Abstract. Combining the strengths of defaults and transparency information is a potentially powerful way to induce policy compliance. Despite negative theoretical predictions, a recent line of research revealed that default nudges may become more effective if people are informed why they should exhibit the targeted behavior. Yet, it is an open empirical question whether the increase in compliance came from setting a default and consequently disclosing it, or the provided information was sufficient to deliver the effect on its own. Results from an online experiment indicate that both defaulting and transparency information exert a statistically independent effect on compliance, with highest compliance rates observed in the combined condition. Practical and theoretical implications are discussed.
Due to the development of computer vision and natural language processing technologies in recent years, there has been a growing interest in multimodal intelligent tasks that require the ability to concurrently understand various forms of input data such as images and text. Vision-and-language navigation (VLN) require the alignment and grounding of multimodal input data to enable real-time perception of the task status on panoramic images and natural language instruction. This study proposes a novel deep neural network model (JMEBS), with joint multimodal embedding and backtracking search for VLN tasks. The proposed JMEBS model uses a transformer-based joint multimodal embedding module. JMEBS uses both multimodal context and temporal context. It also employs backtracking-enabled greedy local search (BGLS), a novel algorithm with a backtracking feature designed to improve the task success rate and optimize the navigation path, based on the local and global scores related to candidate actions. A novel global scoring method is also used for performance improvement by comparing the partial trajectories searched thus far with a plurality of natural language instructions. The performance of the proposed model on various operations was then experimentally demonstrated and compared with other models using the Matterport3D Simulator and room-to-room (R2R) benchmark datasets.
Educational leaders are required to respond in real time to questions, quandaries, and cases that involve individuals in different contexts. They face an array of possible choices that exist in tension. Justice and fairness must coexist with mercy and compassion; in enforcing policy, compliance must make room for flexibility in special cases. School leaders are called to adjudicate competing goods and teach others to do the same. This article examines what practical wisdom is, why it matters, and introduces a theoretically grounded Practical Wisdom Framework (PWF) to help school leaders deliberate well to create and sustain formative institutions.
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
