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In the presence of risk and uncertainty, measures such as poverty rates are inadequate to analyze the well-being of poor households. The poor are not only concerned about the current low levels of their income or consumption, but also the likelihood of experiencing stressful declines in these levels in the future. Risks to livelihood are particularly important in rural areas where there is generally high dependence on agriculture and the environment. In this study, the author analyzes the nature, extent, and causes of rural vulnerability in Serbia using panel national household data from the 2002 and 2003 Serbia Living Standard Surveys. He measures rural vulnerability as a function of nonstochastic determinants of poverty as well as exposure to risk. While low levels of consumption (poverty) explain about 70 percent of vulnerability, the author identifies risk and uncertainty as crucial dimensions of rural life in accounting for the remaining 30 percent of household vulnerability. Households and regions with a greater share of their livelihood depending on agricultural activities are more at risk of vulnerability than those with a significantly higher share of their income coming from nonagricultural sources. Dependence on agricultural income is directly associated with higher aggregate risk, underscoring the agricultural sector's lopsided exposure to covariate shocks in general, and the negative impact of the 2003 drought in particular. Rural vulnerability to poverty and risk is also strongly associated with asset ownership and access to markets to mobilize them in time of need.

Access To Markets --- Agricultural Sector --- Covariate Shocks --- Drought --- Health, Nutrition and Population --- Household Vulnerability --- Household Welfare --- Human Development --- Income --- Measures --- Poor --- Poor Households --- Population Policies --- Poverty --- Poverty Assessment --- Poverty Measures --- Poverty Rates --- Poverty Reduction --- Risks --- Rural --- Rural Areas --- Rural Development --- Rural Life --- Rural Poverty Reduction --- Rural Vulnerability

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Economists have long sought to learn the effect of a "treatment" on some outcome of interest, just as doctors do with their patients. A central practical objective of research on treatment response is to provide decision makers with information useful in choosing treatments. Often the decision maker is a social planner who must choose treatments for a heterogeneous population--for example, a physician choosing medical treatments for diverse patients or a judge choosing sentences for convicted offenders. But research on treatment response rarely provides all the information that planners would like to have. How then should planners use the available evidence to choose treatments? This book addresses key aspects of this broad question, exploring and partially resolving pervasive problems of identification and statistical inference that arise when studying treatment response and making treatment choices. Charles Manski addresses the treatment-choice problem directly using Abraham Wald's statistical decision theory, taking into account the ambiguity that arises from identification problems under weak but justifiable assumptions. The book unifies and further develops the influential line of research the author began in the late 1990s. It will be a valuable resource to researchers and upper-level graduate students in economics as well as other social sciences, statistics, epidemiology and related areas of public health, and operations research.

Estimation theory. --- Social choice. --- Social sciences --- Statistical methods. --- "Objective Function. --- Bayes Rules. --- Binary Covariates. --- Bounding Expected Welfare. --- Concave Monotonicity. --- Covariate Information. --- Empirical Success Rules. --- Hoeffding theorem. --- Hypothesis Testing. --- Maximin Criterion. --- Nonutilitarian Objective Functions. --- Optimal Treatment Rule. --- Semi-Monotone Response. --- Sentencing. --- Social Interactions. --- Untenable Assumptions. --- conditional empirical success. --- external validity. --- instrumental variable. --- quasi-optimal". --- shrinkage estimators. --- variable selection procedures.

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This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others.

Information technology industries --- sleep stage scoring --- neural network-based refinement --- residual attention --- T-end annotation --- signal quality index --- tSQI --- optimal shrinkage --- emotion --- EEG --- DEAP --- CNN --- surgery image --- disgust --- autonomic nervous system --- electrocardiogram --- galvanic skin response --- olfactory training --- psychophysics --- smell --- wearable sensors --- wine sensory analysis --- accuracy --- convolution neural network (CNN) --- classifiers --- electrocardiography --- k-fold validation --- myocardial infarction --- sensitivity --- sleep staging --- electroencephalography (EEG) --- brain functional connectivity --- frequency band fusion --- phase-locked value (PLV) --- wearable device --- emotional state --- mental workload --- stress --- heart rate --- eye blinks rate --- skin conductance level --- emotion recognition --- electroencephalogram (EEG) --- photoplethysmography (PPG) --- machine learning --- feature extraction --- feature selection --- deep learning --- non-stationarity --- individual differences --- inter-subject variability --- covariate shift --- cross-participant --- inter-participant --- drowsiness detection --- EEG features --- drowsiness classification --- fatigue detection --- residual network --- Mish --- spatial transformer networks --- non-local attention mechanism --- Alzheimer’s disease --- fall detection --- event-centered data segmentation --- accelerometer --- window duration --- n/a --- Alzheimer's disease

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A powerful framework for understanding how natural selection shapes adaptation and biological designDesign and diversity are the two great challenges in the study of life. Microbial Life History draws on the latest advances in microbiology to describe the fundamental forces of biological design and apply these evolutionary processes to a broad diversity of traits in microbial metabolism and biochemistry.Emphasizing how to formulate and test hypotheses of adaptation, Steven Frank provides a new foundation for exploring the evolutionary forces of design. He discusses the economic principles of marginal valuations, tradeoffs, and payoffs in risky and random environments; the social aspects of conflict and cooperation; the demographic aspects of age and spatial heterogeneity; and the engineering control theory principles by which systems adjust to environments. Frank then applies these evolutionary principles to the biochemistry of microbial metabolism, providing the first comprehensive link between the forces that shape biological design and cellular energetics.Tracing how natural selection sculpts metabolism, Microbial Life History provides new perspectives on the life histories of organisms, from growth rate and survival to dispersal and defense against attack. Along the way, this incisive book addresses the conceptual and philosophical challenges confronting evolutionary biologists and other practitioners who study biological design and seek to apply its lessons.

Microbial metabolism --- Evolution (Biology) --- Evolution. --- Allele frequency. --- Availability. --- Bacteria. --- Bacteroides. --- Biochemistry. --- Biomass (ecology). --- Biomass. --- Carbon. --- Catabolism. --- Causality. --- Cell membrane. --- Complication (horology). --- Computer scientist. --- Computer simulation. --- Contexts. --- Covariance. --- Covariate. --- Credibility. --- David Spergel. --- Distrust. --- Electron acceptor. --- Electron transport chain. --- Employment. --- Environmental factor. --- Enzyme. --- Equilibrium (puzzle). --- Escherichia coli. --- Fecundity. --- Feed forward (control). --- Fermentation. --- Fitness (biology). --- Fitness landscape. --- Gamma ray. --- Genetic correlation. --- Genotype. --- Glucose uptake. --- Glucose. --- Glycine. --- Glycolysis. --- Heat transfer. --- Hydrogen sulfide. --- Innovation. --- International Union of Pure and Applied Physics. --- Kin selection. --- Laboratory. --- Laser. --- Likelihood function. --- Low frequency. --- Measurement. --- Metabolism. --- Metabolite. --- Metallicity. --- Microbial metabolism. --- Microbiology. --- Microorganism. --- Molecule. --- Monosaccharide. --- Mutation rate. --- National Science Foundation. --- Natural environment. --- Nicotinamide adenine dinucleotide. --- One-Step. --- Optics. --- Organism. --- Oxidative stress. --- Parameter. --- Pareto efficiency. --- Phenotype. --- Phenotypic trait. --- Prediction. --- Probability. --- Processing (Chinese materia medica). --- Protein. --- Proteome. --- Redox. --- Reproductive success. --- Reproductive value (population genetics). --- Robotic telescope. --- Secretion. --- Selection coefficient. --- Siderophore. --- Sociality. --- Star system. --- Stellar mass. --- Stimulation. --- Subset. --- Sulfate-reducing bacteria. --- Sulfate. --- Sulfide. --- Sunset provision. --- Synthetic biology. --- Technology. --- Toxin. --- Trade-off. --- Trait theory. --- Unit sphere. --- Virulence. --- Weighting. --- rRNA Operon.

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Although weather shocks are a major source of income fluctuation, most of the world's poor lack insurance coverage against them. Absence of formal insurance contributes to poverty traps, as investment decisions are conflicted with risk management ones: risk-averse farmers tend to underinvest and produce lower yielding yet safer crops. In the past few years, weather index insurance has gained increasing attention as an effective tool to provide small-scale farmers coverage against aggregate shocks. However, there is little empirical evidence about its effectiveness. This paper studies the effect of the recently introduced rainfall-indexed insurance on farmers' productivity, risk management strategies, as well as per capita income and expenditure in Mexico. The identification strategy takes advantage of the variation across counties and across time in which the insurance was rolled-out. The analysis finds that the presence of insurance in treated counties has significant and positive effects on maize productivity. Similarly, there is a positive association between the presence of insurance in the municipality and rural households' per capita expenditure and income, although no significant relation is found between the presence of insurance and the number of hectares destined for maize production.

Administrative Costs --- Adverse Selection --- Agricultural Development --- Agricultural Insurance --- Agricultural Land --- Agricultural Policy --- Agricultural Production --- Agricultural Productivity --- Agricultural Technology --- Agriculture --- Basis Risk --- Beneficiaries --- Cash Crops --- Cash Transfer Programs --- Cash Transfers --- Checks --- Claims --- Communal Land --- Consumption Smoothing --- Contracts --- Counterfactual --- Covariate Shocks --- Coverage --- Credit --- Crop Insurance --- Crop Varieties --- Crops & Crop Management Systems --- Debt Markets --- Development Economics --- Drought --- Durable --- Durable Assets --- Economics --- Effects --- Efficiency --- Equity --- Exchange --- Expenditure --- Extreme Poverty --- Famine --- Farmers --- Female Labor --- Female Labor Force --- Finance and Financial Sector Development --- Financial Support --- Guarantee --- Household Head --- Household Income --- Household Survey --- Illiteracy --- Implicit Contracts --- Incentives --- Income --- Income Smoothing --- Indemnity --- Indemnity Payments --- Infant Mortality --- Information --- Insurance --- Insurance & Risk Mitigation --- Insurance Company --- Insurance Contracts --- Insurance Coverage --- Insurance Market --- Insurance Policies --- Insurance Premiums --- Insurance Product --- Insurances --- Insurers --- Interest --- International Bank --- Investment --- Investment Decisions --- Irrigation --- Labor --- Labor Force --- Labor Policies --- Lack of Infrastructure --- Land Quality --- Land Size --- Loans --- Loss --- Malnutrition --- Management --- Market --- Market Failures --- Measures --- Minimum Wages --- Moral Hazard --- Mortality --- Organizations --- Outcomes --- Policies --- Policyholders --- Political Economy --- Poor --- Poor Rural Household --- Poverty --- Poverty Index --- Poverty Levels --- Poverty Reduction --- Premiums --- Private Insurance --- Private Insurance Companies --- Production --- Production of Cash Crops --- Productivity --- Productivity Growth --- Profit --- Programs --- Property Rights --- Rates --- Real Income --- Reinsurance --- Reinsurance Markets --- Rights --- Risk --- Risk Exposure --- Risk Management --- Risk Management Strategies --- Risk Sharing --- Risk Sharing Arrangements --- Risk Taking --- Risk Transfer --- Risks --- Running Water --- Rural --- Rural Areas --- Rural Household --- Rural Level --- Rural Population --- Rural Poverty --- Rural Poverty Reduction --- Rural Settings --- Social Protections and Labor --- Standards --- Supply --- Theory --- Training --- Transfer Programs --- Transfers --- Value --- Wages