HypoSVI: Hypocenter inversion with Stein Variational Inference and Physics Informed Neural Networks

<p>High resolution earthquake hypocentral locations are of critical importance for understanding the regional context driving seismicity. We introduce a scheme to reliably approximate a hypocenter posterior in a continuous domain that relies on recent advances in deep learning.</p><p>Our method relies on a differentiable forward model in the form of a deep neural network, which is trained to solve the Eikonal equation (EikoNet). EikoNet can rapidly determine the travel-time between any source-receiver pair for a non-gridded solution. We demonstrate the robustness of these travel-time solutions are for a series of complex velocity models.</p><p>For the inverse problem, we utilize Stein Variational Inference, which is a recent approximate inference procedure that iteratively updates a configuration of particles to approximate a target posterior by minimizing the so-called Stein discrepancy. The gradients of this objective function can be rapidly calculated due to the differentiability of the EikoNet. The particle locations are updated until convergence, after which we utilize clustering techniques and kernel density methods to determine the optimal hypocenter and its uncertainty.</p><p>The inversion procedure outlined in this work is validated using a series of synthetic tests to determine the parameter optimisation and the validity for large observational datasets, which can locate earthquakes in 439s per event for 2039 observations. In addition, we apply this technique to a case study of seismicity in the Southern California region for earthquakes from 2019.</p>

Conceptualizing an Interdisciplinary Collective Impact Approach to Examine and Intervene in the Chronic Cycle of Homelessness

Homelessness is a persistent problem in the United States in general and in Southern California especially. While progress has been made in reducing the number of people experiencing homelessness in the United States from 2007 (647,000) to 2019 (567,000), it remains an entrenched problem. The purpose of this paper is to outline a novel, interdisciplinary academic-practice partnership model to address homelessness. Where singular disciplinary approaches may fall short in substantially reducing homelessness at the community and population level, our model draws from a collective impact model which coordinates discipline-specific approaches through mutually reinforcing activities and shared metrics of progress and impact to foster synergy and sustainability of efforts. This paper describes the necessary capacity-building at the institution and community level for the model, the complementary strengths and contributions of each stakeholder discipline in the proposed model, and future goals for implementation to address homelessness in the Southern California region.

An Initial Framework for Implementation of Industry 4.0 in the High Technological Manufacturing Sector in Southern California

The fourth industrial revolution, also known as Industry 4.0 is a new paradigm that is significantly influencing several manufacturing industries across the globe. Industry 4.0 synchronizes concepts such as Smart Manufacturing, Smart Factory, and the Internet of Things with existing factory automation technologies in order to improve value in manufacturing by monitoring key performance indicators and creates value in all manufacturing related aspects. Currently, several industries have started early initiatives of implementing these technologies. As the industries are evaluating their readiness for implementing the Industry 4.0 concepts, there are several challenges which need to be addressed including high initial investment, lack of standardization, data security and lack of skilled labor. A strategic roadmap towards implementing the Industry 4.0 paradigms is still unclear in the industry as well as in academia. This research develops an initial framework for the effective implementation of Industry 4.0 in the high technology manufacturing sectors in the Southern California region. The results of this study are expected to provide a platform to expand the opportunities of Industry 4.0 further and facilitate worldwide adoption.

893. The SHIELD Orange County Project: A Decolonization Strategy in 35 Hospitals and Nursing Homes Reduces Multi-Drug-Resistant Organism (MDRO) Prevalence in a Southern California Region

Background Patient movement between hospitals, nursing homes (NH), and long-term acute care facilities (LTACs) contributes to MDRO spread. SHIELD OC is a regional decolonization collaborative among adult facilities with high patient sharing designed to reduce countywide MDRO prevalence. We report pre- and post-intervention MDRO colonization prevalence. Methods Decolonization included chlorhexidine bath (CHG) (4% liquid or 2% cloth) and twice-daily nasal swab 10% povidone–iodine (PI). LTAC and NH used CHG for all baths and PI 5 days on admission and Monday–Friday every other week. Patients in contact precautions (CP) at hospitals had daily CHG and 5-days PI on admission. Point-prevalence screening for MRSA, VRE, ESBL, and CRE using nares, axilla/groin, and peri-rectal swabs was conducted pre-intervention (September 2016–March 2017) and post-intervention (August 2018–April 2019); 50 random LTAC and 50 CP hospitalized patients were sampled; for NH up to 50 were sampled at baseline and all residents post-intervention. Raw impact of the intervention was assessed by the average change in colonization prevalence, with each facility carrying equal weight. Generalized linear mixed models (GLM) stratified by facility type were used to assess the impact on MDRO colonization when clustering by facility. Results Across 35 facilities (16 hospitals, 16 NHs, 3 LTACs), the overall MDRO prevalence was reduced 22% in NHs (OR 0.58, P < 0.001), 34% LTACs (OR = 0.27, P < 0.001), and 11% CP patients (OR = 0.67, P < 0.001, Table 1). For MRSA, raw reductions were 31% NHs (OR = 0.58, P < 0.001), 39% LTACs (OR = 0.51, P = 0.01), and 3% CP patients (OR = 0.88, P = NS). For VRE, raw reductions were 40% NHs (OR = 0.62, P = 0.001), 55% LTACs (OR = 0.26, P < 0.001), and 15% CP patients (OR = 0.67, P = 0.004). For ESBLs, raw reductions were 24% NHs (OR = 0.65, P < 0.001), 34% LTACs (OR = 0.53, P = 0.01), and 26% CP patients (OR = 0.64, P < 0.001). For CRE, raw reductions were 24% NHs (OR = 0.70, P = NS), and 23% LTACs (OR = 0.75, P = NS). CRE increased by 26% in CP averaged across hospitals, although patient -level CRE declined 2.4% to 1.8% (OR = 0.74, P = NS). Conclusion MDRO carriage was common in highly inter-connected NHs, LTACs and hospitals. A regional collaborative of universal decolonization in long-term care and targeted decolonization of CP patients in hospitals led to sizeable reductions in MDRO carriage. Disclosures All Authors: No reported Disclosures.

Institutional Completeness and Crime Rates in Immigrant Neighborhoods

Objectives: A growing body of research finds that immigration has a null or negative association with neighborhood crime rates. We build on this important literature by investigating the extent to which one theory, institutional completeness theory, may help explain lower crime rates in immigrant communities across the Southern California region. Specifically, we test whether two key measures of institutional completeness—the presence of immigrant/ethnic voluntary organizations in the community and the presence and diversity of immigrant/ethnic businesses in the community—account for lower crime rates in some immigrant communities. Method: Compiling a tract-level data set utilizing various data sources, we estimate negative binomial regression models predicting violent and property crime levels that include measures of institutional completeness while controlling for a range of neighborhood correlates of crime. We also account for possible endogeneity by estimating instrumental variable models. Results: The results reveal very limited support for institutional completeness theory. Conclusions: Several possible explanations for these findings are discussed.

Methane emissions from dairies in the Los Angeles Basin

We estimate the amount of methane (CH4) emitted by the largest dairies in the southern California region by combining measurements from four mobile solar-viewing ground-based spectrometers (EM27/SUN), in situ isotopic 13∕12CH4 measurements from a CRDS analyzer (Picarro), and a high-resolution atmospheric transport simulation with a Weather Research and Forecasting model in large-eddy simulation mode (WRF-LES). The remote sensing spectrometers measure the total column-averaged dry-air mole fractions of CH4 and CO2 (XCH4 and XCO2) in the near infrared region, providing information on total emissions of the dairies at Chino. Differences measured between the four EM27/SUN ranged from 0.2 to 22 ppb (part per billion) and from 0.7 to 3 ppm (part per million) for XCH4 and XCO2, respectively. To assess the fluxes of the dairies, these differential measurements are used in conjunction with the local atmospheric dynamics from wind measurements at two local airports and from the WRF-LES simulations at 111 m resolution. Our top-down CH4 emissions derived using the Fourier transform spectrometers (FTS) observations of 1.4 to 4.8 ppt s−1 are in the low end of previous top-down estimates, consistent with reductions of the dairy farms and urbanization in the domain. However, the wide range of inferred fluxes points to the challenges posed by the heterogeneity of the sources and meteorology. Inverse modeling from WRF-LES is utilized to resolve the spatial distribution of CH4 emissions in the domain. Both the model and the measurements indicate heterogeneous emissions, with contributions from anthropogenic and biogenic sources at Chino. A Bayesian inversion and a Monte Carlo approach are used to provide the CH4 emissions of 2.2 to 3.5 ppt s−1 at Chino.

Changing Neighborhood Determinants of Housing Price Trends in Southern California, 1960–2009

Research has generally failed to explore whether the effect of neighborhood characteristics on home values has changed over time. We take a long–range view and study decadal changing home values in the southern California region over a 50–year period, from 1960 to 2009. We focus on the effects of racial composition and measures associated with the New Urbanism on changing home values. We find that whereas neighborhoods with more racial/ethnic minorities and racial mixing experienced relative decreases in home values in the earlier decades, this effect has effectively disappeared in the most recent decade and actually became positive for some measures. We also found that certain characteristics associated with the New Urbanism—population density, older homes, a lack of concentration of single family units—show stronger positive effects on home values in the most recent decades.

Drastic Measures in Los Angeles

This essay is a review of two recent books of criticism: Bill Mohr's account of the Los Angeles poetry scene and Ignacio Lopez-Calvo's account of recent film and fiction set in Latino L.A. The essay argues for a conception of L.A. rooted in understanding the political and economic history of the city, and concludes with some speculation on the future of cultural production in the southern California region.