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The document discusses the development of an innovation model for integrated decentralized food system governance at the district level in Cambodia. It includes pilot projects involving community fisheries, community fish refuges, farmer water user communities, community-based ecotourism, agriculture cooperatives, and private water supply. The models aim to enhance coordination, technical support, and capacity building among local communities and government agencies to improve water management and food system governance.

Robot audition systems with multiple microphone arrays have many applications in practice. However, accurate calibration of multiple microphone arrays remains challenging because there are many unknown parameters to be identified, including the relative transforms (i.e., orientation, translation) and asynchronous factors (i.e., initial time offset and sampling clock difference) between microphone arrays. To tackle these challenges, in this paper, we adopt batch simultaneous localization and mapping (SLAM) for joint calibration of multiple asynchronous microphone arrays and sound source localization. Using the Fisher information matrix (FIM) approach, we first conduct the observability analysis (i.e., parameter identifiability) of the above-mentioned calibration problem and establish necessary/sufficient conditions under which the FIM and the Jacobian matrix have full column rank, which implies the identifiability of the unknown parameters. We also discover several scenarios where the unknown parameters are not uniquely identifiable. Subsequently, we propose an effective framework to initialize the unknown parameters, which is used as the initial guess in batch SLAM for multiple microphone arrays calibration, aiming to further enhance optimization accuracy and convergence. Extensive numerical simulations and real experiments have been conducted to verify the performance of the proposed method. The experiment results show that the proposed pipeline achieves higher accuracy with fast convergence in comparison to methods that use the noise-corrupted ground truth of the unknown parameters as the initial guess in the optimization and other existing frameworks.

Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing methods are impractical in that they assume to know signal source positions or certain parameters (i.e., path loss exponent), and try to estimate the others. In this paper, we adopt graph simultaneous localization and mapping (SLAM) as a general framework for jointly estimating the source positions and parameters of the DRSS sensor array. Our contributions are twofold. On the one hand, by using a Fisher information matrix approach, we conduct a systematic observability analysis of the corresponding SLAM setup for the calibration problem. On the other hand, we propose an effective procedure to select the initial value which is fed to Levenberg-Marquardt iterations for further improving optimization accuracy and convergence. Extensive simulation and hardware experiments show that the proposed method renders high-quality calibration results. All the codes and data are publicly available at https://github.com/SUSTech2022/DRSS-sensor-array-calibration.

Multiple microphone arrays have many applications in robot audition including sound source localization audio scene perception and analysis etc However accurate calibration of multiple microphone arrays remains a challenge because there are many unknown parameters to be identified including the Euler angles geometry asynchronous factors between the microphone arrays This paper is concerned with joint calibration of multiple microphone arrays and sound source localization using graph simultaneous localization and mapping SLAM By using a Fisher information matrix FIM approach we focus on the observability analysis of the graph SLAM framework for the above mentioned calibration problem We thoroughly investigate the identifiability of the unknown parameters including the Euler angles geometry asynchronous effects between the microphone arrays and the sound source locations We establish necessary sufficient conditions under which the FIM and the Jacobian matrix have full column rank which implies the identifiability of the unknown parameters These conditions are closely related to the variation in the motion of the sound source and the configuration of microphone arrays and have intuitive and physical interpretations We also discover several scenarios where the unknown parameters are not uniquely identifiable All theoretical findings are demonstrated using simulation data

This work presents the application of a new tool, Obiwan, which uses image simulations to determine the selection function of a galaxy redshift survey and calculate three-dimensional (3D) clustering statistics. Obiwan relies on a forward model of the process by which images of the night sky are transformed into a 3D large-scale structure catalogue, and offers several advantages over more traditional map-based techniques - such as operating on individual exposures and adopting a maximum likelihood approach. The photometric pipeline automatically detects and models galaxies and then generates a catalogue of such galaxies with detailed information for each one of them, including their location, redshift, and so on. Systematic biases in the imaging data are therefore imparted into the catalogues and must be accounted for in any scientific analysis of their information content. Obiwan simulates this process for samples selected from the Legacy Surveys imaging data. This imaging data will be used to select target samples for the next-generation Dark Energy Spectroscopic Instrument (DESI) experiment. Here, we apply Obiwan to a portion of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxies (ELGs). Systematic biases in the data are clearly identified and removed. We compare the 3D clustering results to those obtained by the map-based approach applied to the complete eBOSS Data Release 16 (DR16) sample. We find the results are consistent, thereby validating the eBOSS DR16 ELG catalogues, which is used to obtain cosmological results.

Deep learning-based video segmentation methods can offer a good performance after being trained on the large-scale pixel labeled datasets. However, a pixel-wise manual labeling of animal images is challenging and time consuming due to irregular contours and motion blur. To achieve desirable tradeoffs between the accuracy and speed, a novel one-shot learning-based approach is proposed in this article to segment animal video with only one labeled frame. The proposed approach consists of the following three main modules: guidance frame selection utilizes 'BubbleNet' to choose one frame for manual labeling, which can leverage the fine-tuning effects of the only labeled frame; Xception-based fully convolutional network localizes dense prediction using depthwise separable convolutions based on one single labeled frame; and postprocessing is used to remove outliers and sharpen object contours, which consists of two submodules-test time augmentation and conditional random field. Extensive experiments have been conducted on the DAVIS 2016 animal dataset. Our proposed video segmentation approach achieved mean intersection-over-union score of 89.5% on the DAVIS 2016 animal dataset with less run time, and outperformed the state-of-art methods (OSVOS and OSMN). The proposed one-shot learning-based approach achieves real-time and automatic segmentation of animals with only one labeled video frame. This can be potentially used further as a baseline for intelligent perception-based monitoring of animals and other domain-specific applications1.

© 2020 Elsevier Ltd Ridesourcing services such as Uber, Lyft, and DiDi are purported to be more efficient than traditional taxis because they can match passengers with drivers more effectively. Previous studies have compared the efficiency of ridesourcing and taxis in several cities. However, gaps still exist regarding the measurement and comparison between the two modes, and the reasons for the higher efficiency of ridesourcing have not been empirically examined. This paper aims to measure, compare, and explain the efficiency and variation of DiDi and taxis. The case study is conducted in Chengdu, China. We use vehicle occupancy rate (VOR) as the efficiency measure - the percentage of time that a vehicle is occupied by a fare-paying passenger. We measure the VORs of DiDi and taxis and their spatial and temporal variations using the trip origin-destination data for DiDi and the trajectory data for taxis. The VOR patterns between DiDi and taxis are compared and contrasted, and the underlying factors that affect the difference are examined: more efficient driver-rider matching algorithm, larger scale of ridesourcing services, and the number of taxi trips per capita. Results show that the overall VOR of DiDi is six percentage points higher than taxis on the weekday and 12 percentage points higher on the weekend. However, the VOR of taxis is slightly higher than DiDi during the weekday morning peak hours and in downtown areas. Regression models reveal that the more efficient matching and the greater scale of DiDi drivers enlarge the VOR gap between DiDi and taxis, while the number of taxi trips per capita reduce the gap. The findings have implications for both business operation and transportation policies in terms of service design, service coordination, and location-specific regulations.