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A cloud screening unit on a satellite platform for Earth observation can play an important role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art.

The Linguistic Inquiry and Word Count (LIWC) is a popular closed-vocabulary text analysis software program that is used to understand whether individuals' use of linguistic categories (i.e., word categories, such as negative affect) depends on their personality traits. Here, we present the first meta-analysis of the relations between the Big Five personality traits and 52 linguistic categories of the English language. Across 31 eligible samples (n = 85,724), the results showed that (a) self-reported personality traits are significantly correlated with linguistic categories, but the effect sizes are relatively small (the strongest effect sizes between the Big Five and linguistic categories ranged from |ρ| = .08 to .14, and the 52 LIWC categories explained on average 5.1% of personality variance); (b) observerreported personality traits are significantly correlated with linguistic categories, with the effect sizes being small-to-medium (|ρ| = .18-.39, explaining 38.5% of personality variance); (c) 20 linguistic categories (out of 260; 5 Personality Traits × 52 LIWC Categories) correlated both with self-and observer-reported personality traits (the "kernel of truth" in linguistic markers of personality); and (d) 10 study, sample, and task characteristics significantly moderated the correlations of the linguistic categories with personality traits, showing that the effect sizes were mainly stronger for longer texts and older LIWC versions, among others. Public Significance StatementThis meta-analysis identifies the linguistic categories (i.e., word categories, such as negative affect) that individuals use depending on their personality traits, as well as the linguistic categories that other people use to draw personality inferences. Individuals indeed use specific linguistic categories depending on their personality traits and others use specific linguistic categories to draw personality inferences, but those relations are dependent on study and tasks characteristics (e.g., text length, Linguistic Inquiry and Word Count version).

Summary: Here we report on the expansion of a previously introduced biointerface platform based on thin spin‐coated films of polystyrene‐block‐poly(tert‐butyl acrylate) (PS‐b‐PtBA). Following the selective deprotection of the tert‐butyl‐ester groups in the PtBA skin layer by hydrolysis under acidic conditions, the activation with N‐hydroxysuccinimide and the subsequent derivatization with α,ω‐biotin‐amine end‐functionalized poly(ethylene glycol) (PEG), strepavidin was immobilized as anchor layer for specific immobilization of biotin‐tagged molecules. Based on contact angle, Fourier transform infrared (FTIR) spectroscopy, atomic force microscopy (AFM) and confocal fluorescence microscopy data it was shown that the polymer films were efficiently modified. The complexation of a biotin‐tagged dye, as well as the non‐specific adsorption of proteins was determined by confocal fluorescence microscopy measurements. The surface coverage of the immobilized dye as a function of dye concentration in solution was found to be consistent with a high affinity‐type adsorption isotherm, while the protein resistant properties were found to be similar to non‐biotinylated PEG. The scope of micro‐ and nanostructured PS‐b‐PtBA biointerfaces is thereby considerably expanded to encompass the specific immobilization of biotin‐tagged biomolecules.

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