Predicting food production potential of urban vacant lots through soil quality

Post-industrial cities such as Cleveland have accumulated substantial number of vacant lots due to home foreclosures and urban sprawl over the past two decades. Interest in this land has escalated recently due to increased demand for food security in disadvantaged urban neighborhoods. We measured soil physical, chemical, and biological parameters in vacant lots in the Hough neighborhood in Cleveland to assess their suitability for food production. Each lot was divided into three approximately equal sections and nine soil cores were collected from each section. The results revealed huge spatial variability in soil properties within vacant lots. Soil pH ranged from 6.24-7.46 and moisture from 1.5-20.5%. Soil clay content ranged from 4-33%, sand 40-92%, and silt 0-50%. Soil NH4-N ranged from 1.7-21.0 ppm, NO3-N from 2.3-35.3 ppm, microbial biomass from 40.2-245.7 ppm (N), soil organic matter from 2.0-7.0%, and soil active carbon from 413.3-694.8 mg/kg. Thirty-four nematode genera were identified, and nematode abundance ranged from 34 to 988 per sample. Soil active carbon, a rapid soil quality indicator, significantly correlated with other measures of ecosystem condition including NH4-N, microbial biomass, soil organic matter, nematode abundance, maturity index, and combined maturity index. Principle Component Analysis revealed that vacant lots had less structured soil food webs than turfgrass lawns, but not from community gardens and vegetable farms. There were also no differences in nematode abundance, genus diversity, and enrichment index among vacant lots, turfgrass lawns, community gardens and vegetable farms. Our results indicate high potential for food production in urban vacant lots.Urban Landscape Ecology Program, The Ohio State UniversityCenter for Urban Environment and Economic Development, The Ohio State UniversityOARDC Research Internships Program, The Ohio State Universit

Adaptive Multi-grained Graph Neural Networks

Graph Neural Networks (GNNs) have been increasingly deployed in a multitude of different applications that involve node-wise and graph-level tasks. The existing literature usually studies these questions independently while they are inherently correlated. We propose in this work a unified model, Adaptive Multi-grained GNN (AdamGNN), to learn node and graph level representation interactively. Compared with the existing GNN models and pooling methods, AdamGNN enhances node representation with multi-grained semantics and avoids node feature and graph structure information loss during pooling. More specifically, a differentiable pooling operator in AdamGNN is used to obtain a multi-grained structure that involves node-wise and meso/macro level semantic information. The unpooling and flyback aggregators in AdamGNN is to leverage the multi-grained semantics to enhance node representation. The updated node representation can further enrich the generated graph representation in the next iteration. Experimental results on twelve real-world graphs demonstrate the effectiveness of AdamGNN on multiple tasks, compared with several competing methods. In addition, the ablation and empirical studies confirm the effectiveness of different components in AdamGNN

Hierarchical Message-Passing Graph Neural Networks

Graph Neural Networks (GNNs) have become a promising approach to machine learning with graphs. Since existing GNN models are based on flat message-passing mechanisms, two limitations need to be tackled. One is costly in encoding global information on the graph topology. The other is failing to model meso- and macro-level semantics hidden in the graph, such as the knowledge of institutes and research areas in an academic collaboration network. To deal with these two issues, we propose a novel Hierarchical Message-Passing Graph Neural Networks framework. The main idea is to generate a hierarchical structure that re-organises all nodes in a graph into multi-level clusters, along with intra- and inter-level edge connections. The derived hierarchy not only creates shortcuts connecting far-away nodes so that global information can be efficiently accessed via message passing but also incorporates meso- and macro-level semantics into the learning of node embedding. We present the first model to implement this hierarchical message-passing mechanism, termed Hierarchical Community-aware Graph Neural Network (HC-GNN), based on hierarchical communities detected from the graph. Experiments conducted on eight datasets under transductive, inductive, and few-shot settings exhibit that HC-GNN can outperform state-of-the-art GNN models in network analysis tasks, including node classification, link prediction, and community detection

Phase retrieval by hyperplanes

We show that a scalable frame does phase retrieval if and only if the hyperplanes of its orthogonal complements do phase retrieval. We then show this result fails in general by giving an example of a frame for $\mathbb R^3$ which does phase retrieval but its induced hyperplanes fail phase retrieval. Moreover, we show that such frames always exist in $\mathbb R^d$ for any dimension $d$. We also give an example of a frame in $\mathbb R^3$ which fails phase retrieval but its perps do phase retrieval. We will also see that a family of hyperplanes doing phase retrieval in $\mathbb R^d$ must contain at least $2d-2$ hyperplanes. Finally, we provide an example of six hyperplanes in $\mathbb R^4$ which do phase retrieval