Moving Arts Leadership Forward: A Changing Landscape

Since 2009, the William and Flora Hewlett Foundation's Performing Arts Program has been making grants to help emerging arts leaders develop satisfying and successful careers through the Next Generation Arts Leadership Initiative. The first phase of that work, which ended in 2015, was funded in partnership with the James Irvine Foundation. It focused on training and retaining emerging arts leaders -- defined as eighteen to thirty-five-year-olds with ten years or less of arts experience -- in anticipation of a widely predicted wave of retirements. The Initiative made grants totaling $1.9 million to five leadership networks across California, and to statewide regranting programs, managed by the Center for Cultural Innovation to support professional development for individuals and innovative organizational practices. While an assessment conducted in 2011 showed that the Initiative was successful in achieving its early goals of building infrastructure and opportunities for younger arts leaders, the Performing Arts Program and our partners continued to grapple with a few persistent questions: what were we preparing up-and-coming leaders to do? To what degree did we aim to sustain the field as it exists or spur its transformation? Were we adequately preparing leaders for the challenges to come? To help answer these questions, in late 2014 we commissioned Michael Courville of Open Mind Consulting to reassess the arts leadership landscape in California and explore opportunities for future investments in arts leadership.The research was conducted in collaboration with a cross-section of local, regional, and national arts leaders, and with the Initiative's partners. It reveals that the arts landscape is in a state of flux and that there is a timely opportunity to reimagine how the nonprofit arts field defines and practices leadership

Learning Temporal Transformations From Time-Lapse Videos

Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in the future. But, what about computers? In this paper, we learn computational models of object transformations from time-lapse videos. In particular, we explore the use of generative models to create depictions of objects at future times. These models explore several different prediction tasks: generating a future state given a single depiction of an object, generating a future state given two depictions of an object at different times, and generating future states recursively in a recurrent framework. We provide both qualitative and quantitative evaluations of the generated results, and also conduct a human evaluation to compare variations of our models.Comment: ECCV201

A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion

Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a probabilistic suggestion model that is able to account for sequences of previous queries of arbitrary lengths. Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that it outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our model is general enough to be used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management (CIKM) 201