An Improved Method of Automated Nonparametric Content Analysis for Social Science

Some scholars build models to classify documents into chosen categories. Others, especially social scientists who tend to focus on population characteristics, instead usually estimate the proportion of documents in each category—using either parametric “classify-and-count” methods or “direct” nonparametric estimation of proportions without individual classification. Unfortunately, classify-and-count methods can be highly model-dependent or generate more bias in the proportions even as the percent of documents correctly classified increases. Direct estimation avoids these problems, but can suffer when the meaning of language changes between training and test sets or is too similar across categories. We develop an improved direct estimation approach without these issues by including and optimizing continuous text features, along with a form of matching adapted from the causal inference literature. Our approach substantially improves performance in a diverse collection of 73 datasets. We also offer easy-to-use software that implements all ideas discussed herein.

Time to imagine moving: Simulated motor activity affects time perception

Sensing the passage of time is important for countless daily tasks, yet time perception is easily influenced by perception, cognition, and emotion. Mechanistic accounts of time perception have traditionally regarded time perception as part of central cognition. Since proprioception, action execution, and sensorimotor contingencies also affect time perception, perception-action integration theories suggest motor processes are central to the experience of the passage of time. We investigated whether sensory information and motor activity may interactively affect the perception of the passage of time. Two prospective timing tasks involved timing a visual stimulus display conveying optical flow at increasing or decreasing velocity. While doing the timing tasks, participants were instructed to imagine themselves moving at increasing or decreasing speed, independently of the optical flow. In the direct-estimation task, the duration of the visual display was explicitly judged in seconds while in the motor-timing task, participants were asked to keep a constant pace of tapping. The direct-estimation task showed imagining accelerating movement resulted in relative overestimation of time, or time dilation, while decelerating movement elicited relative underestimation, or time compression. In the motor-timing task, imagined accelerating movement also accelerated tapping speed, replicating the time-dilation effect. The experiments show imagined movement affects time perception, suggesting a causal role of simulated motor activity. We argue that imagined movements and optical flow are integrated by temporal unfolding of sensorimotor contingencies. Consequently, as physical time is relative to spatial motion, so too is perception of time relative to imaginary motion.

Accurate Pore Fluid Indicator Prediction Using Seismic Fluid Bulk Modulus Inversion

Submitted Abstract Objectives/Scope The prediction of fluid parameter related to hydrocarbon presence using seismic data has often been limited by the performance of probability density function in estimating fluid properties from seismic inversion results. A novel fluid bulk modulus inversion (fBMI) is a pre-stack seismic inversion technique that has been developed to allow a direct estimation of pore fluid bulk modulus (Kf) from seismic data. Real data application in Malay basin showcases that Kf volume can be used to pinpoint areas with high probability of hydrocarbon presence. Methods, Procedures, Process The fluid term AVO reflectivity (Russell et al., 2011) is used as the basis of our formulation and has been extended to allow direct estimation of pore fluid bulk modulus, shearmodulus, porosity parameter and density through standard least-square inversion. The novel formulation is able to relax the dependency of fluid terms on the porosity. To demonstrate this, verifications were made against standard linear AVO approximations. Our observation shows that the young tertiary basins such as the Malay basin the fluid bulk modulus values have a big contrast between hydrocarbon saturated and water bearing reservoirs with a minimum of 60% ratio difference. The inverted fluid bulk modulus volume provides thus a direct assessment of areas with high probability of hydrocarbon saturation. Results, Observations, Conclusions In this paper, the fBMI technique is showcased on a field in the Malay basin. The outcome is demonstrated on a well panel analysis for four wells located across the study area (Figure 1). The inverted fluid bulk modulus extracted along a horizon representing the top of target reservoir is shown in Figure 2b. The blue color indicates high bulk modulus corresponds to water-bearing zone, while the yellow-red color range corresponding to low hydrocarbon-bearing zones. The areas of low fluid bulk modulus values at the north-western region are calibrated to known production zones in that region. fBMI shows areas that delineate high probability of hydrocarbon presence and provides a quantitative measure in terms of fluid parameter directly related to the presence of hydrocarbon saturations. Figure 1: Comparison analysis of water saturation (blue curve) and fluid bulk modulus (red curve) of well log data in the Malay basin. Black strips indicate the coal intervals. Figure 2: a) Inverted acoustic impedance extracted from the top reservoir horizon of a field in the Malay basin. b) The corresponding fluid bulk modulus values from fBMI. Novel/Additive Information The fBMI is a new four parameters linear amplitude-versus-offset inversion technique that provides quantitative fluid parameter directly related to fluid bulk modulus from seismic data. It is utilized as a tool for direct hydrocarbon prospect assessment to differentiate gas, oil, condensate and water.

Dynamic changes in moisture content and applicability analysis of a typical litter prediction model in Yunnan Province

Background Forest fire risk predictions are based on the most conservation daily predictions, and the lowest litter moisture content of each day is often used to predict the day’s fire risk. Yunnan Province is the area with the most frequent and serious forest fires in China, but there is almost no research on the dynamic changes and model predictions of the litter moisture content in this area. Therefore, to reduce the occurrence of forest fires and improve the accuracy of forest fire risk predictions, it is necessary to understand these dynamic changes and establish an appropriate prediction model for the typical litter moisture content in Yunnan Province. Method During the fire prevention period, daily dynamic changes in the litter moisture content are obtained by monitoring the daily step size, and the relationships between the litter moisture content and meteorological elements are analyzed. In this study, the meteorological element regression method, moisture code method and direction estimation method are selected to establish litter moisture content prediction models, and the applicability of each model is analyzed. Results We found that dynamic changes in the litter moisture content have obvious lags compared with meteorological elements, and the litter moisture content is mainly related to the air temperature, relative humidity and wind speed. With an increase in the sampling interval of meteorological elements, the significances of these correlations first increase and then decrease. The moisture content value obtained by directly using the moisture code method in the Fire Weather Index (FWI) significantly different from the measured value, so this method is not applicable. The mean absolute error (MAE) and mean relative error (MRE) values obtained with the meteorological element regression method are 2.97% and 14.06%, those from the moisture code method are 3.27% and 14.07%, and those from the direct estimation method are 2.82% and 12.76%, respectively. Conclusions The direct estimation method has the lowest error and the strongest extrapolation ability; this method can meet the needs of daily fire forecasting. Therefore, it is feasible to use the direct estimation method to predict litter moisture contents in Yunnan Province.