Abstract. Three-dimensional (3D) shape models are powerful because they enable the inference of object shape from incomplete, noisy, or ambiguous 2D or 3D data. For example, realistic parameterized 3D human body models have been used to infer the shape and pose of people from images. To train such models, a corpus of 3D body scans is typically brought into registration by aligning a common 3D human-shaped template to each scan. This is an ill-posed problem that typically involves solving an optimization problem with regularization terms that penalize implausible deformations of the template. When aligning a corpus, however, we can do better than generic regularization. If we have a model of how the template can deform then alignments can be regularized by this model. Constructing a model of deformations, however, requires having a corpus that is already registered. We address this chicken-and-egg problem by approaching modeling and registration together. By minimizing a single objective function, we reliably obtain high quality registration of noisy, incomplete, laser scans, while simultaneously learning a highly realistic articulated body model. The model greatly improves robustness to noise and missing data. Since the model explains a corpus of body scans, it captures how body shape varies across people and poses.

Peer-to-peer systems have been proposed for a wide variety of applications, including file-sharing, web caching, distributed computation, cooperative backup, and onion routing. An important motivation for such systems is self-scaling. That is, increased participation increases the capacity of the system. Unfortunately, this property is at risk from selfish participants. The decentralized nature of peer-to-peer systems makes accounting difficult. We show that e-cash can be a practical solution to the desire for accountability in peerto-peer systems while maintaining their ability to self-scale. No less important, e-cash is a natural fit for peer-to-peer systems that attempt to provide (or preserve) privacy for their participants. We show that e-cash can be used to provide accountability without compromising the existing privacy goals of a peer-to-peer system.We show how e-cash can be practically applied to a file sharing application. Our approach includes a set of novel cryptographic protocols that mitigate the computational and communication costs of anonymous e-cash transactions, and system design choices that further reduce overhead and distribute load. We conclude that provably secure, anonymous, and scalable peer-to-peer systems are within reach.

We introduce radial encoding of nanowires (NWs), a new method of differentiating and controlling NWs by a small set of mesoscale wires for use in crossbar memories. We describe methods of controlling these NWs and give efficient manufacturing algorithms. These new encoding and decoding methods do not suffer from the misalignment characteristic of flow-aligned NWs. They achieve comparable effective pitch and resulting memory density with axially encoded NWs while avoiding potential cases of address ambiguity and simplifying NW preparation. We also explore hybrid axial/radial encodings and show that they offer no net benefit over pure codes.

Abstract