The role of biases in on-line learning of two-layer networks

The influence of biases on the learning dynamics of a two-layer neural network, a normalized soft-committee machine, is studied for on-line gradient descent learning. Within a statistical mechanics framework, numerical studies show that the inclusion of adjustable biases dramatically alters the learning dynamics found previously. The symmetric phase which has often been predominant in the original model all but disappears for a non-degenerate bias task. The extended model furthermore exhibits a much richer dynamical behavior, e.g. attractive suboptimal symmetric phases even for realizable cases and noiseless data.

Biases in approximating log production

Most empirical work in economic growth assumes either a Cobb–Douglas production function expressed in logs or a log-approximated constant elasticity of substitution specification. Estimates from each are likely biased due to logging the model and the latter can also suffer from approximation bias. We illustrate this with a successful replication of Masanjala and Papagerogiou (The Solow model with CES technology: nonlinearities and parameter heterogeneity, Journal of Applied Econometrics 2004; 19: 171–201) and then estimate both models in levels to avoid these biases. Our estimation in levels gives results in line with conventional wisdom.

Nursing curriculum: Changing students' knowledge and biases towards older adults

Caring for the older adult is a topic debated and discussed at all levels of today's society. Nurses are expected to educate patients and family members about their medications and care following hospitalization or contact with the health care system. The majority of these patients are elderly. The purpose of the study was to determine if a course on aging would affect the knowledge and biases of nursing students in a Baccalaureate nursing program at a Southeast Florida University. Nursing students (N = 52) were surveyed at the beginning of the semester using Palmore's Facts on Aging Quiz that is structured to determine the knowledge and biases of individuals towards the older adult. Students were surveyed before and after the nursing course that had a didactic and clinical component in the hospital setting. Analysis of the data by Chi square and repeated measure ANOVA supported the hypothesis that a course segment on aging would affect the knowledge level of the nursing students and result in changes of their biases toward the older adult. ^

Gender Biases and Nontraditional Literature in the Urban Elementary Classroom

Gender stereotypes pervade children’s literature. This action research project sought to alter stereotypical perceptions of gender roles held by a classroom of urban elementary school students through the introduction of nontraditional gender role literature. Results suggested that some stereotypical perceptions could be altered through utilization and discussion of such literature.

The Effects of Attention Bias Modification Training on Cognitive Biases Perceived Threats in Children with Anxiety

It has been found in research that children and adults with anxiety have a bias toward interpreting ambiguous situations as threatening. This bias is thought to consequently maintain many symptoms of anxiety. An emergent computer treatment system called Attention Bias Modification Training (ABMT) has been used to try to reduce this bias. It is essential to understand whether this bias can be reduced with ABMT because of its feasibility and cost effective nature of treatment. In the current study, interpretation bias is measured using the Children's Opinions of Everyday Life Events (COELE). The ABMT treatment is given to children once a week for an hour and their answers to the COELE are recorded before and after treatment. The recorded procedures are transcribed by undergraduate students working at the Child Anxiety and Phobia lab, and then scored. Each of the situations of the COELE are rated 0 being neutral or 1 threatening interpretation of the situation. The hypothesis is that ABMT will reduce the negative interpretation bias in children over the course of 4 weeks of treatment. The study is still in the collection and transcription of data phase, and will expect to have analytical conclusions in the start of spring 2015.

"Sexism in the Schools" conference at P.S. 41 about sex biases in high schools

General note: Title and date provided by Bettye Lane.

"Sexism in the Schools" conference about sex biases in high schools

General note: Title and date provided by Bettye Lane.

"Sexism in the Schools" conference at P.S. 41 about sex biases in high schools

General note: Title and date provided by Bettye Lane.

"Sexism in the Schools" conference at P.S. 41 about sex biases in high schools

General note: Title and date provided by Bettye Lane.

Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models

Key life history traits such as breeding time and clutch size are frequently both heritable and under directional selection, yet many studies fail to document micro-evolutionary responses. One general explanation is that selection estimates are biased by the omission of correlated traits that have causal effects on fitness, but few valid tests of this exist. Here we show, using a quantitative genetic framework and six decades of life-history data on two free-living populations of great tits Parus major, that selection estimates for egg-laying date and clutch size are relatively unbiased. Predicted responses to selection based on the Robertson-Price Identity were similar to those based on the multivariate breeder’s equation, indicating that unmeasured covarying traits were not missing from the analysis. Changing patterns of phenotypic selection on these traits (for laying date, linked to climate change) therefore reflect changing selection on breeding values, and genetic constraints appear not to limit their independent evolution. Quantitative genetic analysis of correlational data from pedigreed populations can be a valuable complement to experimental approaches to help identify whether apparent associations between traits and fitness are biased by missing traits, and to parse the roles of direct versus indirect selection across a range of environments.

Predicting bee community responses to land-use changes: Effects of geographic and taxonomic biases

Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.