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Despite increasing risks from sea-level rise (SLR) and storms, US coastal communities continue to attract relatively high-income residents, and coastal property values continue to rise. To understand this seeming paradox and explore policy responses, we develop the Coastal Home Ownership Model (C-HOM) and analyze the long-term evolution of coastal real estate markets. C-HOM incorporates changing physical attributes of the coast, economic values of these attributes, and dynamic risks associated with storms and flooding. Resident owners, renters, and non-resident investors jointly determine coastal property values and the policy choices that influence the physical evolution of the coast. In the coupled system, we find that subsidies for coastal management, such as beach nourishment, tax advantages for high-income property owners, and stable or increasing property values outside the coastal zone all dampen the effects of SLR on coastal property values. The effects, however, are temporary and only delay precipitous declines as total inundation approaches. By removing subsidies, prices would more accurately reflect risks from SLR but also trigger more coastal gentrification, as relatively high-income owners enter the market and self-finance nourishment. Our results suggest a policy tradeoff between slowing demographic transitions in coastal communities and allowing property markets to adjust smoothly to risks from climate change.

 

Spatial resolution is critical for observing and monitoring environmental phenomena. Acquiring high-resolution bathymetry data directly from satellites is not always feasible due to limitations on equipment, so spatial data scientists and researchers turn to single image super-resolution (SISR) methods that utilize deep learning techniques as an alternative method to increase pixel density. While super resolution residual networks (e.g., SR-ResNet) are promising for this purpose, several challenges still need to be addressed: (1) Earth data such as bathymetry is expensive to obtain and relatively limited in its data record amount; (2) certain domain knowledge needs to be complied with during model training; (3) certain areas of interest require more accurate measurements than other areas. To address these challenges, following the transfer learning principle, we study how to leverage an existing pre-trained super-resolution deep learning model, namely SR-ResNet, for high-resolution bathymetry data generation. We further enhance the SR-ResNet model to add corresponding loss functions based on domain knowledge. To let the model perform better for certain spatial areas, we add additional loss functions to increase the penalty of the areas of interest. Our experiments show our approaches achieve higher accuracy than most baseline models when evaluating using metrics including MSE, PSNR, and SSIM. 

Empirical diagnosis of stability has received considerable attention, often focused on variance metrics for early warning signals of abrupt system change or delicate techniques measuring Lyapunov spectra. The theoretical foundation for the popular early warning signal approach has been limited to relatively simple system changes such as bifurcating fixed points where variability is extrinsic to the steady state. We offer a novel measurement of stability that applies in wide ranging systems that contain variability in both internal steady state dynamics and in response to external perturbations. Utilizing connections between stability, dissipation, and phase space flow, we show that stability correlates with temporal asymmetry in a measure of phase space flow contraction. Our method is general as it reveals stability variation independent of assumptions about the nature of system variability or attractor shape. After showing efficacy in a variety of model systems, we apply our technique for measuring stability to monthly returns of the S&P 500 index in the time periods surrounding the global stock market crash of October 1987. Market stability is shown to be higher in the several years preceding and subsequent to the 1987 market crash. We anticipate our technique will have wide applicability in climate, ecological, financial, and social systems where stability is a pressing concern.

 

Abstract Background Individuals on the autism spectrum are reported to display alterations in interoception, the sense of the internal state of the body. The Interoception Sensory Questionnaire (ISQ) is a 20-item self-report measure of interoception specifically intended to measure this construct in autistic people. The psychometrics of the ISQ, however, have not previously been evaluated in a large sample of autistic individuals. Methods Using confirmatory factor analysis, we evaluated the latent structure of the ISQ in a large online sample of adults on the autism spectrum and found that the unidimensional model fit the data poorly. Using misspecification analysis to identify areas of local misfit and item response theory to investigate the appropriateness of the seven-point response scale, we removed redundant items and collapsed the response options to put forth a novel eight-item, five-response choice ISQ. Results The revised, five-response choice ISQ (ISQ-8) showed much improved fit while maintaining high internal reliability. Differential item functioning (DIF) analyses indicated that the items of the ISQ-8 were answered in comparable ways by autistic adolescents and adults and across multiple other sociodemographic groups. Limitations Our results were limited by the fact that we did not collect data for typically developing controls, preventing the analysis of DIF by diagnostic status. Additionally, while this study proposes a new 5-response scale for the ISQ-8, our data were not collected using this method; thus, the psychometric properties for the revised version of this instrument require further investigation. Conclusion The ISQ-8 shows promise as a reliable and valid measure of interoception in adolescents and adults on the autism spectrum, but additional work is needed to examine its psychometrics in this population. A free online score calculator has been created to facilitate the use of ISQ-8 latent trait scores for further studies of autistic adolescents and adults (available at https://asdmeasures.shinyapps.io/ISQ_score/ ). 

We study the problem of predicting human biogeographical ancestry using genomic data. While continental level ancestry is relatively simple using genomic information, distinguishing between individuals from closely associated subpopulations (e.g., from the same continent) is still a difficult challenge. In particular, we focus on the case where the analysis is constrained to using single nucleotide polymorphisms (SNPs) from just one chromosome. We thus propose methods to construct such ancestry informative SNP panels, and access the performance of such SNP panels from just one chromosome, for both continental-level and sub-population level ancestry prediction. We include results that demonstrate the performance of the proposed methods, including comparison with other recently published related methods.