Computing at LHC is based on the Grid model, where geographically distributed local batch farms are federated using proper middleware. Several computing centers are considering a conversion to private clouds, capable of supporting alternative computing models along with the Grid one: CERN is pioneering such conversion. Computing tasks at LHC are mostly performed using ROOT, a framework for simulation, reconstruction and data analysis. In High Energy Physics (HEP) data are independent physics collision events. PROOF (Parallel ROOT Facility) is a cluster model built on top of ROOT, capable of processing such events interactively and in parallel. Given the increasing availability of cloud resources, running virtual PROOF clusters has become an appealing alternative. We will show how PROOF interactivity naturally fits into the cloud model: in particular by overcoming VM performance diversity via dynamic workload assignment. Several technologies are combined: the CernVM ecosystem provides the base VM image, APIs for cloud federation and a distributed filesystem for LHC software distribution, HTCondor and PROOF on Demand (PoD) control scheduling of leases and affect the lifecycle of VMs opportunistically. We will illustrate the current status of the project, and the conjoint efforts on PROOF and CernVM to create a reference implementation suiting the needs of all LHC experiments. The final product will be a personal and elastic "Analysis Facility as a Service" where the user connects using ROOT itself as standard LHC client.

We explore how to track people and furniture based on a high-resolution pressure-sensitive floor. Gravity pushes people and objects against the floor, causing them to leave imprints of pressure distributions across the surface. While the sensor is limited to sensing direct contact with the surface, we can sometimes conclude what takes place above the surface, such as users' poses or collisions with virtual objects. We demonstrate how to extend the range of this approach by sensing through passive furniture that propagates pressure to the floor. To explore our approach, we have created an 8 m2 back-projected floor prototype, termed GravitySpace, a set of passive touch-sensitive furniture, as well as algorithms for identifying users, furniture, and poses. Pressure-based sensing on the floor offers four potential benefits over camera-based solutions: (1) it provides consistent coverage of rooms wall-to-wall, (2) is less susceptible to occlusion between users, (3) allows for the use of simpler recognition algorithms, and (4) intrudes less on users' privacy.

Tabletop applications cannot display more than a few dozen on-screen objects. The reason is their limited size: tables cannot become larger than arm's length without giving up direct touch. We propose creating direct touch surfaces that are orders of magnitude larger. We approach this challenge by integrating high-resolution multitouch input into a back-projected floor. As the same time, we maintain the purpose and interaction concepts of tabletop computers, namely direct manipulation.