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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: Yaoqiang Pan; Kewei Hu; Hao Cao; Hanwen Kang; +1 Authors

    Agricultural robots must navigate challenging dynamic and semi-structured environments. Recently, environmental modeling using LiDAR-based SLAM has shown promise in providing highly accurate geometry. However, how this chaotic environmental information can be used to achieve effective robot automation in the agricultural sector remains unexplored. In this study, we propose a novel semantic mapping and navigation framework for achieving robotic autonomy in orchards. It consists of two main components: a semantic processing module and a navigation module. First, we present a novel 3D detection network architecture, 3D-ODN, which can accurately process object instance information from point clouds. Second, we develop a framework to construct the visibility map by incorporating semantic information and terrain analysis. By combining these two critical components, our framework is evaluated in a number of key horticultural production scenarios, including a robotic system for in-situ phenotyping and daily monitoring, and a selective harvesting system in apple orchards. The experimental results show that our method can ensure high accuracy in understanding the environment and enable reliable robot autonomy in agricultural environments.

    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/ arXiv.org e-Print Ar...arrow_drop_down
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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2023
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    Computers and Electronics in Agriculture
    Article . 2024 . Peer-reviewed
    License: Elsevier TDM
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    https://doi.org/10.48550/arxiv...
    Article . 2023
    License: CC BY
    Data sources: Datacite
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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/ arXiv.org e-Print Ar...arrow_drop_down
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      arXiv.org e-Print Archive
      Other literature type . Preprint . 2023
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      Computers and Electronics in Agriculture
      Article . 2024 . Peer-reviewed
      License: Elsevier TDM
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      https://doi.org/10.48550/arxiv...
      Article . 2023
      License: CC BY
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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: Nikolaos Stathoulopoulos; Anton Koval; George Nikolakopoulos;

    Localization algorithms that rely on 3D LiDAR scanners often encounter temporary failures due to various factors, such as sensor faults, dust particles, or drifting. These failures can result in a misalignment between the robot’s estimated pose and its actual position in the global map. To address this issue, the process of global re-localization becomes essential, as it involves accurately estimating the robot’s current pose within the given map. In this article, we propose a novel global re-localization framework that addresses the limitations of current algorithms heavily reliant on scan matching and direct point cloud feature extraction. Unlike most methods, our framework eliminates the need for an initial guess and provides multiple top-� candidates for selection, enhancing robustness and flexibility. Furthermore, we introduce an event-based re-localization trigger module, enabling autonomous robotic missions. Focusing on subterranean environments with low features, we leverage range image descriptors derived from 3D LiDAR scans to preserve depth information. Our approach enhances a state-of-the-art data-driven descriptor extraction framework for place recognition and orientation regression by incorporating a junction detection module that utilizes the descriptors for classification purposes. The effectiveness of the proposed approach was evaluated across three distinct real-life subterranean environments. Validerad;2023;Nivå 2;2023-09-22 (joosat);CC BY 4.0 License

    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/ arXiv.org e-Print Ar...arrow_drop_down
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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2022
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    https://doi.org/10.48550/arxiv...
    Article . 2022
    License: arXiv Non-Exclusive Distribution
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    Expert Systems with Applications
    Article . 2024 . Peer-reviewed
    License: CC BY
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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/ arXiv.org e-Print Ar...arrow_drop_down
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      arXiv.org e-Print Archive
      Other literature type . Preprint . 2022
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      https://doi.org/10.48550/arxiv...
      Article . 2022
      License: arXiv Non-Exclusive Distribution
      Data sources: Datacite; Sygma
      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/
      Expert Systems with Applications
      Article . 2024 . Peer-reviewed
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    Authors: Mohammad Safeea; Pedro Neto;

    This study evaluates the application of a discrete action space reinforcement learning method (Q-learning) to the continuous control problem of robot inverted pendulum balancing. To speed up the learning process and to overcome technical difficulties related to the direct learning on the real robotic system, the learning phase is performed in simulation environment. A mathematical model of the system dynamics is implemented, deduced by curve fitting on data acquired from the real system. The proposed approach demonstrated feasible, featuring its application on a real world robot that learned to balance an inverted pendulum. This study also reinforces and demonstrates the importance of an accurate representation of the physical world in simulation to achieve a more efficient implementation of reinforcement learning algorithms in real world, even when using a discrete action space algorithm to control a continuous action.

    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/ arXiv.org e-Print Ar...arrow_drop_down
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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2023
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    Intelligent Systems with Applications
    Article . 2024 . Peer-reviewed
    License: CC BY NC ND
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    https://doi.org/10.48550/arxiv...
    Article . 2023
    License: CC BY
    Data sources: Datacite
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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/ arXiv.org e-Print Ar...arrow_drop_down
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      arXiv.org e-Print Archive
      Other literature type . Preprint . 2023
      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/
      Intelligent Systems with Applications
      Article . 2024 . Peer-reviewed
      License: CC BY NC ND
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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/
      https://doi.org/10.48550/arxiv...
      Article . 2023
      License: CC BY
      Data sources: Datacite
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    Tangle is a concept in graph theory that has a dual relationship with tree-width which is well-known graph width parameter. Ultrafilter is a fundamental notion in mathematics. In this concise paper, we will reconsider the relationship between Tangle and Ultrafilter in digraph.

    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/ Journal of Advances ...arrow_drop_down
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    ResearchGate Data
    Preprint . 2023
    Data sources: Datacite
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    Preprint . 2023
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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/ Journal of Advances ...arrow_drop_down
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      Preprint . 2023
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      Preprint . 2023
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    Authors: MWANGI, MARY N; MAGEMBE, ERIC; SHARMA, KALPANA; CELLIER, GILLES; +3 Authors

    Bacterial wilt, caused by the Ralstonia solanacearum species complex (RSSC), is one of the most destructive diseases of potato in sub-tropical regions. This study reports the whole-genome shotgun sequences of eight RSSC strains, isolated from potato (Solanum tuberosum L.), Pelargonium, Capsicum annuum, Nicotiana tabacum, symptomatic for bacterial wilt in Sub-Saharan Africa. Sequencing was done on the Illumina NovaSeq 6000 and genomic sequences were deposited in NCBI GenBank under the BioProject PRJNA1070535. R. solanacearum strains were assembled into between 84 and 147 contigs with total genome sizes of between 5.23 Mb and 5.62 Mb in length and GC content between 66.49% and 67.08%. These data will provide a useful resource for future studies into RSSC and associated diseases of important crop plants. The sequencing of the bacterial strains was supported by GetGenome and The Sainsbury Laboratory, Norwich, UK, with contributions from the Gatsby Charitable Foundation, the Biotechnology, Biological Sciences Research Council (BBSRC) and The University of East Anglia.

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    ZENODO
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    Preprint . 2024
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      ZENODO
      Preprint . 2024
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    Authors: Matej Grcić; Petra Bevandić; Zoran Kalafatić; Siniša Šegvić;

    Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be only partially anomalous. Previous work has addressed dense out-of-distribution detection by discriminative training with respect to off-the-shelf negative datasets. However, real negative data may lead to over-optimistic evaluation due to possible overlap with test anomalies. To this end, we extend this approach by generating synthetic negative patches along the border of the inlier manifold. We leverage a jointly trained normalizing flow due to a coverage-oriented learning objective and the capability to generate samples at different resolutions. We detect anomalies according to a principled information-theoretic criterion which can be consistently applied through training and inference. The resulting models set the new state of the art on benchmarks for out-of-distribution detection in road-driving scenes and remote sensing imagery despite minimal computational overhead.

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    Sensors
    Article . 2024 . Peer-reviewed
    License: CC BY
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    Sensors
    Article . 2024
    Data sources: DOAJ
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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2021
    https://doi.org/10.48550/arxiv...
    Article . 2021
    License: CC BY NC SA
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      Sensors
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      arXiv.org e-Print Archive
      Other literature type . Preprint . 2021
      https://doi.org/10.48550/arxiv...
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    Authors: Shashi Raj Pandey; Pierre Pinson; Petar Popovski;

    This paper considers a market for trading Internet of Things (IoT) data that is used to train machine learning models. The data, either raw or processed, is supplied to the market platform through a network and the price of such data is controlled based on the value it brings to the machine learning model. We explore the correlation property of data in a game-theoretical setting to eventually derive a simplified distributed solution for a data trading mechanism that emphasizes the mutual benefit of devices and the market. The key proposal is an efficient algorithm for markets that jointly addresses the challenges of availability and heterogeneity in participation, as well as the transfer of trust and the economic value of data exchange in IoT networks. The proposed approach establishes the data market by reinforcing collaboration opportunities between device with correlated data to avoid information leakage. Therein, we develop a network-wide optimization problem that maximizes the social value of coalition among the IoT devices of similar data types; at the same time, it minimizes the cost due to network externalities, i.e., the impact of information leakage due to data correlation, as well as the opportunity costs. Finally, we reveal the structure of the formulated problem as a distributed coalition game and solve it following the simplified split-and-merge algorithm. Simulation results show the efficacy of our proposed mechanism design toward a trusted IoT data market, with up to 32.72% gain in the average payoff for each seller. Comment: 15 pages. 12 figures. This paper has been accepted for publication in IEEE Internet of Things Journal. Copyright may change without notice

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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2022
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    https://doi.org/10.1109/jiot.2...
    Article . 2024 . Peer-reviewed
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      https://doi.org/10.1109/jiot.2...
      Article . 2024 . Peer-reviewed
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    Authors: Fortin, Mathieu; Van Lier, Olivier; Jean-François Côté; Erdle, Heidi; +1 Authors

    Abstract Combining forest growth models with remotely sensed data is possible under a generalized hierarchical model-based (GHMB) inferential framework. This implies the existence of two submodels: the growth model itself ($\mathcal{M}_{1}$) and a second submodel that links the growth predictions to some remotely sensed variables ($\mathcal{M}_{2}$). Analytical GHMB estimators are available to fit submodel $\mathcal{M}_{2}$ and account for the uncertainty stemming from submodel $\mathcal{M}_{1}$, i.e. the growth model. However, when the growth model is individual based, it is usually too complex to be differentiated with respect to its parameters. As a result, the analytical GHMB estimators cannot be used. In this study, we developed a bootstrap approach for the GHMB inferential framework in order to combine individual-based forest growth models with remotely sensed data. Through simulation studies, we showed that the bootstrap estimators were nearly unbiased when both submodels were linear. The estimator of the parameter estimates remained nearly unbiased when submodel $\mathcal{M}_{1}$ became complex, i.e. non-differentiable, and submodel $\mathcal{M}_{2}$ was nonlinear with heterogeneous variances and correlated error terms. The variance estimator showed some biases but these were relatively small. We further demonstrated through a real-world case study that the predictions of a complex individual-based model could be linked to a Landsat-8 near-infrared spectral band in the boreal forest zone of Quebec, Canada.

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    Forestry An International Journal of Forest Research
    Article . 2024 . Peer-reviewed
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    Preprint . 2023
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      Forestry An International Journal of Forest Research
      Article . 2024 . Peer-reviewed
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    Authors: Long Bao; Xuemei Zhao; Gaowa Kang; Kaito Suzuki; +3 Authors

    Platycodon is a medicinal plant of considerable pharmacological and dietary value. With the growing demand, agricultural production is increasing. However, the continuous cropping significantly causes negative impacts on its yield and quality. In this study, in order to solve the problem of continuous cropping, we evaluated the allelopathic activity of Platycodon and investigated the potential use of activated carbon for mitigating the negative impacts of allelopathic chemicals produced by Platycodon. The sandwich method (method for assaying the allelopathic activity of each part of a plant) was employed to evaluate the allelopathic activity of different parts (leaves, stems, and roots) of Platycodon. The inhibitory effects of various Platycodon plant parts were assessed based on their effects on lettuce seedling growth. At a concentration of 10 mg parts/10 mL agar, the average inhibition rates of Platycodon leaves on the radicle and hypocotyl growth of lettuce were 79.4% and 61.8%, stems 58.0% and 45.7%, and roots 53.4% and 49.3%, respectively. At a concentration of 50 mg parts/10 mL agar, the inhibitory effects were as follows: leaves (91.9%, 72.2%), stems (79.5%, 60.3%), and roots (71.4%, 65.2%). The effect of activated carbon on the adsorption of allelopathic substances was investigated, and the results of the sandwich method with a concentration of 10 mg parts/10 mL agar showed the following growth-inhibitory effects on lettuce seedlings and hypocotyls—roots (27.8%, 25.7%), leaves (13.3%, 25.7%), and stems (9.1%, 13.6%)—in each case showing a significant decrease in the inhibitory activity. The plant box method (method for assaying the allelopathic activity of plant root exudates) was employed to evaluate the activity of Platycodon root exudate. The growth inhibition rates of lettuce radicle and hypocotyls were 45.5% and 18.9%, respectively. The plant box method with addition of activated carbon revealed average rates of promotion of 16.7% and 4.7% on the growth of lettuce seedlings and hypocotyls, respectively. The results of this study demonstrated that activated carbon has a mitigating effect on allelopathic inhibition associated with the different plant parts and root exudation of Platycodon and provide a potential solution for overcoming problems associated with the continuous cropping of Platycodon.

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    Agronomy
    Article . 2024
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    https://doi.org/10.20944/prepr...
    Preprint . 2024
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    Authors: Alexander E. Thelen; Katherine de Kleer; Maria Camarca; Alex Akins; +3 Authors

    We present best-fit values of porosity -- and the corresponding effective thermal inertiae -- determined from three different depths in Europa's near-subsurface (~1-20 cm). The porosity of the upper ~20 cm of Europa's subsurface varies between 75-50% ($\Gamma_{eff}\approx50-140$ J m$^{-2}$ K$^{-1}$ s$^{-1/2}$) on the leading hemisphere and 50-40% ($\Gamma_{eff}\approx140-180$ J m$^{-2}$ K$^{-1}$ s$^{-1/2}$) on the trailing hemisphere. Residual maps produced by comparison with these models reveal thermally anomalous features that cannot be reproduced by globally homogeneous porosity models. These regions are compared to Europa's surface terrain and known compositional variations. We find that some instances of warm thermal anomalies are co-located with known geographical or compositional features on both the leading and trailing hemisphere; cool temperature anomalies are well correlated with surfaces previously observed to contain pure, crystalline water ice and the expansive rays of Pwyll crater. Anomalous regions correspond to locations with subsurface properties different from those of our best-fit models, such as potentially elevated thermal inertia, decreased emissivity, or more porous regolith. We also find that ALMA observations at ~3 mm sound below the thermal skin depth of Europa (~10-15 cm) for a range of porosity values, and thus do not exhibit features indicative of diurnal variability or residuals similar to other frequency bands. Future observations of Europa at higher angular resolution may reveal additional locations of variable subsurface thermophysical properties, while those at other wavelengths will inform our understanding of the regolith compaction length and the effects of external processes on the shallow subsurface. Comment: Accepted for publication in the Planetary Science Journal on 01/31/2024. 34 pages, 10 Figures, 4 tables

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    The Planetary Science Journal
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    The Planetary Science Journal
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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: Yaoqiang Pan; Kewei Hu; Hao Cao; Hanwen Kang; +1 Authors

    Agricultural robots must navigate challenging dynamic and semi-structured environments. Recently, environmental modeling using LiDAR-based SLAM has shown promise in providing highly accurate geometry. However, how this chaotic environmental information can be used to achieve effective robot automation in the agricultural sector remains unexplored. In this study, we propose a novel semantic mapping and navigation framework for achieving robotic autonomy in orchards. It consists of two main components: a semantic processing module and a navigation module. First, we present a novel 3D detection network architecture, 3D-ODN, which can accurately process object instance information from point clouds. Second, we develop a framework to construct the visibility map by incorporating semantic information and terrain analysis. By combining these two critical components, our framework is evaluated in a number of key horticultural production scenarios, including a robotic system for in-situ phenotyping and daily monitoring, and a selective harvesting system in apple orchards. The experimental results show that our method can ensure high accuracy in understanding the environment and enable reliable robot autonomy in agricultural environments.

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    Computers and Electronics in Agriculture
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      Computers and Electronics in Agriculture
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    Authors: Nikolaos Stathoulopoulos; Anton Koval; George Nikolakopoulos;

    Localization algorithms that rely on 3D LiDAR scanners often encounter temporary failures due to various factors, such as sensor faults, dust particles, or drifting. These failures can result in a misalignment between the robot’s estimated pose and its actual position in the global map. To address this issue, the process of global re-localization becomes essential, as it involves accurately estimating the robot’s current pose within the given map. In this article, we propose a novel global re-localization framework that addresses the limitations of current algorithms heavily reliant on scan matching and direct point cloud feature extraction. Unlike most methods, our framework eliminates the need for an initial guess and provides multiple top-� candidates for selection, enhancing robustness and flexibility. Furthermore, we introduce an event-based re-localization trigger module, enabling autonomous robotic missions. Focusing on subterranean environments with low features, we leverage range image descriptors derived from 3D LiDAR scans to preserve depth information. Our approach enhances a state-of-the-art data-driven descriptor extraction framework for place recognition and orientation regression by incorporating a junction detection module that utilizes the descriptors for classification purposes. The effectiveness of the proposed approach was evaluated across three distinct real-life subterranean environments. Validerad;2023;Nivå 2;2023-09-22 (joosat);CC BY 4.0 License

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    https://doi.org/10.48550/arxiv...
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    Expert Systems with Applications
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      https://doi.org/10.48550/arxiv...
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      Expert Systems with Applications
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    Authors: Mohammad Safeea; Pedro Neto;

    This study evaluates the application of a discrete action space reinforcement learning method (Q-learning) to the continuous control problem of robot inverted pendulum balancing. To speed up the learning process and to overcome technical difficulties related to the direct learning on the real robotic system, the learning phase is performed in simulation environment. A mathematical model of the system dynamics is implemented, deduced by curve fitting on data acquired from the real system. The proposed approach demonstrated feasible, featuring its application on a real world robot that learned to balance an inverted pendulum. This study also reinforces and demonstrates the importance of an accurate representation of the physical world in simulation to achieve a more efficient implementation of reinforcement learning algorithms in real world, even when using a discrete action space algorithm to control a continuous action.

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    Intelligent Systems with Applications
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    https://doi.org/10.48550/arxiv...
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      Intelligent Systems with Applications
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      https://doi.org/10.48550/arxiv...
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    Tangle is a concept in graph theory that has a dual relationship with tree-width which is well-known graph width parameter. Ultrafilter is a fundamental notion in mathematics. In this concise paper, we will reconsider the relationship between Tangle and Ultrafilter in digraph.

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    Authors: MWANGI, MARY N; MAGEMBE, ERIC; SHARMA, KALPANA; CELLIER, GILLES; +3 Authors

    Bacterial wilt, caused by the Ralstonia solanacearum species complex (RSSC), is one of the most destructive diseases of potato in sub-tropical regions. This study reports the whole-genome shotgun sequences of eight RSSC strains, isolated from potato (Solanum tuberosum L.), Pelargonium, Capsicum annuum, Nicotiana tabacum, symptomatic for bacterial wilt in Sub-Saharan Africa. Sequencing was done on the Illumina NovaSeq 6000 and genomic sequences were deposited in NCBI GenBank under the BioProject PRJNA1070535. R. solanacearum strains were assembled into between 84 and 147 contigs with total genome sizes of between 5.23 Mb and 5.62 Mb in length and GC content between 66.49% and 67.08%. These data will provide a useful resource for future studies into RSSC and associated diseases of important crop plants. The sequencing of the bacterial strains was supported by GetGenome and The Sainsbury Laboratory, Norwich, UK, with contributions from the Gatsby Charitable Foundation, the Biotechnology, Biological Sciences Research Council (BBSRC) and The University of East Anglia.

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    Authors: Matej Grcić; Petra Bevandić; Zoran Kalafatić; Siniša Šegvić;

    Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be only partially anomalous. Previous work has addressed dense out-of-distribution detection by discriminative training with respect to off-the-shelf negative datasets. However, real negative data may lead to over-optimistic evaluation due to possible overlap with test anomalies. To this end, we extend this approach by generating synthetic negative patches along the border of the inlier manifold. We leverage a jointly trained normalizing flow due to a coverage-oriented learning objective and the capability to generate samples at different resolutions. We detect anomalies according to a principled information-theoretic criterion which can be consistently applied through training and inference. The resulting models set the new state of the art on benchmarks for out-of-distribution detection in road-driving scenes and remote sensing imagery despite minimal computational overhead.

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    Sensors
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    https://doi.org/10.48550/arxiv...
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      https://doi.org/10.48550/arxiv...
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    Authors: Shashi Raj Pandey; Pierre Pinson; Petar Popovski;

    This paper considers a market for trading Internet of Things (IoT) data that is used to train machine learning models. The data, either raw or processed, is supplied to the market platform through a network and the price of such data is controlled based on the value it brings to the machine learning model. We explore the correlation property of data in a game-theoretical setting to eventually derive a simplified distributed solution for a data trading mechanism that emphasizes the mutual benefit of devices and the market. The key proposal is an efficient algorithm for markets that jointly addresses the challenges of availability and heterogeneity in participation, as well as the transfer of trust and the economic value of data exchange in IoT networks. The proposed approach establishes the data market by reinforcing collaboration opportunities between device with correlated data to avoid information leakage. Therein, we develop a network-wide optimization problem that maximizes the social value of coalition among the IoT devices of similar data types; at the same time, it minimizes the cost due to network externalities, i.e., the impact of information leakage due to data correlation, as well as the opportunity costs. Finally, we reveal the structure of the formulated problem as a distributed coalition game and solve it following the simplified split-and-merge algorithm. Simulation results show the efficacy of our proposed mechanism design toward a trusted IoT data market, with up to 32.72% gain in the average payoff for each seller. Comment: 15 pages. 12 figures. This paper has been accepted for publication in IEEE Internet of Things Journal. Copyright may change without notice

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    arXiv.org e-Print Archive
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    https://doi.org/10.1109/jiot.2...
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      https://doi.org/10.1109/jiot.2...
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    Authors: Fortin, Mathieu; Van Lier, Olivier; Jean-François Côté; Erdle, Heidi; +1 Authors

    Abstract Combining forest growth models with remotely sensed data is possible under a generalized hierarchical model-based (GHMB) inferential framework. This implies the existence of two submodels: the growth model itself ($\mathcal{M}_{1}$) and a second submodel that links the growth predictions to some remotely sensed variables ($\mathcal{M}_{2}$). Analytical GHMB estimators are available to fit submodel $\mathcal{M}_{2}$ and account for the uncertainty stemming from submodel $\mathcal{M}_{1}$, i.e. the growth model. However, when the growth model is individual based, it is usually too complex to be differentiated with respect to its parameters. As a result, the analytical GHMB estimators cannot be used. In this study, we developed a bootstrap approach for the GHMB inferential framework in order to combine individual-based forest growth models with remotely sensed data. Through simulation studies, we showed that the bootstrap estimators were nearly unbiased when both submodels were linear. The estimator of the parameter estimates remained nearly unbiased when submodel $\mathcal{M}_{1}$ became complex, i.e. non-differentiable, and submodel $\mathcal{M}_{2}$ was nonlinear with heterogeneous variances and correlated error terms. The variance estimator showed some biases but these were relatively small. We further demonstrated through a real-world case study that the predictions of a complex individual-based model could be linked to a Landsat-8 near-infrared spectral band in the boreal forest zone of Quebec, Canada.

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    Forestry An International Journal of Forest Research
    Article . 2024 . Peer-reviewed
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      Forestry An International Journal of Forest Research
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    Authors: Long Bao; Xuemei Zhao; Gaowa Kang; Kaito Suzuki; +3 Authors

    Platycodon is a medicinal plant of considerable pharmacological and dietary value. With the growing demand, agricultural production is increasing. However, the continuous cropping significantly causes negative impacts on its yield and quality. In this study, in order to solve the problem of continuous cropping, we evaluated the allelopathic activity of Platycodon and investigated the potential use of activated carbon for mitigating the negative impacts of allelopathic chemicals produced by Platycodon. The sandwich method (method for assaying the allelopathic activity of each part of a plant) was employed to evaluate the allelopathic activity of different parts (leaves, stems, and roots) of Platycodon. The inhibitory effects of various Platycodon plant parts were assessed based on their effects on lettuce seedling growth. At a concentration of 10 mg parts/10 mL agar, the average inhibition rates of Platycodon leaves on the radicle and hypocotyl growth of lettuce were 79.4% and 61.8%, stems 58.0% and 45.7%, and roots 53.4% and 49.3%, respectively. At a concentration of 50 mg parts/10 mL agar, the inhibitory effects were as follows: leaves (91.9%, 72.2%), stems (79.5%, 60.3%), and roots (71.4%, 65.2%). The effect of activated carbon on the adsorption of allelopathic substances was investigated, and the results of the sandwich method with a concentration of 10 mg parts/10 mL agar showed the following growth-inhibitory effects on lettuce seedlings and hypocotyls—roots (27.8%, 25.7%), leaves (13.3%, 25.7%), and stems (9.1%, 13.6%)—in each case showing a significant decrease in the inhibitory activity. The plant box method (method for assaying the allelopathic activity of plant root exudates) was employed to evaluate the activity of Platycodon root exudate. The growth inhibition rates of lettuce radicle and hypocotyls were 45.5% and 18.9%, respectively. The plant box method with addition of activated carbon revealed average rates of promotion of 16.7% and 4.7% on the growth of lettuce seedlings and hypocotyls, respectively. The results of this study demonstrated that activated carbon has a mitigating effect on allelopathic inhibition associated with the different plant parts and root exudation of Platycodon and provide a potential solution for overcoming problems associated with the continuous cropping of Platycodon.

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    https://doi.org/10.20944/prepr...
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    Authors: Alexander E. Thelen; Katherine de Kleer; Maria Camarca; Alex Akins; +3 Authors

    We present best-fit values of porosity -- and the corresponding effective thermal inertiae -- determined from three different depths in Europa's near-subsurface (~1-20 cm). The porosity of the upper ~20 cm of Europa's subsurface varies between 75-50% ($\Gamma_{eff}\approx50-140$ J m$^{-2}$ K$^{-1}$ s$^{-1/2}$) on the leading hemisphere and 50-40% ($\Gamma_{eff}\approx140-180$ J m$^{-2}$ K$^{-1}$ s$^{-1/2}$) on the trailing hemisphere. Residual maps produced by comparison with these models reveal thermally anomalous features that cannot be reproduced by globally homogeneous porosity models. These regions are compared to Europa's surface terrain and known compositional variations. We find that some instances of warm thermal anomalies are co-located with known geographical or compositional features on both the leading and trailing hemisphere; cool temperature anomalies are well correlated with surfaces previously observed to contain pure, crystalline water ice and the expansive rays of Pwyll crater. Anomalous regions correspond to locations with subsurface properties different from those of our best-fit models, such as potentially elevated thermal inertia, decreased emissivity, or more porous regolith. We also find that ALMA observations at ~3 mm sound below the thermal skin depth of Europa (~10-15 cm) for a range of porosity values, and thus do not exhibit features indicative of diurnal variability or residuals similar to other frequency bands. Future observations of Europa at higher angular resolution may reveal additional locations of variable subsurface thermophysical properties, while those at other wavelengths will inform our understanding of the regolith compaction length and the effects of external processes on the shallow subsurface. Comment: Accepted for publication in the Planetary Science Journal on 01/31/2024. 34 pages, 10 Figures, 4 tables

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    The Planetary Science Journal
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