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It is often claimed that the fact that some wrongs are public is a fact that is important to our thinking about permissible criminalization. We argue that it is not. Some say: what gives us reason to criminalise wrongs—when we have such a reason—is the fact that those wrongs are public. Others say: the fact that a wrong is public is a necessary condition of there being reason to criminalise that wrong, or of its permissible criminalization. What we should make of these statements depends on what is meant by a public wrong. If the claim that a wrong is public is simply the conclusion of a sound argument that there is reason to criminalise the wrong, or that the wrong is permissibly criminalised, the above statements are true, but trivially so. If the claim that a wrong is public is a premise in an argument that there is reason to criminalise the wrong, or that the wrong is permissibly criminalised, the above statements, we argue, are false. We conclude that it would be better, when we think about permissible criminalization, to do without the idea of a public wrong.

· As interest in outcomes continues to rise, community indicators have become a widely used tool to measure progress. While indicators provide a vehicle for understanding and addressing community issues from a holistic perspective, current efforts seem to suffer from both a notable absence of local-level data and end-user information overload, whereby the presentation of numerous and often disconnected indicators makes it difficult to draw meaningful conclusions from the analysis. · We highlight the results and our experiences with a community health needs assessment conducted through an indicator project in Michigan's Kent County. The analysis and visualization of the indicator project was based on the book Our Patchwork Nation: The Surprising Truth About the 'Real' America and the website PatchworkNation.org. Using principal components analysis, we reduced a set of 25 separate indicators developed through broad participation into a five-component solution at the census-tract level to facilitate greater understanding of health needs and disparities across the county. The result is a more informative approach to assessing community needs that is easily understandable, visually appealing, and more applicable to a broad audience. · We believe the lessons learned from our approach to community-indicator projects can help other grantmakers increase the effectiveness of dataintensive, large-scale community-indicator work.

In-network processing, involving operations such as filtering, compression and fusion, is a technique widely used in wireless sensor and ad hoc networks for reducing the communication overhead. In many tactical stream-oriented applications, especially in military scenarios, both link bandwidth and node energy are critically constrained resources. For such applications, in-network processing itself imposes non-negligible computing cost. In this work, we have developed a unified, utility-based closed-loop control framework that permits distributed convergence to both a) the optimal level of compression performed by a forwarding node on streams, and b) the best set of nodes where the operators of the stream processing graph should be deployed. We also show how the generalized model can be adapted to more realistic cases, where the in-network operator may be varied only in discrete steps, and where a fusion operation cannot be fractionally distributed across multiple nodes. Finally, we provide a real-time implementation of the protocol on an 802.11b network with a video application and show that the performance of the network is improved significantly in terms of the packet loss, node lifetime and quality of video received.

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