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This study introduces culture relatedness of words as a novel variable and explores its impact on emotional responses of English-Mandarin bilinguals living in the UK, where their second language (L2) is dominant. First, we conducted a norming study to identify emotive words related to participants' native (e.g., bamboo) and residential (e.g., scones) cultures. We then used event-related potentials (ERPs) to examine whether culture relatedness affects emotional responses to words presented in L1 and L2. We were particularly interested in investigating whether the well-established emotional distance from L2 may be due to cultural distance, and whether concepts related to one's native culture in L2 may enhance affective responses. Initial evidence from ongoing data analyses seems to suggest an interaction of culture relatedness and emotional valance on affective responses. This research offers new insights into the interplay of language, culture, and emotion in bilingual contexts, examining how cultural salience modulates emotional responses.

Airborne aircrafts are dependent on the Global Navigation Satellite System (GNSS), which is susceptible to interference due to the satellite base-station and cooperative communication. Synthetic aperture radar altimeter (SARAL) provides the ability to measure the topographic terrain for matching with Digital Elevation Model (DEM) to achieve positioning without relying on GNSS. However, due to the near-vertical coupling in the delay-Doppler map (DDM), the similarity of DDMs of adjacent apertures is high, and the probability of successful matching is low. To this end, a novel neural network of terrain matching and aircraft positioning is proposed based on the airborne SARAL imagery. The model-driven terrain matching and aircraft positioning network (TMP-Net) is capable of realizing aircraft positioning by utilizing the real-time DDMs to match with the DEM-based DDM references, which are generated by a point-by-point coupling mechanism between the airborne routine and ground terrain DEM. Specifically, the training dataset is established by a numerical simulation method based on a semi-analytical model. Therefore, DEM-based DDM references can be generated by forward deduction when only regional DEM can be obtained. In addition to the model-based DDM generation, feature extraction, and similarity measurement, an aircraft positioning module is added. Three different positioning methods are designed to achieve the aircraft positioning, where three-point weighting exhibits the best performance in terms of positioning accuracy. Due to the fact that both the weighted triplet loss and softmax loss are employed in a cooperative manner, the matching accuracy can be improved and the positioning error can be reduced. Finally, both simulated and measured airborne datasets are used to validate the effectiveness of the proposed algorithm. Quantitative and qualitative evaluations show the superiority. [ABSTRACT FROM AUTHOR]

The burgeoning digital economy has also aroused wide public concerns over its improper use of personal data for economic and political profits. This study focuses on the milestone Facebook-Cambridge Analytica scandal and examines how Mark Zuckerberg succeeded in avoiding public blame during two US Congressional hearings. An integrated analytic framework has been established by combining blame theory and critical discourse analysis to examine blame-avoiding strategies used by Mark Zuckerberg during the two Congressional hearings. The findings have revealed not only the topics but also the specific strategies and the linguistic means and realizations for these strategies. It is expected that this study can generate significant implications on blame-avoiding strategies by digital corporations for their inherently flawed business models. [ABSTRACT FROM AUTHOR]

Due to the lack of accurate labels for the airborne synthetic aperture radar altimeter (SARAL), the use of deep learning methods is limited for estimating the above ground level (AGL) of complicated landforms. In addition, the inherent additive and speckle noise definitely influences the intended delay/Doppler map (DDM); accurate AGL estimation becomes more challenging when using the feature extraction approach. In this paper, a generalized AGL estimation algorithm is proposed, based on a fully supervised altimetry enhancement network (FuSAE-net), where accurate labels are generated by a novel semi-analytical model. In such a case, there is no need to have a fully analytical DDM model, and accurate labels are achieved without additive noises and speckles. Therefore, deep learning supervision is easy and accurate. Next, to further decrease the computational complexity for various landforms on the airborne platform, the network architecture is designed in a lightweight manner. Knowledge distillation has proven to be an effective and intuitive lightweight paradigm. To significantly improve the performance of the compact student network, both the encoder and decoder of the teacher network are utilized during knowledge distillation under the supervision of labels. In the experiments, airborne raw radar altimeter data were applied to examine the performance of the proposed algorithm. Comparisons with conventional methods in terms of both qualitative and quantitative aspects demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]