Perspectives on Cultivating a Positive Collegiate Clarinet Studio Environment: A Survey of Students and Professors

Data was analyzed from a survey of collegiate clarinet students and professors concerning student and faculty preferences and perceptions concerning the cultivation of a positive collegiate clarinet studio environment. Over two hundred respondents indicated preferences for the structure of individual lessons and studio class. The data indicated it is essential that the professor adapt their teaching to individual students during lesson instruction. Goals should be recorded, a verbal agreement alone is insufficient. Contact information for all studio colleagues should be available, and the professor should be accessible should the need arise. Large ensemble concert attendance should be encouraged, and recital attendance prioritized for both studio colleagues and the professor. Students should engage in informal bonding activities throughout the academic year, including events such as studio parties. Collaborations with peers in chamber ensembles should be encouraged. Student feedback in studio class should be spoken directly, and the class should be instructed at the beginning of each semester on appropriate ways to give feedback. Finally, the professor should take an active role in building a supportive studio community and addressing conflict within the studio

Blue noise through optimal transport

We present a fast, scalable algorithm to generate high-quality blue noise point distributions of arbitrary density functions. At its core is a novel formulation of the recently-introduced concept of capacity-constrained Voronoi tessellation as an optimal transport problem. This insight leads to a continuous formulation able to enforce the capacity constraints exactly, unlike previous work. We exploit the variational nature of this formulation to design an efficient optimization technique of point distributions via constrained minimization in the space of power diagrams. Our mathematical, algorithmic, and practical contributions lead to high-quality blue noise point sets with improved spectral and spatial properties

Analysis of Corporate Carbon Dioxide Footprints and Pledges

Carbon emissions are rapidly posing a more urgent threat to the planet. This is an issue that cannot be tackled by one group alone. Several companies across different industrial sectors are taking steps to practice corporate sustainability in regards to their direct and supply chain carbon emissions through reduction, offsets, and removal. This paper explores the steps taken by four role model companies, along with multiple progressive companies within four chosen sectors. The breakdown of their carbon footprints was analyzed and their different strategies were compared and contrasted. These companies have created individual goals for environmental sustainability; some have had success or show promise, prompting others to follow, while some are unrealistic or unsupported by true action

Blue Noise through Optimal Transport

International audienceWe present a fast, scalable algorithm to generate high-quality blue noise point distributions of arbitrary density functions. At its core is a novel formulation of the recently-introduced concept of capacity-constrained Voronoi tessellation as an optimal transport problem. This insight leads to a continuous formulation able to enforce the capacity constraints exactly, unlike previous work. We exploit the variational nature of this formulation to design an efficient optimization technique of point distributions via constrained minimization in the space of power diagrams. Our mathematical, algorithmic, and practical contributions lead to high-quality blue noise point sets with improved spectral and spatial properties

Predicting image influence on visual saliency distribution: the focal and ambient dichotomy

International audienceThe computational modelling of visual attention relies entirely on visual fixations that are collected during eye-tracking experiments. Although all fixations are assumed to follow the same attention paradigm, some studies suggest the existence of two visual processing modes, called ambient and focal. In this paper, we present the high discrepancy between focal and ambient saliency maps and propose an automatic method for inferring the degree of focalness of an image. This method opens new avenues for the computational modelling of saliency models and their benchmarking