The Challenges of Place, Capacity, and Systems Change: The Story of Yes we can!

· Yes we can!, a comprehensive community initiative (CCI) funded by the W. K. Kellogg Foundation, was designed to improve educational and economic outcomes within the foundation’s hometown of Battle Creek, Mich. Since 2002, Yes we can! has supported five core strategies designed to trigger the systems changes needed to reduce educational and economic inequities in Battle Creek. · Yes we can! has achieved some important wins to date; for example, more residents are involved, more neighborhoods have stronger neighborhood associations, and more organizations are engaging residents in their decision-making processes. However, the scale of wins remains small, and the targeted systemic changes have not yet emerged. · Some common CCI design elements featured in Yes we can! may have inadvertently bounded its success: a) community building efforts targeted small-scale places, restricting the scale and scope of wins; b) demands for current work competed with building capacities for future work; and c) local partners who were implementing their individual grants struggled to maintain a focus on the larger vision and collective work

A novel system architecture for real-time low-level vision

A novel system architecture that exploits the spatial locality in memory access that is found in most low-level vision algorithms is presented. A real-time feature selection system is used to exemplify the underlying ideas, and an implementation based on commercially available Field Programmable Gate Arrays (FPGA’s) and synchronous SRAM memory devices is proposed. The peak memory access rate of a system based on this architecture is estimated at 2.88 G-Bytes/s, which represents a four to five times improvement with respect to existing reconfigurable computers

Summary Assessment Report: The Planning Phase of the Rebuilding Communities Initiative

Evaluates the planning and implementation of a multiyear community change initiative in Boston, Philadelphia, Washington, D.C., Denver, and Detroit

Opportunity-Rich Schools and Sustainable Communities: Seven Steps to Align High-Quality Education With Innovations in City and Metropolitan Planning and Development

Details challenges and steps for linking quality education and community and economic vitality, including establishing a shared vision and metrics, aligning investments for prosperity, and expanding access via transportation. Lists promising practices

End of One Way

Describes the role of three South Minneapolis community-based organizations. Demonstrates how the organizations form partnerships and share leadership with their communities. Explores a set of themes derived from each example of community engagement

Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of a point's local neighborhood that contains fine-grained structural information which turns out to be helpful towards better semantic learning. In this regard, we present two new operations to improve PointNet with a more efficient exploitation of local structures. The first one focuses on local 3D geometric structures. In analogy to a convolution kernel for images, we define a point-set kernel as a set of learnable 3D points that jointly respond to a set of neighboring data points according to their geometric affinities measured by kernel correlation, adapted from a similar technique for point cloud registration. The second one exploits local high-dimensional feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions. Experiments show that our network can efficiently capture local information and robustly achieve better performances on major datasets. Our code is available at http://www.merl.com/research/license#KCNetComment: Accepted in CVPR'18. *indicates equal contributio