Organic fertilization improves soil aggregation through increases in abundance of eubacteria and products of arbuscular mycorrhizal fungi.

An important goal of sustainable agriculture is to maintain soil quality. Soil aggregation, which can serve as a measure of soil quality, plays an important role in maintaining soil structure, fertility, and stability. The process of soil aggregation can be affected through impacts on biotic and abiotic factors. Here, we tested whether soil management involving application of organic and mineral fertilizers could significantly improve soil aggregation and if variation among differently fertilized soils could be specifically attributed to a particular biotic and/or abiotic soil parameter. In a field experiment within Central Europe, we assessed stability of 1-2 mm soil aggregates together with other parameters of soil samples from differently fertilized soils. Application of compost and digestates increased stability of soil aggregates. Most of the variation in soil aggregation caused by different fertilizers was associated with soil organic carbon lability, occurrence of aromatic functional groups, and variations in abundance of eubacteria, total glomalins, concentrations of total S, N, C, and hot water extractable C. In summary, we have shown that application of compost and digestates improves stability of soil aggregates and that this is accompanied by increased soil fertility, decomposition resistance, and abundance of total glomalins and eubacteria. These probably play significant roles in increasing stability of soil aggregates.

OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields.

Realtime multi-person 2D pose estimation is a key component in enabling machines to have an understanding of people in images and videos. In this work, we present a realtime approach to detect the 2D pose of multiple people in an image. The proposed method uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. This bottom-up system achieves high accuracy and realtime performance, regardless of the number of people in the image. In previous work, PAFs and body part location estimation were refined simultaneously across training stages. We demonstrate that a PAF-only refinement rather than both PAF and body part location refinement results in a substantial increase in both runtime performance and accuracy. We also present the first combined body and foot keypoint detector, based on an internal annotated foot dataset that we have publicly released. We show that the combined detector not only reduces the inference time compared to running them sequentially, but also maintains the accuracy of each component individually. This work has culminated in the release of OpenPose, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints.

Panoptic Studio: A Massively Multiview System for Social Interaction Capture.

We present an approach to capture the 3D motion of a group of people engaged in a social interaction. The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions. The Panoptic Studio is a system organized around the thesis that social interactions should be measured through the integration of perceptual analyses over a large variety of view points. We present a modularized system designed around this principle, consisting of integrated structural, hardware, and software innovations. The system takes, as input, 480 synchronized video streams of multiple people engaged in social activities, and produces, as output, the labeled time-varying 3D structure of anatomical landmarks on individuals in the space. Our algorithm is designed to fuse the "weak" perceptual processes in the large number of views by progressively generating skeletal proposals from low-level appearance cues, and a framework for temporal refinement is also presented by associating body parts to reconstructed dense 3D trajectory stream. Our system and method are the first in reconstructing full body motion of more than five people engaged in social interactions without using markers. We also empirically demonstrate the impact of the number of views in achieving this goal.

Non-enhanced phytoextraction of cadmium, zinc, and lead by high-yielding crops.

Heavy metal soil contamination from mining and smelting has been reported in several regions around the world, and phytoextraction, using plants to accumulate risk elements in aboveground harvestable organs, is a useful method of substantially reducing this contamination. In our 3-year experiment, we tested the hypothesis that phytoextraction can be successful in local soil conditions without external fertilizer input. The phytoextraction efficiency of 15 high-yielding crop species was assessed in a field experiment performed at the Litavka River alluvium in the Príbram region of Czechia. This area is heavily polluted by Cd, Zn, and Pb from smelter installations which also polluted the river water and flood sediments. Heavy metal concentrations were analyzed in the herbaceous plants' aboveground and belowground biomass and in woody plants' leaves and branches. The highest Cd and Zn mean concentrations in the aboveground biomass were recorded in Salix x fragilis L. (10.14 and 343 mg kg-1 in twigs and 16.74 and 1188 mg kg-1 in leaves, respectively). The heavy metal content in woody plants was significantly higher in leaves than in twigs. In addition, Malva verticillata L. had the highest Cd, Pb, and Zn concentrations in herbaceous species (6.26, 12.44, and 207 mg kg-1, respectively). The calculated heavy metal removal capacities in this study proved high phytoextraction efficiency in woody species; especially for Salix × fragilis L. In other tested plants, Sorghum bicolor L., Helianthus tuberosus L., Miscanthus sinensis Andersson, and Phalaris arundinacea L. species are also recommended for phytoextraction.

Kronecker-Markov Prior for Dynamic 3D Reconstruction.

Recovering dynamic 3D structures from 2D image observations is highly under-constrained because of projection and missing data, motivating the use of strong priors to constrain shape deformation. In this paper, we empirically show that the spatiotemporal covariance of natural deformations is dominated by a Kronecker pattern. We demonstrate that this pattern arises as the limit of a spatiotemporal autoregressive process, and derive a Kronecker Markov Random Field as a prior distribution over dynamic structures. This distribution unifies shape and trajectory models of prior art and has the individual models as its marginals. The key assumption of the Kronecker MRF is that the spatiotemporal covariance is separable into the product of a temporal and a shape covariance, and can therefore be modeled using the matrix normal distribution. Analysis on motion capture data validates that this distribution is an accurate approximation with significantly fewer free parameters. Using the trace-norm, we present a convex method to estimate missing data from a single sequence when the marginal shape distribution is unknown. The Kronecker-Markov distribution, fit to a single sequence, outperforms state-of-the-art methods at inferring missing 3D data, and additionally provides covariance estimates of the uncertainty.