Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum.

Food preferences are acquired through experience and can exert strong influence on choice behavior. In order to choose which food to consume, it is necessary to maintain a predictive representation of the subjective value of the associated food stimulus. Here, we explore the neural mechanisms by which such predictive representations are learned through classical conditioning. Human subjects were scanned using fMRI while learning associations between arbitrary visual stimuli and subsequent delivery of one of five different food flavors. Using a temporal difference algorithm to model learning, we found predictive responses in the ventral midbrain and a part of ventral striatum (ventral putamen) that were related directly to subjects' actual behavioral preferences. These brain structures demonstrated divergent response profiles, with the ventral midbrain showing a linear response profile with preference, and the ventral striatum a bivalent response. These results provide insight into the neural mechanisms underlying human preference behavior.

Measuring design investment in firms: Conceptual foundations and exploratory UK survey

The importance of design to company and national performance has been widely discussed, with a number of studies investigating the value or impact of design on performance. However, none of these studies has measured design investment as an input against which performance can be compared. As yet, there is no established way in which design investment might be measured. Without such a method, we cannot develop a reliable picture, akin to that for R&D spending, on the impact of design spending on company performance. This paper presents a conceptual framework for the measurement of design investment and applies this framework in a survey of UK firms. The framework describes design as being part of the creation and commercialization of new products and services. The survey highlights some surprising patterns of design spend in the reported sample and demonstrates the viability of the underpinning framework. A revised framework is proposed that situates design investment in the context of R&D. The model has implications for policy makers trying to understand the role and scale of design in the private sector, for managers wishing to optimize their design investments and for academics seeking to measure the value of design. © 2013 Published by Elsevier B.V.

A survey of alternative once-through fast reactor core designs

Reprocessing of Light Water Reactor (LWR) spent fuel to recover plutonium or transuranics for use in Sodium cooled Fast Reactors (SFRs) is a distant prospect in the U.S.A. This has motivated our evaluation of potentially cost-effective operation of uranium startup fast reactors (USFRs) in a once-through mode. This review goes beyond findings reported earlier based on a UC fueled MgO reflected SFR to describe a broader parametric study of options. Cores were evaluated for a variety of fuel/coolant/reflector combinations: UC/UZr/UO 2/UN;Na/Pb; MgO/SS/Zr. The challenge is achieving high burnup while minimizing enrichment and respecting both cladding fluence/dpa and reactivity lifetime limits. These parametric studies show that while UC fuel is still the leading contender, UO 2 fuel and ZrH 1.7 moderated metallic fuel are also attractive if UC proves to be otherwise inadequate. Overall, these findings support the conclusion that a competitive fuel cycle cost and uranium utilization compared to LWRs is possible for SFRs operated on a once-through uranium fueled fuel cycle. In addition, eventual transition to TRU recycle mode is studied, as is a small test reactor to demonstrate key features.

A survey on technology exposure and range of abilities of elderly and disabled users in India

This paper reports a survey on people with age-related and physical impairments in India. The survey evaluates functional parameters related to human computer interaction and reports subjective attitude and exposure of users towards technology. We found a significant cognitive decline in elderly users while their functional parameters are sufficient to use existing electronic devices. However young disabled users are found to be experienced with computer but could not have access to appropriate assistive devices, which would benefit them. Most users used desktop computers and mobile phone but none used tablet, smartphone or kiosks though they are keen to learn new technologies. Overall we hope that our results will be useful for HCI practitioners in developing countries. © 2013 Springer-Verlag Berlin Heidelberg.

Linear dimensionality reduction: Survey, insights, and generalizations

© 2015 John P. Cunningham and Zoubin Ghahramani. Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, input-output relationships, and margin between data classes. Methods have been developed with a variety of names and motivations in many fields, and perhaps as a result the connections between all these methods have not been highlighted. Here we survey methods from this disparate literature as optimization programs over matrix manifolds. We discuss principal component analysis, factor analysis, linear multidimensional scaling, Fisher's linear discriminant analysis, canonical correlations analysis, maximum autocorrelation factors, slow feature analysis, sufficient dimensionality reduction, undercomplete independent component analysis, linear regression, distance metric learning, and more. This optimization framework gives insight to some rarely discussed shortcomings of well-known methods, such as the suboptimality of certain eigenvector solutions. Modern techniques for optimization over matrix manifolds enable a generic linear dimensionality reduction solver, which accepts as input data and an objective to be optimized, and returns, as output, an optimal low-dimensional projection of the data. This simple optimization framework further allows straightforward generalizations and novel variants of classical methods, which we demonstrate here by creating an orthogonal-projection canonical correlations analysis. More broadly, this survey and generic solver suggest that linear dimensionality reduction can move toward becoming a blackbox, objective-agnostic numerical technology.