem and enhance the stability of the training procedure. Extensive experiments on large face datasets have proved that the proposed method can achieve superior super-resolution results and significantly outperform other state-of-the-art methods in both qualitative and quantitative comparisons. © 2022, CC BY.

关键词： Semantics

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CapSight: A Social Aid System for the Visually Impaired Using Facial Recognition and Haptic Feedback

CapSight: A Social Aid System for the Visually Impaired Usin...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chun-Nan Chi Jia-Lien Hsu Chi-Huang Hung Shih-Yung Huang Cheng-Hsuan Chen Chia-Hsun Hsu Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan R.O.C. Department of Computer Science and Information Engineering Fu Jen Catholic University New Taipei City Taiwan R.O.C. Department of Electrical Engineering National Central University Fu Jen Catholic University Taoyuan City Taiwan R.O.C. Dept. of Information Technology Graduate Institute of Applied Science and Engineering Fu Jen Catholic University Lee-Ming Institute of Technology New Taipei City Taiwan R.O.C. Dept. of International High School Kang Chiao International School New Taipei City Taiwan R.O.C.

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

This study introduces "CapSight," a system for enhancing social interaction in visually impaired individuals. It includes a single-board computer, camera, vibration motors, and Bluetooth bone conduction headphones on a baseball cap. The camera identifies people using facial recognition and announces names through the headphones, with vibration motors indicating face positions. Users can add new friends by taking a photo and recording their name. This system helps visually impaired individuals recognize and locate social partners, boosting confidence and independence.

关键词： Vibrations Headphones Electric potential Face recognition Speech recognition Motors Cameras Recording Haptic interfaces Sports

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Multiangle QAOA Does Not Always Need All Its Angles

Multiangle QAOA Does Not Always Need All Its Angles

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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Kaiyan Shi Rebekah Herrman Ruslan Shaydulin Shouvanik Chakrabarti Marco Pistoia Jeffrey Larson Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA Dept. of Computer Science University of Maryland College Park MD USA Industrial and Systems Engineering University of Tennessee at Knoxville Knoxville TN USA Global Technology Applied Research JPMorgan Chase New York NY USA

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

Introducing additional tunable parameters to quantum circuits is a powerful way of improving per-formance without increasing hardware requirements. A recently introduced multiangle extension of the quantum approximate optimization algorithm (ma-QAOA) signifi-cantly improves the solution quality compared with QAOA by allowing the parameters for each term in the Hamilto-nian to vary independently. Prior results suggest, however, considerable redundancy in parameters, the removal of which would reduce the cost of parameter optimization. In this work we show numerically the connection between the problem symmetries and the parameter redundancy by demonstrating that symmetries can be used to reduce the number of parameters used by ma-QAOA without decreasing the solution quality. We study Max-Cut on all 7,565 connected, non-isomorphic 8-node graphs with a nontrivial symmetry group and show numerically that in 67.4% of these graphs, symmetry can be used to reduce the number of parameters with no decrease in the objective, with the average ratio of parameters reduced by 28.1%. Moreover, we show that in 35.9% of the graphs this reduction can be achieved by simply using the largest symmetry. For the graphs where reducing the number of parameters leads to a decrease in the objective, the largest symmetry can be used to reduce the parameter count by 37.1% at the cost of only a 6.1% decrease in the objective. We demonstrate the central role of symmetries by showing that a random parameter reduction strategy leads to much worse performance.

关键词： Costs Redundancy Approximation algorithms Hardware Quantum circuit Optimization Edge computing

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning

Exploiting Partial Observability and Optimized Simple State ...

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Frontiers of Information Technology (FIT)

作者： Danyaal Mahmood Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-dept. exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent’s performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent’s decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game.

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Solving Facility Location Problems for Disaster Response Using Simheuristics and Survival Analysis: A Hybrid Modeling Approach 22

Solving Facility Location Problems for Disaster Response Usi...

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Proceedings of the Winter Simulation Conference

作者： Bhakti Stephan Onggo Xabier Martin Javier Panadero Canan Gunes Corlu Angel A. Juan CORMSIS - Southampton Business School University of Southampton Southampton UK Department of Computer Science Universitat Oberta de Catalunya Barcelona SPAIN Metropolitan College Boston University Boston MA USA Dept. of Applied Statistics and Operations Research Universitat Politècnica de València Alcoy SPAIN

One of the important decisions for mitigating the risk from a sudden onset disaster is to determine the optimal location of relevant facilities (e.g., warehouses), because this affects the subsequent humanitarian operations. Researchers have proposed several methods to solve the facility location problem (FLP) in disaster management. This paper considers a stochastic FLP where the goal is to minimize the expected time required to provide service to all affected regions when travel times are stochastic due to uncertain road conditions. The number of facilities to open is constrained by a certain maximum budget. To solve this stochastic optimization problem, we propose a hybrid simulation optimization model that combines a simheuristic algorithm with a survival analysis method to evaluate the probability of meeting the demand of all affected areas within a time target. An experiment using a benchmark set shows our model outperforms deterministic solutions by about 8.9%.

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