Measuring Part-Whole Bias: Some Evidence from Crop Biotechnology

We analyze the non-pecuniary aspects of some crop biotechnologies taken from three farm-level surveys. We focus particularly on the phenomen on of part-whole bias, which is the empirical finding that the sum of the stated part-worths (the value of each nonpecuniary characteristic) is greater than the stated total value of all the non-pecuniary characteristics. We analyze the empirical evidence of part-whole bias in the surveys, while decomposing it to further understand the phenomenon and to rescale the stated values of the non-pecuniary characteristics in the surveys. We find for all three surveys that the degree to which part-worths should be rescaled is about 60 percent.Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies,

Relative Importance of Environmental Attributes Using Logistic Regression

We investigate the problem of determining the relative importance of attributes in the discrete choice setting. Four alternative methods of extracting the relative importance of attributes are considered. The empirical application involves the development of a risk index system for individual herbicides combining the information on the herbicides' different human and environmental risks. The values of the pesticide risk indices are found to be consistent across the different methods.Environmental Economics and Policy,

Neuropsychological Sequela of Mild Traumatic Brain Injury: A Contemporary Meta-Analytic Review

Mild traumatic brain injuries (MTBIs) result in a constellation of non-specific physical, cognitive, and psychological symptoms. There is significant variability in neurocognitive recovery after MTBI, ranging from a few days to a few months, and others who fail to make complete recovery. A broad literature has attempted to elucidate what individual differences explain this variability. The present study sought to build upon previous meta-analyses, which systematically aggregated and examine relevant literature, by including a more heterogenous population and utilizing contemporary meta-analytic techniques. Three online databases (PsychINFO, PubMed, MedLine) were searched for pertinent studies. Separate random-effects Analogue-to-ANOVA were utilized to examine the overall neurocognitive effects of MTBI across time points, stratified by age, psychological comorbidity, populations of interest (athletes, general medical referrals, Veterans, litigants), and whether performance validity tests (PVT) were utilized. Subsequent analyses utilized meta-regressive techniques to simultaneously examine the variables of interest. After article review, 109 studies were retained for analysis (NMTBI = 5919, NControl = 8318). Analogue-to-ANOVA analyses revealed a medium-large overall neurocognitive effect size in the first 24 hours post-injury (d = .64) that decreased to a small effect size over the first 90 days (d = .24). Driven by a higher number of Veteran and litigant samples, the effect size increased in the post-acute period (\u3e 90 days; d = .39). Veteran samples were observed to have significantly larger effect sizes than other populations considered. Meta-regressive analyses found that, across heterogenous populations, time since injury (TSI) was predictive of overall cognitive function only prior to 90 days post-injury, but not in the post-acute period. Psychological functioning was the most important predictor of cognitive functioning after MTBI (β = .47), over and above TSI, population, demographic variables, injury parameters, age, or PVT. This study is consistent with the growing research suggesting that psychological functioning largely explains MTBI recovery and suggests that assessment of emotional well-being and psychological functioning should be part of routine clinical care for the management of MTBI

Kinetics of a Model Weakly Ionized Plasma in the Presence of Multiple Equilibria

We study, globaly in time, the velocity distribution $f(v,t)$ of a spatially homogeneous system that models a system of electrons in a weakly ionized plasma, subjected to a constant external electric field $E$. The density $f$ satisfies a Boltzmann type kinetic equation containing a full nonlinear electron-electron collision term as well as linear terms representing collisions with reservoir particles having a specified Maxwellian distribution. We show that when the constant in front of the nonlinear collision kernel, thought of as a scaling parameter, is sufficiently strong, then the $L^1$ distance between $f$ and a certain time dependent Maxwellian stays small uniformly in $t$. Moreover, the mean and variance of this time dependent Maxwellian satisfy a coupled set of nonlinear ODE's that constitute the ``hydrodynamical'' equations for this kinetic system. This remain true even when these ODE's have non-unique equilibria, thus proving the existence of multiple stabe stationary solutions for the full kinetic model. Our approach relies on scale independent estimates for the kinetic equation, and entropy production estimates. The novel aspects of this approach may be useful in other problems concerning the relation between the kinetic and hydrodynamic scales globably in time.Comment: 30 pages, in TeX, to appear in Archive for Rational Mechanics and Analysis: author's email addresses: [email protected], [email protected], [email protected], [email protected], [email protected]

Propagation of Chaos for a Thermostated Kinetic Model

We consider a system of N point particles moving on a d-dimensional torus. Each particle is subject to a uniform field E and random speed conserving collisions. This model is a variant of the Drude-Lorentz model of electrical conduction. In order to avoid heating by the external field, the particles also interact with a Gaussian thermostat which keeps the total kinetic energy of the system constant. The thermostat induces a mean-field type of interaction between the particles. Here we prove that, starting from a product measure, in the large N limit, the one particle velocity distribution satisfies a self consistent Vlasov-Boltzmann equation.. This is a consequence of "propagation of chaos", which we also prove for this model.Comment: This version adds affiliation and grant information; otherwise it is unchange

Null test for interactions in the dark sector

Since there is no known symmetry in Nature that prevents a non-minimal coupling between the dark energy (DE) and cold dark matter (CDM) components, such a possibility constitutes an alternative to standard cosmology, with its theoretical and observational consequences being of great interest. In this paper we propose a new null test on the standard evolution of the dark sector based on the time dependence of the ratio between the CDM and DE energy densities which, in the standard $\Lambda$CDM scenario, scales necessarily as $a^{-3}$. We use the latest measurements of type Ia supernovae, cosmic chronometers and angular baryonic acoustic oscillations to reconstruct the expansion history using model-independent Machine Learning techniques, namely, the Linear Model formalism and Gaussian Processes. We find that while the standard evolution is consistent with the data at $3\sigma$ level, some deviations from the $\Lambda$CDM model are found at low redshifts, which may be associated with the current tension between local and global determinations of $H_0$.Comment: 15 pages, 12 figure

Video Game Interventions to Improve Cognition in Older Adults

Cognitive abilities decline as part of the normal aging process. Various non-pharmacological interventions are being studied in an effort to ameliorate this cognitive decline. Some of these interventions include computerized cognitive training, such as neuropsychological software (i.e., brain training games) and video games. A previous study in our lab found that older adults who played a brain training game or a video poker game showed similar cognitive gains. The purpose of the present study was to follow the methodological procedures of our previous study to try and determine if the positive effects seen for the brain training program and video poker were due to training effects or merely engagement effects. In doing so, it also sought to determine if a visual art intervention, a relatively unstudied but potentially beneficial intervention, would elicit cognitive gains. Twenty-five individuals (Mage = 86, Meducation = 16.2) were quasi-randomly assigned to an experimental digital art intervention, Art Academy, or an active control condition, Tetris. Participants played their assigned game at least twenty minutes per day for six weeks. Comprehensive neuropsychological assessments were administered before and after the intervention. Outcome measures were in the form of residualized change scores were calculated by regressing the pre-test scores onto the post-test scores to reduce effects of baseline and other non-treatment factors. Compared to the Tetris group, the digital art group improved on aspects of a list-learning test, visual memory test, a scanning and sequencing task, a psychomotor task, a mental rotation task, and a composite score of all cognitive change (Total Change Score). The Tetris group improved on a math fluency task, and both groups improved on the delayed recall of a story memory task. However, the Art Academy group also engaged in the intervention for significantly more minutes of overall play time than the Tetris group, potentially confounding the results. Two groups were created via a median split based on the duration of gameplay: High Gameplay and Low Gameplay. The High Gameplay group showed greater improvement on visual memory, verbal memory, a measure of executive functioning, as well as the Total Change Score. Compared to the active control of the current study (Tetris), the Brain Age group of the previous study showed greater improvement on tasks that are specifically trained (i.e., visual working memory, math fluency) but not untrained tasks (e.g., verbal memory). The study suggests that playing a digital art video game could be a viable intervention to improve cognitive functioning in older adults. However, future research is also needed because the confounding of total gameplay time with group, a metric that other studies rarely report, precludes strong conclusions about the specific training effects

Accurate Modeling of Weak Lensing with the sGL Method

We revise and extend the stochastic approach to cumulative weak lensing (hereafter the sGL method) first introduced in Ref. [1]. Here we include a realistic halo mass function and density profiles to model the distribution of mass between and within galaxies, galaxy groups and galaxy clusters. We also introduce a modeling of the filamentary large-scale structures and a method to embed halos into these structures. We show that the sGL method naturally reproduces the weak lensing results for the Millennium Simulation. The strength of the sGL method is that a numerical code based on it can compute the lensing probability distribution function for a given inhomogeneous model universe in a few seconds. This makes it a useful tool to study how lensing depends on cosmological parameters and its impact on observations. The method can also be used to simulate the effect of a wide array of systematic biases on the observable PDF. As an example we show how simple selection effects may reduce the variance of observed PDF, which could possibly mask opposite effects from very large scale structures. We also show how a JDEM-like survey could constrain the lensing PDF relative to a given cosmological model. The updated turboGL code is available at turboGL.org.Comment: PRD style: 20 pages, 10 figures; replaced to match the improved version accepted for publication in PRD. The updated turboGL code can be downloaded at http://www.turbogl.org

VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming

We present VAEL, a neuro-symbolic generative model integrating variational autoencoders (VAE) with the reasoning capabilities of probabilistic logic (L) programming. Besides standard latent subsymbolic variables, our model exploits a probabilistic logic program to define a further structured representation, which is used for logical reasoning. The entire process is end-to-end differentiable. Once trained, VAEL can solve new unseen generation tasks by (i) leveraging the previously acquired knowledge encoded in the neural component and (ii) exploiting new logical programs on the structured latent space. Our experiments provide support on the benefits of this neuro-symbolic integration both in terms of task generalization and data efficiency. To the best of our knowledge, this work is the first to propose a general-purpose end-to-end framework integrating probabilistic logic programming into a deep generative model