SUMMARY Accurate measures of contrast sensitivity are important for evaluating visual disease progression and for navigation safety. Previous measures suggested that cortical contrast sensitivity was constant across widely different luminance ranges experienced indoors and outdoors. Against this notion, here, we show that luminance range changes contrast sensitivity in both cat and human cortex, and the changes are different for dark and light stimuli. As luminance range increases, contrast sensitivity increases more within cortical pathways signaling lights than those signaling darks. Conversely, when the luminance range is constant, light-dark differences in contrast sensitivity remain relatively constant even if background luminance changes. We show that a Naka-Rushton function modified to include luminance range and light-dark polarity accurately replicates both the statistics of light-dark features in natural scenes and the cortical responses to multiple combinations of contrast and luminance. We conclude that differences in light-dark contrast increase with luminance range and are largest in bright environments.

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The present work investigated the effect of ripening stage on the chemical composition of essential oil extracted from peel of bitter orange (Citrus aurantium). Samples were collected at three harvesting periods: stage 1 (green colour, immature: The size of a lemon), stage 2 (green colour; semi-mature: The size of a bitter orange), and stage 3 (orange colour: mature). The fruits peel including flavedo (epicarp) and albedo (mesocarp) layers were peeled off carefully and discarded. limonene was the most represented compound in bitter orange oil. limonene showed the highest level at the maturity stage (93.47 %); several minor compounds including linalool, myrcene and α-terpineol reached their lowest content. The results showed that ripening stage influenced the chemical composition of essential oil. This knowledge could help establish the optimum harvest date ensuring the maximum limonene of bitter orange.

In some scenarios, like music, people often consume items in groups. However, reaching a consensus is difficult, and often compromises need to be made. Such compromises can potentially help users expand their tastes. They can also lead to outright rejection of the recommended items. One way to avoid this is to explain recommendations that are surprising, or even expected to be disliked, by an individual user. This paper presents an approach for generating explanations for groups. We propose algorithms for selecting a sequence of songs for a group to consume. These algorithms consider consensus but have different trade-offs. Next, using these algorithms we generated explanations in a layered evaluation using synthetic data. We studied the influence of these explanations in structured interviews with users (n=16) on user satisfaction. CCS CONCEPTS • Information systems → Recommender systems; • Humancentered computing → User studies; Natural language interfaces; Empirical studies in HCI; Laboratory experiments;