When looking at drawings of graphs, questions about graph density, community structures, local clustering and other graph properties may be of critical importance for analysis. While graph layout algorithms have focused on minimizing edge crossing, symmetry, and other such layout properties, there is not much known about how these algorithms relate to a user's ability to perceive graph properties for a given graph layout. In this study, we apply previously established methodologies for perceptual analysis to identify which graph drawing layout will help the user best perceive a particular graph property. We conduct a large scale (n = 588) crowdsourced experiment to investigate whether the perception of two graph properties (graph density and average local clustering coefficient) can be modeled using Weber's law. We study three graph layout algorithms from three representative classes (Force Directed -FD, Circular, and Multi-Dimensional Scaling -MDS), and the results of this experiment establish the precision of judgment for these graph layouts and properties. Our findings demonstrate that the perception of graph density can be modeled with Weber's law. Furthermore, the perception of the average clustering coefficient can be modeled as an inverse of Weber's law, and the MDS layout showed a significantly different precision of judgment than the FD layout.
ObjectivesThe study aimed to review the prescribing knowledge of first-year postgraduate doctors in a medical college in India, using the principles of good prescribing, to suggest strategies to improve rational prescribing, and to recommend what curriculum planners can do to accomplish this objective.MethodsFifty first-year postgraduate doctors were asked to fill in a structured questionnaire that sought information regarding their undergraduate training in clinical pharmacology and therapeutics, prescribing habits, and commonly consulted drug information sources. Also, the questionnaire assessed any perceived deficiencies in their undergraduate clinical pharmacology teaching and sought feedback regarding improvement in the teaching.ResultsEighty-eight percent of residents said that they were taught prescription writing in undergraduate pharmacology teaching; 48% of residents rated their prescribing knowledge at graduation as average, 28% good, 4% excellent, 14% poor, and 4% very poor; 58% felt that their undergraduate training did not prepare them to prescribe safely, and 62% felt that their training did not prepare them to prescribe rationally. Fifty-eight percent of residents felt that they had some specific problems with writing a prescription during their internship training, while 92% thought that undergraduate teaching should be improved. Their suggestions for improving teaching methods were recorded.ConclusionsThis study concludes that efforts are needed to develop a curriculum that encompasses important aspects of clinical pharmacology and therapeutics along with incorporation of the useful suggestions given by the residents.
As more and more complex AI systems are introduced into our day-to-day lives, it becomes important that everyday users can work and interact with such systems with relative ease. Orchestrating such interactions require the system to be capable of providing explanations and rationale for its decisions and be able to field queries about alternative decisions. A significant hurdle to allowing for such explanatory dialogue could be the mismatch between the complex representations that the systems use to reason about the task and the terms in which the user may be viewing the task. This paper introduces methods that can be leveraged to provide contrastive explanations in terms of user-specified concepts for deterministic sequential decision-making settings where the system dynamics may be best represented in terms of black box simulators. We do this by assuming that system dynamics can at least be partly captured in terms of symbolic planning models, and we provide explanations in terms of these models. We implement this method using a simulator for a popular Atari game (Montezuma's Revenge) and perform user studies to verify whether people would find explanations generated in this form useful.
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