A transfer paradigm was used to investigate the relationship between picture naming and translation English-Spanish bilniguals first named pictures and subsequently translated words in both their first (LI) and second (L2) languages Some words in the translation task were repetitions of concepts that had previously been named as pictures Whereas picture naming produced reliable transfer to translation from L1 to L2, it produced no transfer to translation from L2 to L1 The results support the claim that connections in bilingual memory are asymmetric Translation is conceptually mediated from L1 to L2 but lexically mediated from L2 to L1

This paper examines the effectiveness of the Penn Resiliency Program (PRP) in an urban Indian setting. The PRP is a program to prevent depression in early adolescence and has proved successful in changing children’s attributional style of life events. While the program has been successful in preventing symptoms of depression in Western populations, the current study explored whether this program could be effective with an Indian sample. The aim of the current study was twofold; first, to study the attributional style of early adolescents in India and identify negative effects (if any) and second, to gain insights in using the PRP as a tool to change explanatory styles in Indian children. A total of 58 children participated in the study (Intervention group n = 29 and Control group n = 29). An Analysis of Covariance comparing post-test scores on Children’s Attributional Style Questionnaire (CASQ) while controlling for baseline scores indicated that children in the intervention group exhibited a significant reduction in pessimistic explanatory style and an increase in optimistic orientation compared to children in the control group. This indicates that the program was effective in changing negative attribution styles among upper-class Indian school children. Future work may look into the longer impact of the program as well as further considerations into adapting the program for a middle class population.

As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, and better tools for flood risk communication could increase the support for flood-resilient infrastructure development. Our work aims to enable more visual communication of large-scale climate impacts via visualizing the output of coastal flood models as satellite imagery. We propose the first deep learning pipeline to ensure physical-consistency in synthetic visual satellite imagery. We advanced a state-of-the-art GAN called pix2pixHD, such that it produces imagery that is physically-consistent with the output of an expert-validated storm surge model (NOAA SLOSH). By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism. We envision our work to be the first step towards a global visualization of how climate change shapes our landscape. Continuing on this path, we show that the proposed pipeline generalizes to visualize arctic sea ice melt. We also publish a dataset of over 25k labelled image-pairs to study image-to-image translation in Earth observation 1 .

Recent advances in technology for hyper-realistic visual effects provoke the concern that deepfake videos of political speeches will soon be visually indistinguishable from authentic video recordings. Yet there exists little empirical research on how audio-visual information influences people's susceptibility to fall for political misinformation. The conventional wisdom in the field of communication research predicts that people will fall for fake news more often when the same version of a story is presented as a video as opposed to text. However, audio-visual manipulations often leave distortions that some but not all people may pick up on. Here, we evaluate how communication modalities influence people's ability to discern real political speeches from fabrications based on a randomized experiment with 5,727 participants who provide 61,792 truth discernment judgments. We show participants soundbites from political speeches that are randomly assigned to appear using permutations of text, audio, and video modalities. We find that communication modalities mediate discernment accuracy: participants are more accurate on video with audio than silent video, and more accurate on silent video than text transcripts. Likewise, we find participants rely more on how something is said -the audio-visual cues -rather than what is said -the speech content itself. However, political speeches that do not match public perceptions of politicians' beliefs reduce participants' reliance on visual cues. In particular, we find that reflective reasoning moderates the degree to which participants consider visual information: low performance on the Cognitive Reflection Test is associated with an underreliance on visual cues and an overreliance on what is said.

We present Wav2Lip-Emotion, a video-to-video translation architecture that modifies facial expressions of emotion in videos of speakers. Previous work modifies emotion in images, uses a single image to produce a video with animated emotion, or puppets facial expressions in videos with landmarks from a reference video. However, many use cases such as modifying an actor's performance in post-production, coaching individuals to be more animated speakers, or touching up emotion in a teleconference require a video-to-video translation approach. We explore a method to maintain speakers' identity and pose while translating their expressed emotion. Our approach extends an existing multi-modal lip synchronization architecture to modify the speaker's emotion using L1 reconstruction and pre-trained emotion objectives. We also propose a novel automated emotion evaluation approach and corroborate it with a user study. These find that we succeed in modifying emotion while maintaining lip synchronization. Visual quality is somewhat diminished, with a trade off between greater emotion modification and visual quality between model variants. Nevertheless, we demonstrate (1) that facial expressions of emotion can be modified with nothing other than L1 reconstruction and pre-trained emotion objectives and (2) that our automated emotion evaluation approach aligns with human judgements.