Grounds for Argument: Local Understandings, Science, and Global Processes in Special Forest Products Harvesting

In posing the question Where are the pickers? , Love and Jones suggest that the shifting paradigm in forestry is real and that academia is not leading the shift. Love and Jones illustrate the emergence of special forest products\u27 legitimacy in competing uses of forests with their experience and research in mushroom harvesting in the Pacific Northwest

Improving California brush ranges /

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Heuristics as Bayesian inference under extreme priors

Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics

Robust priors for regularized regression

Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for a more general model, yielding a principled way to interpolate between models of differing complexity. We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data. Models with robust priors had excellent worst-case performance. Solutions followed from the form of the heuristic that was used to derive the prior. These new algorithms can serve applications in data analysis and machine learning, as well as help in understanding how people transition from novice to expert performance

Fair Pay for Writers’ research report

This research was produced in collaboration with Literature Wales. The research presents guidelines for calculating a fair rate of pay for authors’ working in Wales that are sensitive to a range of considerations both from the perspective of authors’ and commissioning organisations

Multistability of free spontaneously-curved anisotropic strips

Multistable structures are objects with more than one stable conformation, exemplified by the simple switch. Continuum versions are often elastic composite plates or shells, such as the common measuring tape or the slap bracelet, both of which exhibit two stable configurations: rolled and unrolled. Here we consider the energy landscape of a general class of multistable anisotropic strips with spontaneous Gaussian curvature. We show that while strips with non-zero Gaussian curvature can be bistable, strips with positive spontaneous curvature are always bistable, independent of the elastic moduli, strips of spontaneous negative curvature are bistable only in the presence of spontaneous twist and when certain conditions on the relative stiffness of the strip in tension and shear are satisfied. Furthermore, anisotropic strips can become tristable when their bending rigidity is small. Our study complements and extends the theory of multistability in anisotropic shells and suggests new design criteria for these structures.Comment: 20 pages, 10 figure

The effect of the Madden-Julian Oscillation on station rainfall and river level in the Fly River system, Papua New Guinea

The Madden-Julian oscillation (MJO) is the dominant mode of intraseasonal variability in tropical rainfall on the large scale, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. The Fly River system, Papua New Guinea, is one of the wettest regions on Earth and is at the heart of the MJO envelope. A 16 year time series of daily precipitation at 15 stations along the river system exhibits strong MJO modulation in rainfall. At each station, the difference in rainfall rate between active and suppressed MJO conditions is typically 40% of the station mean. The spread of rainfall between individual MJO events was small enough such that the rainfall distributions between wet and dry phases of the MJO were clearly separated at the catchment level. This implies that successful prediction of the large-scale MJO envelope will have a practical use for forecasting local rainfall. In the steep topography of the New Guinea Highlands, the mean and MJO signal in station precipitation is twice that in the satellite Tropical Rainfall Measuring Mission 3B42HQ product, emphasizing the need for ground-truthing satellite-based precipitation measurements. A clear MJO signal is also present in the river level, which peaks simultaneously with MJO precipitation input in its upper reaches but lags the precipitation by approximately 18 days on the flood plains

Real-time localised forecasting of the Madden-Julian Oscillation using neural network models

Existing statistical forecast models of the Madden-Julian Oscillation (MJO) are generally of very low order and predict the evolution of a small number (typically two) of principal components (PCs). While such models are skilful up to 25 days lead time, by design they only predict the very largest-scale features of the MJO. Here we present a higher-order MJO statistical forecast model that is able to predict MJO variability on smaller, more localised scales, that will be of more direct benefit to national weather agencies and regional government planning. The model is based on daily outgoing long-wave radiation (OLR) data that are intraseasonally filtered using a recently developed technique of empirical mode decomposition that can be used in real time. A standard truncated PC analysis is then used to isolate the maximum amount of variance in a finite number of modes. The evolution of these modes is then forecast using a neural network model, which does not suffer from the parametrisation problems of other statistical forecast techniques when applied to a higher number of modes. Compared to a standard 2-PC model, the higher-order PC model showed improved skill over the whole MJO domain, with substantial improvements over the western Pacific, Arabian Sea, Bay of Bengal, South China Sea and Phillipine Sea

Style For Life: A Fashion Blog Analysis

Social media usage has drastically increased and has made interacting with a much larger number of people from around the world possible. Because social media has allowed increased interaction, blogging has been able to capitalize on this by building brand awareness and exposure in the market with more people as a result. This has also helped fashion blogging become a huge part of the fashion industry, with it now being viewed as a respectable facet of the industry. These people have become hugely influential in the fashion world, having been deemed the title of product influencers. Consumers become invested in bloggers largely because they view them as incredibly trustworthy and not trying to necessarily sell something for the sake of selling; they truly believe in and use the product/service being promoted. These blogs become a sense of community for readers, and readers look to them for advice on their product purchases. Advertisers and companies see how influential bloggers have become on the consumers they are trying to target. They now use these sites for their advertisements. Secondary research was conducted to learn more about what fashion blogging really is, why it is important, and who are the notable people in the industry. It was found that consumers read fashion blogs for various reasons, but they really like them for their relatability and authenticity. Many of the notable bloggers had a wide range of the number of followers. I also created my own blog, Style for Life, to showcase a personal fashion blog and to really see what it is like to be a fashion blogger