Turning aggression into an object of intervention: Tinkering in a crime control pilot study

Real-world experiments that test new technologies can affect policy and practice by introducing new objects of intervention through tinkering; the ad hoc work of realigning relations in the face of frictions, surprises, and disturbances that occur when introducing a technology. In a pilot study on aggression detection, tinkering moved aggression in and out of the human body. In the end, the pilot defined aggression as a set of acoustic-physical variables representing the aroused human body, alongside other signals of aggression. How aggression as an object intervention was shaped by tinkering is relevant because it involved inclusions and exclusions by the authorities who identified aggression, the methods they applied, and mandate for intervention. A focus on relations that are tinkered within a real-world experiment permits critical engagement with this format. Although the real-world experimental format is credited with producing knowledge about a technology's ‘actual’ performance, actors and events at the pilot study location were made only selectively relevant. Analyses of real-world experiments should therefore explain how experiments selectively make the world relevant, giving only particular objects of intervention a truth status

Applikationstechnische Untersuchungen im Weinbau an Pflanzenschutzgeraeten mit Einrichtungen zur Rueckfuehrung nicht angelagerter Spritzfluessigkeit

SIGLETIB Hannover: RA 4212(175) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Building Statistical Shape Spaces for {3D} Human Modeling

Statistical models of 3D human shape and pose learned from scan databases have developed into valuable tools to solve a variety of vision and graphics problems. Unfortunately, most publicly available models are of limited expressiveness as they were learned on very small databases that hardly reflect the true variety in human body shapes. In this paper, we contribute by rebuilding a widely used statistical body representation from the largest commercially available scan database, and making the resulting model available to the community (visit http://humanshape.mpi-inf.mpg.de). As preprocessing several thousand scans for learning the model is a challenge in itself, we contribute by developing robust best practice solutions for scan alignment that quantitatively lead to the best learned models. We make implementations of these preprocessing steps also publicly available. We extensively evaluate the improved accuracy and generality of our new model, and show its improved performance for human body reconstruction from sparse input data