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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Catherine, Holtz; Jason, Gilliland; Amardeep, Thind; Piotr, Wilk; +1 Authors

    Objective Primary health service use (P-HSU) may be influenced by contextual characteristics and is equitable when driven by need. Contextual effects and inequity of maternal P-HSU were determined. Methods Participant data from a London-Middlesex, Ontario, prenatal cohort were linked by residential address to contextual characteristics. Multilevel logistic regression estimated contextual effects and tested for effect measure modification of need factors. Results Maternal P-HSU varied between neighbourhoods. The effect of obesity was different for rural mothers living in low- (OR = 0.26) and middle-income households (OR = 0.15) and for urban mothers living in high-income households (OR = 2.82). The effect of having a health condition was greatest in mothers with three or more children (OR = 2.41). Discussion Differences in maternal P-HSU exist between neighbourhoods, and enabling factors modified need factors' effects, identifying subgroups of mothers with inequitable P-HSU. RESULTS have the potential to inform Canadian health policy with regard to contextual effects and inequity of P-HSU.

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    Scholarship@Western
    Other literature type . 2014
    World Health & Population
    Article . 2104 . Peer-reviewed
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      Other literature type . 2014
      World Health & Population
      Article . 2104 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Anshuman Kalla; Chamitha de Alwis; Sarada Prasad Gochhayat; Gürkan Gür; +2 Authors

    The next generation of mobile networks, i.e., sixth generation (6G), is expected by 2030, with already burgeoning research efforts towards this goal. Along with various other candidate technologies, blockchain is envisioned to enable and enhance numerous key functionalities of 6G. Thus, the main objective of this paper is threefold: 1) to categorize the different aspects of 6G into four emerging directions that anticipate significant advancements leveraging blockchain, 2) to discuss the potential role of blockchainized 6G under each key emerging direction, 3) to expound on the technical challenges in blockchaining 6G along with possible solutions. Checked 23/06/23 and citing details still not available MB To check citation details and date in 6 months

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    Other literature type . 2024
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    IEEE Consumer Electronics Magazine
    Article . 2024 . Peer-reviewed
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      IEEE Consumer Electronics Magazine
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bienvenido-Huertas, David; Moyano Campos, Juan José; E. Rodríguez-Jiménez, Carlos; Marín, David;

    Abstract: Most of the existing building stock has a deficient energy behaviour. The thermal transmittance of façades is among those aspects which most affect this situation. In this paper, the calculation procedure with correction for storage effects from ISO 9869-1 was applied to the thermometric method to determine the U-value. Due to the need for determining the number and type of layers that compose the wall to apply the calculation, a multilayer perceptron has been developed to estimate the U-value. From the different model configurations suggested, the most adequate architecture was the one with 14 nodes in the hidden layer without making transformations in the input variables. Valid results have been obtained by the multilayer perceptron for the case studies analysed from different building periods, with deviations lower than 20% between the measured value and the expected one, varying the test duration according to the thermal resistance of the wall and the temperature variations. Furthermore, it is not necessary to carry out a data post-processing for the model, so this fact simplifies and hastens the calculation procedure.Journal: Applied Energy.Volume: 233–234.Pages: 1-14.Received 10 June 2018, Revised 19 August 2018, Accepted 11 October 2018, Available online 16 October 2018, Version of Record 16 October 2018.

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  • Authors: Chen, Elizabeth; Leos, Cristina; Kowitt, Sarah D.; Moracco, Kathryn E.;

    Supplemental material, sj-pdf-1-hpp-10.1177_1524839919850557 for Enhancing Community-Based Participatory Research Through Human-Centered Design Strategies by Elizabeth Chen, Cristina Leos, Sarah D. Kowitt and Kathryn E. Moracco in Health Promotion Practice

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jiang, Yuang; Wang, Shiqiang; Valls, Victor; Ko, Bong Jun; +4 Authors

    Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision applications. A challenge is that client devices in FL usually have much more limited computation and communication resources compared to servers in a datacenter. To overcome this challenge, we propose PruneFL -- a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model. PruneFL includes initial pruning at a selected client and further pruning as part of the FL process. The model size is adapted during this process, which includes maximizing the approximate empirical risk reduction divided by the time of one FL round. Our experiments with various datasets on edge devices (e.g., Raspberry Pi) show that: (i) we significantly reduce the training time compared to conventional FL and various other pruning-based methods; (ii) the pruned model with automatically determined size converges to an accuracy that is very similar to the original model, and it is also a lottery ticket of the original model. Comment: Accepted for publication in IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    https://doi.org/10.48550/arxiv...
    Article . 2019
    License: arXiv Non-Exclusive Distribution
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    Access Routes
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      https://doi.org/10.48550/arxiv...
      Article . 2019
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dobkowska-Chudon, Wioletta; Wrobel, Miroslaw; Frankowska, Emilia; Zegadlo, Arkadiusz; +3 Authors

    The brain is subject to damage, due to ageing, physiological processes and/or disease. Some of the damage is acute in nature, such as strokes; some is more subtle, like white matter lesions. White matter lesions or hyperintensities (WMH) can be one of the first signs of micro brain damage. We implemented the Acoustocerebrography (ACG) as an easy to use method designed to capture differing states of human brain tissue and the respective changes. Aim: The purpose of the study is to compare the efficacy of ACG and Magnetic Resonance Imaging (MRI) to detect WMH in patients with clinically silent atrial fibrillation (AF). Methods and results: The study included 97 patients (age 66.26 ± 6.54 years) with AF. CHA2DS2-VASc score (2.5 ±1.3) and HAS BLED (1.65 ± 0.9). According to MRI data, the patients were assigned into four groups depending on the number of lesions: L0 - 0 to 4 lesions, L5 - 5 to 9 lesions, L10 - 10 to 29 lesions, and L30 - 30 or more lesions. Authors found that the ACG method clearly differentiates the groups L0 (with 0-4 lesions) and L30 (with more than 30 lesions) of WMH patients. Fisher's Exact Test shows that this correlation is highly significant (p < 0:001). Conclusion: ACG is a new, easy and cost-effective method for detecting WMH in patients with atrial fibrillation. The ACG measurement methodology should become increasingly useful for the assessment of WMH.

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    Archives of Acoustics
    Article . Other ORP type . 2023 . 2020 . Peer-reviewed
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      Archives of Acoustics
      Article . Other ORP type . 2023 . 2020 . Peer-reviewed
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    Authors: Shijie Li; Yazan AbuFarha; Yun Liu; Ming-Ming Cheng; +1 Authors

    With the success of deep learning in classifying short trimmed videos, more attention has been focused on temporally segmenting and classifying activities in long untrimmed videos. State-of-the-art approaches for action segmentation utilize several layers of temporal convolution and temporal pooling. Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors. In this paper, we propose a multi-stage architecture for the temporal action segmentation task that overcomes the limitations of the previous approaches. The first stage generates an initial prediction that is refined by the next ones. In each stage we stack several layers of dilated temporal convolutions covering a large receptive field with few parameters. While this architecture already performs well, lower layers still suffer from a small receptive field. To address this limitation, we propose a dual dilated layer that combines both large and small receptive fields. We further decouple the design of the first stage from the refining stages to address the different requirements of these stages. Extensive evaluation shows the effectiveness of the proposed model in capturing long-range dependencies and recognizing action segments. Our models achieve state-of-the-art results on three datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset. Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv admin note: substantial text overlap with arXiv:1903.01945

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    https://doi.org/10.48550/arxiv...
    Article . 2020
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      https://doi.org/10.48550/arxiv...
      Article . 2020
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Il Yong Chun; Zhengyu Huang; Hongki Lim; Jeffrey A. Fessler;

    Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimization models. This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum and majorizers with regression NNs. For fast MBIR, Momentum-Net uses momentum terms in extrapolation modules, and noniterative MBIR modules at each iteration by using majorizers, where each iteration of Momentum-Net consists of three core modules: image refining, extrapolation, and MBIR. Momentum-Net guarantees convergence to a fixed-point for general differentiable (non)convex MBIR functions (or data-fit terms) and convex feasible sets, under two asymptomatic conditions. To consider data-fit variations across training and testing samples, we also propose a regularization parameter selection scheme based on the "spectral spread" of majorization matrices. Numerical experiments for light-field photography using a focal stack and sparse-view computational tomography demonstrate that, given identical regression NN architectures, Momentum-Net significantly improves MBIR speed and accuracy over several existing INNs; it significantly improves reconstruction quality compared to a state-of-the-art MBIR method in each application. Comment: 28 pages, 13 figures, 3 algorithms, 4 tables, submitted revision to IEEE T-PAMI

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    Europe PubMed Central
    Other literature type . 2020
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    IEEE Transactions on Pattern Analysis and Machine Intelligence
    Article . 2023 . Peer-reviewed
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    Article . 2019
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      Europe PubMed Central
      Other literature type . 2020
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      IEEE Transactions on Pattern Analysis and Machine Intelligence
      Article . 2023 . Peer-reviewed
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      Article . 2019
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Enrique Naredo; Candelaria Sansores; Flaviano Godinez; Francisco López; +3 Authors

    Robotics technology has made significant advancements in various fields in industry and society. It is clear how robotics has transformed manufacturing processes and increased productivity. Additionally, navigation robotics has also been impacted by these advancements, with investors now investing in autonomous transportation for both public and private use. This research aims to explore how training scenarios affect the learning process for autonomous navigation tasks. The primary objective is to address whether the initial conditions (learning cases) have a positive or negative impact on the ability to develop general controllers. By examining this research question, the study seeks to provide insights into how to optimize the training process for autonomous navigation tasks, ultimately improving the quality of the controllers that are developed. Through this investigation, the study aims to contribute to the broader goal of advancing the field of autonomous navigation and developing more sophisticated and effective autonomous systems. Specifically, we conducted a comprehensive analysis of a particular navigation environment using evolutionary computing to develop controllers for a robot starting from different locations and aiming to reach a specific target. The final controller was then tested on a large number of unseen test cases. Experimental results provide strong evidence that the initial selection of the learning cases plays a role in evolving general controllers. This work includes a preliminary analysis of a specific set of small learning cases chosen manually, provides an in-depth analysis of learning cases in a particular navigation task, and develops a tool that shows the impact of the selected learning cases on the overall behavior of a robot’s controller.

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    DOAJ
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    https://doi.org/10.34961/resea...
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    Authors: Andrea Bourke; Daniela Oboroceanu; Nathan Quill; Catherine Lenihan; +10 Authors

    Two aspects of vanadium flow batteries are reviewed: electrochemical kinetics on carbon electrodes and positive electrolyte stability. There is poor agreement between reported values of kinetic parameters; however, most authors report that kinetic rates are faster for VIV/VVthan for VII/VIII. Cycling the electrode potential increases the rates of both reactions initially due to roughening but when no further roughening is observed, the VII/VIIIand VIV/VVreactions are affected oppositely by the pretreatment potential. Anodic pretreatment activates the electrode for the VII/VIIIreaction, and deactivates it for VIV/VV. Three states of the carbon surface are suggested: reduced and oxidized states R and O, respectively, both with low electrocatalytic activity, and an intermediate state M with higher activity. The role of surface functional groups and the mechanisms of electron transfer for the VII/VIIIand VIV/VVreactions are still not well understood. The induction time for precipitation of V2O5from positive electrolytes decreases with temperature, showing an Arrhenius-type dependence with an activation energy of 1.79 eV in agreement with DFT calculations based on a VO(OH)3intermediate. It also decreases exponentially with increasing VVconcentration and increases exponentially with increasing sulphate concentration. Both arsenate and phosphate are effective additives for improving thermal stability.

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    Journal of The Electrochemical Society
    Article . 2023 . Peer-reviewed
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    Arrow@TU Dublin
    Article . 2023
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    University of Limerick Research Repository
    Other literature type . Article . 2023
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      Journal of The Electrochemical Society
      Article . 2023 . Peer-reviewed
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      Arrow@TU Dublin
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Catherine, Holtz; Jason, Gilliland; Amardeep, Thind; Piotr, Wilk; +1 Authors

    Objective Primary health service use (P-HSU) may be influenced by contextual characteristics and is equitable when driven by need. Contextual effects and inequity of maternal P-HSU were determined. Methods Participant data from a London-Middlesex, Ontario, prenatal cohort were linked by residential address to contextual characteristics. Multilevel logistic regression estimated contextual effects and tested for effect measure modification of need factors. Results Maternal P-HSU varied between neighbourhoods. The effect of obesity was different for rural mothers living in low- (OR = 0.26) and middle-income households (OR = 0.15) and for urban mothers living in high-income households (OR = 2.82). The effect of having a health condition was greatest in mothers with three or more children (OR = 2.41). Discussion Differences in maternal P-HSU exist between neighbourhoods, and enabling factors modified need factors' effects, identifying subgroups of mothers with inequitable P-HSU. RESULTS have the potential to inform Canadian health policy with regard to contextual effects and inequity of P-HSU.

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    Scholarship@Western
    Other literature type . 2014
    World Health & Population
    Article . 2104 . Peer-reviewed
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      Other literature type . 2014
      World Health & Population
      Article . 2104 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Anshuman Kalla; Chamitha de Alwis; Sarada Prasad Gochhayat; Gürkan Gür; +2 Authors

    The next generation of mobile networks, i.e., sixth generation (6G), is expected by 2030, with already burgeoning research efforts towards this goal. Along with various other candidate technologies, blockchain is envisioned to enable and enhance numerous key functionalities of 6G. Thus, the main objective of this paper is threefold: 1) to categorize the different aspects of 6G into four emerging directions that anticipate significant advancements leveraging blockchain, 2) to discuss the potential role of blockchainized 6G under each key emerging direction, 3) to expound on the technical challenges in blockchaining 6G along with possible solutions. Checked 23/06/23 and citing details still not available MB To check citation details and date in 6 months

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    ZHAW digitalcollection
    Other literature type . 2024
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    IEEE Consumer Electronics Magazine
    Article . 2024 . Peer-reviewed
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      IEEE Consumer Electronics Magazine
      Article . 2024 . Peer-reviewed
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    Authors: Bienvenido-Huertas, David; Moyano Campos, Juan José; E. Rodríguez-Jiménez, Carlos; Marín, David;

    Abstract: Most of the existing building stock has a deficient energy behaviour. The thermal transmittance of façades is among those aspects which most affect this situation. In this paper, the calculation procedure with correction for storage effects from ISO 9869-1 was applied to the thermometric method to determine the U-value. Due to the need for determining the number and type of layers that compose the wall to apply the calculation, a multilayer perceptron has been developed to estimate the U-value. From the different model configurations suggested, the most adequate architecture was the one with 14 nodes in the hidden layer without making transformations in the input variables. Valid results have been obtained by the multilayer perceptron for the case studies analysed from different building periods, with deviations lower than 20% between the measured value and the expected one, varying the test duration according to the thermal resistance of the wall and the temperature variations. Furthermore, it is not necessary to carry out a data post-processing for the model, so this fact simplifies and hastens the calculation procedure.Journal: Applied Energy.Volume: 233–234.Pages: 1-14.Received 10 June 2018, Revised 19 August 2018, Accepted 11 October 2018, Available online 16 October 2018, Version of Record 16 October 2018.

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  • Authors: Chen, Elizabeth; Leos, Cristina; Kowitt, Sarah D.; Moracco, Kathryn E.;

    Supplemental material, sj-pdf-1-hpp-10.1177_1524839919850557 for Enhancing Community-Based Participatory Research Through Human-Centered Design Strategies by Elizabeth Chen, Cristina Leos, Sarah D. Kowitt and Kathryn E. Moracco in Health Promotion Practice

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    Authors: Jiang, Yuang; Wang, Shiqiang; Valls, Victor; Ko, Bong Jun; +4 Authors

    Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision applications. A challenge is that client devices in FL usually have much more limited computation and communication resources compared to servers in a datacenter. To overcome this challenge, we propose PruneFL -- a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model. PruneFL includes initial pruning at a selected client and further pruning as part of the FL process. The model size is adapted during this process, which includes maximizing the approximate empirical risk reduction divided by the time of one FL round. Our experiments with various datasets on edge devices (e.g., Raspberry Pi) show that: (i) we significantly reduce the training time compared to conventional FL and various other pruning-based methods; (ii) the pruned model with automatically determined size converges to an accuracy that is very similar to the original model, and it is also a lottery ticket of the original model. Comment: Accepted for publication in IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

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    https://doi.org/10.48550/arxiv...
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    Authors: Dobkowska-Chudon, Wioletta; Wrobel, Miroslaw; Frankowska, Emilia; Zegadlo, Arkadiusz; +3 Authors

    The brain is subject to damage, due to ageing, physiological processes and/or disease. Some of the damage is acute in nature, such as strokes; some is more subtle, like white matter lesions. White matter lesions or hyperintensities (WMH) can be one of the first signs of micro brain damage. We implemented the Acoustocerebrography (ACG) as an easy to use method designed to capture differing states of human brain tissue and the respective changes. Aim: The purpose of the study is to compare the efficacy of ACG and Magnetic Resonance Imaging (MRI) to detect WMH in patients with clinically silent atrial fibrillation (AF). Methods and results: The study included 97 patients (age 66.26 ± 6.54 years) with AF. CHA2DS2-VASc score (2.5 ±1.3) and HAS BLED (1.65 ± 0.9). According to MRI data, the patients were assigned into four groups depending on the number of lesions: L0 - 0 to 4 lesions, L5 - 5 to 9 lesions, L10 - 10 to 29 lesions, and L30 - 30 or more lesions. Authors found that the ACG method clearly differentiates the groups L0 (with 0-4 lesions) and L30 (with more than 30 lesions) of WMH patients. Fisher's Exact Test shows that this correlation is highly significant (p < 0:001). Conclusion: ACG is a new, easy and cost-effective method for detecting WMH in patients with atrial fibrillation. The ACG measurement methodology should become increasingly useful for the assessment of WMH.

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    Archives of Acoustics
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    Authors: Shijie Li; Yazan AbuFarha; Yun Liu; Ming-Ming Cheng; +1 Authors

    With the success of deep learning in classifying short trimmed videos, more attention has been focused on temporally segmenting and classifying activities in long untrimmed videos. State-of-the-art approaches for action segmentation utilize several layers of temporal convolution and temporal pooling. Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors. In this paper, we propose a multi-stage architecture for the temporal action segmentation task that overcomes the limitations of the previous approaches. The first stage generates an initial prediction that is refined by the next ones. In each stage we stack several layers of dilated temporal convolutions covering a large receptive field with few parameters. While this architecture already performs well, lower layers still suffer from a small receptive field. To address this limitation, we propose a dual dilated layer that combines both large and small receptive fields. We further decouple the design of the first stage from the refining stages to address the different requirements of these stages. Extensive evaluation shows the effectiveness of the proposed model in capturing long-range dependencies and recognizing action segments. Our models achieve state-of-the-art results on three datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset. Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv admin note: substantial text overlap with arXiv:1903.01945

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    https://doi.org/10.48550/arxiv...
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    Authors: Il Yong Chun; Zhengyu Huang; Hongki Lim; Jeffrey A. Fessler;

    Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in imaging, image processing, and computer vision. INNs combine regression NNs and an iterative model-based image reconstruction (MBIR) algorithm, often leading to both good generalization capability and outperforming reconstruction quality over existing MBIR optimization models. This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum and majorizers with regression NNs. For fast MBIR, Momentum-Net uses momentum terms in extrapolation modules, and noniterative MBIR modules at each iteration by using majorizers, where each iteration of Momentum-Net consists of three core modules: image refining, extrapolation, and MBIR. Momentum-Net guarantees convergence to a fixed-point for general differentiable (non)convex MBIR functions (or data-fit terms) and convex feasible sets, under two asymptomatic conditions. To consider data-fit variations across training and testing samples, we also propose a regularization parameter selection scheme based on the "spectral spread" of majorization matrices. Numerical experiments for light-field photography using a focal stack and sparse-view computational tomography demonstrate that, given identical regression NN architectures, Momentum-Net significantly improves MBIR speed and accuracy over several existing INNs; it significantly improves reconstruction quality compared to a state-of-the-art MBIR method in each application. Comment: 28 pages, 13 figures, 3 algorithms, 4 tables, submitted revision to IEEE T-PAMI

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    Europe PubMed Central
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    IEEE Transactions on Pattern Analysis and Machine Intelligence
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      Europe PubMed Central
      Other literature type . 2020
      Data sources: PubMed Central
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      IEEE Transactions on Pattern Analysis and Machine Intelligence
      Article . 2023 . Peer-reviewed
      License: IEEE Copyright
      Data sources: Crossref
      https://doi.org/10.48550/arxiv...
      Article . 2019
      License: arXiv Non-Exclusive Distribution
      Data sources: Datacite
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    Authors: Enrique Naredo; Candelaria Sansores; Flaviano Godinez; Francisco López; +3 Authors

    Robotics technology has made significant advancements in various fields in industry and society. It is clear how robotics has transformed manufacturing processes and increased productivity. Additionally, navigation robotics has also been impacted by these advancements, with investors now investing in autonomous transportation for both public and private use. This research aims to explore how training scenarios affect the learning process for autonomous navigation tasks. The primary objective is to address whether the initial conditions (learning cases) have a positive or negative impact on the ability to develop general controllers. By examining this research question, the study seeks to provide insights into how to optimize the training process for autonomous navigation tasks, ultimately improving the quality of the controllers that are developed. Through this investigation, the study aims to contribute to the broader goal of advancing the field of autonomous navigation and developing more sophisticated and effective autonomous systems. Specifically, we conducted a comprehensive analysis of a particular navigation environment using evolutionary computing to develop controllers for a robot starting from different locations and aiming to reach a specific target. The final controller was then tested on a large number of unseen test cases. Experimental results provide strong evidence that the initial selection of the learning cases plays a role in evolving general controllers. This work includes a preliminary analysis of a specific set of small learning cases chosen manually, provides an in-depth analysis of learning cases in a particular navigation task, and develops a tool that shows the impact of the selected learning cases on the overall behavior of a robot’s controller.

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    DOAJ
    Article . 2023
    Data sources: DOAJ
    https://doi.org/10.34961/resea...
    Other literature type . 2023
    License: CC BY NC SA
    Data sources: Datacite
    https://doi.org/10.34961/resea...
    Other literature type . 2023
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      DOAJ
      Article . 2023
      Data sources: DOAJ
      https://doi.org/10.34961/resea...
      Other literature type . 2023
      License: CC BY NC SA
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      https://doi.org/10.34961/resea...
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    Authors: Andrea Bourke; Daniela Oboroceanu; Nathan Quill; Catherine Lenihan; +10 Authors

    Two aspects of vanadium flow batteries are reviewed: electrochemical kinetics on carbon electrodes and positive electrolyte stability. There is poor agreement between reported values of kinetic parameters; however, most authors report that kinetic rates are faster for VIV/VVthan for VII/VIII. Cycling the electrode potential increases the rates of both reactions initially due to roughening but when no further roughening is observed, the VII/VIIIand VIV/VVreactions are affected oppositely by the pretreatment potential. Anodic pretreatment activates the electrode for the VII/VIIIreaction, and deactivates it for VIV/VV. Three states of the carbon surface are suggested: reduced and oxidized states R and O, respectively, both with low electrocatalytic activity, and an intermediate state M with higher activity. The role of surface functional groups and the mechanisms of electron transfer for the VII/VIIIand VIV/VVreactions are still not well understood. The induction time for precipitation of V2O5from positive electrolytes decreases with temperature, showing an Arrhenius-type dependence with an activation energy of 1.79 eV in agreement with DFT calculations based on a VO(OH)3intermediate. It also decreases exponentially with increasing VVconcentration and increases exponentially with increasing sulphate concentration. Both arsenate and phosphate are effective additives for improving thermal stability.

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    Journal of The Electrochemical Society
    Article . 2023 . Peer-reviewed
    License: CC BY NC ND
    Data sources: Crossref
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    Arrow@TU Dublin
    Article . 2023
    License: CC BY SA
    Data sources: Arrow@TU Dublin
    University of Limerick Research Repository
    Other literature type . Article . 2023
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    https://doi.org/10.34961/resea...
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      Journal of The Electrochemical Society
      Article . 2023 . Peer-reviewed
      License: CC BY NC ND
      Data sources: Crossref
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      Arrow@TU Dublin
      Article . 2023
      License: CC BY SA
      Data sources: Arrow@TU Dublin
      University of Limerick Research Repository
      Other literature type . Article . 2023
      License: CC BY NC SA
      https://doi.org/10.34961/resea...
      Other literature type . 2023
      License: CC BY NC SA
      Data sources: Datacite
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