Context reinstatement effects in children’s cued recall of strongly and weakly associated word pairs

Typically, asking people to reinstate the context of events increases their recall of those events; however, research findings have been mixed with children. We tested whether the principle underlying context reinstatement applies to children as it does to adults. This underlying principle, encoding specificity, suggests that the greater the overlap between study context cues and retrieval context cues, the more information that people should recall. In the current experiment, four age groups (7-year-olds, 9-year-olds, 11-year-olds and adults) took part in an encoding specificity procedure. At study, participants saw cue– target word pairs in which the cue word was either a strong or a weak associate of the target word (e.g., ice–COLD; blow–COLD). During an immediate cued recall test, participants were presented with the same strong or weak cue words and new, extra-list cue words. Overall, children and adults recalled more targets when they were presented with the same cue words at study and test, regardless of whether the cues were strong or weak. This finding suggests that encoding specificity applies to children as well as adults. We discuss the implications of these results.

A resonance tunable and durable LSPR nano-particle sensor : AL2O3 capped silver nano-disks

Localized surface plasmon resonance (LSPR) biosensors are employed to detect target biomolecules which have particular resonance wavelengths. Accordingly, tunability of the LSPR wavelength is essential in designing LSPR devices. LSPR devices employing silver nano-particles present better efficiencies than those using other noble metals such as gold; however, silver nano-particles are easily oxidized when they come in contact with liquids, which is inevitable in biosensing applications. To attain both durability and tunabilty in a LSPR biosensor, this paper proposes alumina (AL₂O₃) capped silver nano-disks. It is shown that through controlling the thickness of the cap, the LSPR resonance frequency can be finely tuned over a wide range; and moreover, the cap protects silver nano-particles from oxidation and high temperature.

Weakly monotone averaging functions

Averaging behaviour of aggregation functions depends on the fundamental property of monotonicity with respect to all arguments. Unfortunately this is a limiting property that ensures that many important averaging functions are excluded from the theoretical framework. We propose a definition for weakly monotone averaging functions to encompass the averaging aggregation functions in a framework with many commonly used non-monotonic means. Weakly monotonic averages are robust to outliers and noise, making them extremely important in practical applications. We show that several robust estimators of location are actually weakly monotone and we provide sufficient conditions for weak monotonicity of the Lehmer and Gini means and some mixture functions. In particular we show that mixture functions with Gaussian kernels, which arise frequently in image and signal processing applications, are actually weakly monotonic averages. Our concept of weak monotonicity provides a sound theoretical and practical basis for understanding both monotone and non-monotone averaging functions within the same framework. This allows us to effectively relate these previously disparate areas of research and gain a deeper understanding of averaging aggregation methods. © Springer International Publishing Switzerland 2014.

Weakly monotonic averaging functions

Monotonicity with respect to all arguments is fundamental to the definition of aggregation functions. It is also a limiting property that results in many important nonmonotonic averaging functions being excluded from the theoretical framework. This work proposes a definition for weakly monotonic averaging functions, studies some properties of this class of functions, and proves that several families of important nonmonotonic means are actually weakly monotonic averaging functions. Specifically, we provide sufficient conditions for weak monotonicity of the Lehmer mean and generalized mixture operators. We establish weak monotonicity of several robust estimators of location and conditions for weak monotonicity of a large class of penalty-based aggregation functions. These results permit a proof of the weak monotonicity of the class of spatial-tonal filters that include important members such as the bilateral filter and anisotropic diffusion. Our concept of weak monotonicity provides a sound theoretical and practical basis by which (monotonic) aggregation functions and nonmonotonic averaging functions can be related within the same framework, allowing us to bridge the gap between these previously disparate areas of research.

Estimation of factor-augmented panel regressions with weakly influential factors

The use of factor-augmented panel regressions has become very popular in recent years. Existing methods for such regressions require that the common factors are strong, such that their cumulative loadings rise proportionally to the number of cross-sectional units, which of course need not be the case in practice. Motivated by this, the current paper offers an indepth analysis of the effect of non-strong factors on two of the most popular estimators for factor-augmented regressions, namely, principal components (PC) and common correlated effects (CCE).