Genetic Algorithm Techniques in Climate Changepoint Problems

The first part of this dissertation studies genetic algorithms as a means of estimating the number of changepoints and their locations in a climatic time series. Such methods bypass classical subsegmentation algorithms, which sometimes yield suboptimal conclusions. Minimum description length techniques are introduced. These techniques require optimizing an objective function over all possible changepoint numbers and location times. Our general objective functions allow for correlated data, reference station aspects, and/or non-normal marginal distributions, all common features of climate time series. As an exhaustive evaluation of all changepoint configurations is not possible, the optimization is accomplished via a genetic algorithm that random walks through a subset of good models in an intelligent manner. The methods are applied in the analysis of 173 years of annual precipitation measurements from New Bedford, Massachusetts and the North Atlantic Basin\u27s tropical cyclone record. In the second part, trend estimation techniques are developed for monthly maximum and minimum temperatures observed in the conterminous 48 United States over the last century. While most scientists concur that this region has warmed in aggregate, there is no a priori reason to believe that temporal trends in extremes will have same patterns as trends in average temperatures. Indeed, under minor regularity conditions, the sample partial sum and maximum of stationary time series are asymptotically independent. Climatologists have found that minimum temperatures are warming most rapidly; such an aspect can be investigated via our methods. Here, models with extreme value and changepoint features are used to estimate trend margins and their standard errors. A spatial smoothing is then done to extract general structure. The results show that monthly maximum temperatures are not significantly changing - perhaps surprisingly, in more cases than not, they are cooling. In contrast, the minimum temperatures show significant warming. Overall, the Southeastern United States shows the least warming (even some cooling) and the Western United States, Northern Midwest, and New England have experienced the most warming

Trends in Extreme U.S. Temperatures

This paper develops trend estimation techniques for monthly maximum and minimum temperature time series observed in the 48 conterminous United States over the last century. While most scientists concur that this region has warmed on aggregate, there is no a priori reason to believe that temporal trends in extremes and averages will exhibit the same patterns. Indeed, under minor regularity conditions, the sample partial sum and maximum of stationary time series are asymptotically independent (statistically). Previous authors have suggested that minimum temperatures are warming faster than maximum temperatures in the United States; such an aspect can be investigated via the methods discussed in this study. Here, statistical models with extreme value and changepoint features are used to estimate trends and their standard errors. A spatial smoothing is then done to extract general structure. The results show that monthly maximum temperatures are not often greatly changing—perhaps surprisingly, there are many stations that show some cooling. In contrast, the minimum temperatures show significant warming. Overall, the southeastern United States shows the least warming (even some cooling), and the western United States, northern Midwest, and New England have experienced the most warming

Serum 25-hydroxyvitamin D and bone mineral density among children and adolescents in a Northwest Chinese city

Although vitamin D is essential for bone health, little is known about prevalence of vitamin D deficiency and low bone mineral density (BMD) among children, especially those in developing countries. It also remains unclear whether serum 25-hydroxyvitamin D [25(OH)D] is associated with BMD among children. We investigated these questions among children and adolescents in Yinchuan (latitude: 38° N), Ningxia, an economically underdeveloped province in Northwest China. A total of 1582 children (756 boys and 826 girls), aged 6–18 years, were recruited from schools using the stratified random sampling method in fall 2015. Serum 25(OH)D concentrations were measured by enzyme-linked immunosorbent assay, and BMD was quantified by dual-energy X-ray absorptiometry. Vitamin D deficiency (defined as serum 25(OH)D ≤ 37.5 nmol/L) was present in 35.5% of study subjects. There were no clear patterns of differences in serum 25(OH)D concentrations across the four age groups compared (6–9 years, 10–13 years, 14–16 years, and 17–18 years). The prevalence of low total body less head (TBLH) BMD (defined as a Z-score of ≤ −2.0 standard deviations away from the mean BMD values of the Chinese pediatric reference population) among children examined was 1.8% and was not significantly different among the four age groups considered. Linear regression analysis revealed that age, weight, and height were significantly and positively associated with TBLH BMD and that the strongest determinant of TBLH BMD was age in boys and weight in girls. There were no significant correlations between serum 25(OH)D concentrations and BMD obtained for total body and at various skeletal sites (r ranged from −0.005 to 0.014) regardless of whether children evaluated were sufficient, insufficient, or deficient in vitamin D. In conclusion, more than one-third of children and adolescents in a Northwest Chinese city were deficient in vitamin D but only <2% of them developed low BMD

Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data

The identification of causal relationships between random variables from large-scale observational data using directed acyclic graphs (DAG) is highly challenging. We propose a new mixed-effects structural equation model (mSEM) framework to estimate subject-specific DAGs, where we represent joint distribution of random variables in the DAG as a set of structural causal equations with mixed effects. The directed edges between nodes depend on observed exogenous covariates on each of the individual and unobserved latent variables. The strength of the connection is decomposed into a fixed-effect term representing the average causal effect given the covariates and a random effect term representing the latent causal effect due to unobserved pathways. The advantage of such decomposition is to capture essential asymmetric structural information and heterogeneity between DAGs in order to allow for the identification of causal structure with observational data. In addition, by pooling information across subject-specific DAGs, we can identify causal structure with a high probability and estimate subject-specific networks with a high precision. We propose a penalized likelihood-based approach to handle multi-dimensionality of the DAG model. We propose a fast, iterative computational algorithm, DAG-MM, to estimate parameters in mSEM and achieve desirable sparsity by hard-thresholding the edges. We theoretically prove the identifiability of mSEM. Using simulations and an application to protein signaling data, we show substantially improved performances when compared to existing methods and consistent results with a network estimated from interventional data. Lastly, we identify gray matter atrophy networks in regions of brain from patients with Huntington’s disease and corroborate our findings using white matter connectivity data collected from an independent study

Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data

The identification of causal relationships between random variables from large-scale observational data using directed acyclic graphs (DAG) is highly challenging. We propose a new mixed-effects structural equation model (mSEM) framework to estimate subject-specific DAGs, where we represent joint distribution of random variables in the DAG as a set of structural causal equations with mixed effects. The directed edges between nodes depend on observed exogenous covariates on each of the individual and unobserved latent variables. The strength of the connection is decomposed into a fixed-effect term representing the average causal effect given the covariates and a random effect term representing the latent causal effect due to unobserved pathways. The advantage of such decomposition is to capture essential asymmetric structural information and heterogeneity between DAGs in order to allow for the identification of causal structure with observational data. In addition, by pooling information across subject-specific DAGs, we can identify causal structure with a high probability and estimate subject-specific networks with a high precision. We propose a penalized likelihood-based approach to handle multi-dimensionality of the DAG model. We propose a fast, iterative computational algorithm, DAG-MM, to estimate parameters in mSEM and achieve desirable sparsity by hard-thresholding the edges. We theoretically prove the identifiability of mSEM. Using simulations and an application to protein signaling data, we show substantially improved performances when compared to existing methods and consistent results with a network estimated from interventional data. Lastly, we identify gray matter atrophy networks in regions of brain from patients with Huntington's disease and corroborate our findings using white matter connectivity data collected from an independent study

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Vinificazione in Cina del vino rosso da vitis quinquangularis rehd

Il Vitis quinquangularis Rehd è un'importante risorsa di alimentare in Cina. Nel processo di coltivazione, "nessun fertilizzante chimico, nessun pesticida, nessun pigmento chimico" e altri fattori antropici lo inquinano. Le bacche dell'uva sono nutrienti. Il vino prodotto con Vitis quinquangularis Rehd ha un sapore unico, un forte aroma di frutta e un gusto gradevole. La composizione nutriente del vino del Vitis quinquangularis Rehd viene esposta introducendo le caratteristiche dell'uva Vitis quinquangularis Rehd e le sue caratteristiche enologiche. Infine, viene descritto il processo di fermentazione del vino Mao e viene analizzato gli aromi nei vini

Predicting S&P500 Index Using Artificial Neural Network

International audienceThis paper studies artificial neural network algorithm as a means of modelling and forecasting the financial market data. Such method bypasses traditional statistical method to deal with financial time series data. A recurrent neural network model, Elman network, is implemented to incorporate autocorrelation in time series data. A 3-parameter model is chosen to fit and forecast S&P 500 index. The experimental data is from 2000–2007, to screen out the abnormal market environment after 2008 financial crisis

Comparison of Three Different Parallel Computation Methods for a Two-Dimensional Dam-Break Model

Three parallel methods (OpenMP, MPI, and OpenACC) are evaluated for the computation of a two-dimensional dam-break model using the explicit finite volume method. A dam-break event in the Pangtoupao flood storage area in China is selected as a case study to demonstrate the key technologies for implementing parallel computation. The subsequent acceleration of the methods is also evaluated. The simulation results show that the OpenMP and MPI parallel methods achieve a speedup factor of 9.8× and 5.1×, respectively, on a 32-core computer, whereas the OpenACC parallel method achieves a speedup factor of 20.7× on NVIDIA Tesla K20c graphics card. The results show that if the memory required by the dam-break simulation does not exceed the memory capacity of a single computer, the OpenMP parallel method is a good choice. Moreover, if GPU acceleration is used, the acceleration of the OpenACC parallel method is the best. Finally, the MPI parallel method is suitable for a model that requires little data exchange and large-scale calculation. This study compares the efficiency and methodology of accelerating algorithms for a dam-break model and can also be used as a reference for selecting the best acceleration method for a similar hydrodynamic model