Linking Heuristic-Systematic Processing to Adoption of Behavior

This study sets out to draw connections among key components within three conceptual models: the Risk Information Seeking and Processing model, the Heuristic-Systematic Model, and the Theory of Planned Behavior. Specifically, it proposes and tests the theoretical linkages among heuristic and systematic information processing, depth of processing, attitude stability, and behavioral intention. Archival data drawn from a panel survey that concerns health risks from drinking municipal tap water are used for theory testing. Findings reveal that systematic processing is positively related to number of strongly held behavioral beliefs, strength of belief outcome evaluations, and strength of cognitive structure--all indicated depth of processing, and that heuristic processing is negatively related to all three measures. Cognitive structure and attitude toward the behavior appear to be consistent in direction and strength. Attitude toward the behavior, subjective norms, and alternative behavior are positively related to behavioral intention. An anticipated positive relationship between perceived behavioral control and behavioral intention was not found. Finally, theoretical and practical implications of the findings are discussed

Regulation of muscle fibre phenotype, muscle mass and igf-1 gene expression in skeletal muscle in response to mechanical activity

This thesis is concerned with the regulation of muscle mass, muscle fibre phenotype and the role of IGF-1 expression in skeletal muscle in response to mechanical activity. Initially this work was done by Northern hybridization and then by in situ hybridization. The latter indicated that the end of normal adult fibres is the region of the longitudinal growth and that IGF-1 is involved in this process. By combining in situ hybridization and immunohistochemistry procedures, the effects of passive stretch and disuse of muscle on the expression of IGF-1 mRNA at the individual muscle fibre level and the fibre type composition of the muscles were studied. The result indicated that stretch also induced an increase in the percentage of fibres expressing neonatal and slow myosin and that IGF-1 is involved not only in muscle hypertrophy, but also in muscle fibre conversion. Using RT-PCR a single IGF-1 isoform cDNA (IGF- 1Ea) could be cloned from the normal resting muscles. However, an additional isoform of IGF-1 (IGF-1Eb) was found to be expressed in stretched muscle undergoing hypertrophy. The latter IGF-1 mRNA probably encodes the precursor IGF-1 isoform that is responsible for local muscle growth regulation in response to mechanical signals. To confirm that alternative splicing of the IGF-1 gene occurs in muscle in response to physical activity, oligonucleotide primers were made which specially amplify the cDNAs of two isoforms (IGF-1 Ea and Eb) in the human as well as the rabbit. Following altered physical activity for 2 hours to 6 days, appreciable levels of IGF-1 Eb (in human the Ec) isoform were detected in skeletal muscle by using RT-PCR and RNA protection. These data suggest that the IGF-1 Eb be a link of mechanical activity and the expression of muscle genes in adaptive hypertrophy and repair processes

Generation-Z Enters the Advertising Workplace: Expectations Through a Gendered Lens

Generation-Z (Gen-Z) is entering the workforce with differing personal and professional expectations from previous generations. Further, those expectations tend to vary by gender. At the same time, workplace environments, and the social structures that underpin the workplace, are slow to change. Advertising is no exception. As educators, we are just beginning our encounter with Gen-Z and their differing habits and expectations. Further, while these young women and men share many common experiences and expectations, their expectations are also influenced by their gendered experiences. Social capital theory helps us make sense of the findings as we explore the gaps between the expectations of Gen-Z and realities of the advertising industry within a changing world. Previous research has largely focused on what the advertising industry expects. However, there is little research exploring what future graduates expect and even less on Gen-Z or these students’ expectations viewed through a gendered lens. This research explores the expectations of 98 Gen-Z students and suggests ways we, as advertising educators, might help them bridge the gap between expectations and the professional realities they will face

One-Shot Relational Learning for Knowledge Graphs

Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion usually require a large number of training instances for each relation. However, we observe that long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge extracted by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.Comment: EMNLP 201

Self-Tuned Deep Super Resolution

Deep learning has been successfully applied to image super resolution (SR). In this paper, we propose a deep joint super resolution (DJSR) model to exploit both external and self similarities for SR. A Stacked Denoising Convolutional Auto Encoder (SDCAE) is first pre-trained on external examples with proper data augmentations. It is then fine-tuned with multi-scale self examples from each input, where the reliability of self examples is explicitly taken into account. We also enhance the model performance by sub-model training and selection. The DJSR model is extensively evaluated and compared with state-of-the-arts, and show noticeable performance improvements both quantitatively and perceptually on a wide range of images