Many subjects such as languages, history, biology, geography, law, and medicine require the memorization of large numbers of unstructured facts and vocabulary. While there are many techniques and tools for memorizing such information, sustaining motivation remains a major barrier towards progress in these subjects. In order to help fill this gap, we brainstormed, experimented, and paper-prototyped to create Picard, a Facebook game that intertwines creative and social elements to turn rote learning into a creative, engaging experience. Picard, inspired by Pictionary, encourages players to come up with creative stories and images to help remember a particular card. Players compete and cooperate with their friends on Facebook to create the most memorable and funny mnemonics, called Picards, gaining points when they help themselves or others remember flashcards. Players use these Picards to learn and prove knowledge of the material at spaced intervals, which research has shown to be maximally effective for learning. Our game is the novel combination of an established learning methodology with player creativity and social support for learning. Picard increases player investment in the process of learning by allowing players to create and share useful visual mnemonics, while at the same time motivating and structuring their study habits through spaced-repetition quizzes.

In the Byzantine agreement problem, a set of n processors, any f of whom may be arbitrarily faulty, must reach agreement on a value proposed by one of the correct processors. It is a celebrated result that unless n > 3f, Byzantine agreement is impossible in a variety of computation and communication models. This is due to the fact that faulty processors can equivocate, that is, say different things to different processors. If this ability is mitigated, for example by assuming a global broadcast channel, then n > 2f is sufficient. With very few exceptions, the literature on Byzantine agreement has been confined to the n > 2f and n > 3f paradigms.

The memory consistency model is a fundamental system property characterizing a multiprocessor. The relative merits of strict versus relaxed memory models have been widely debated in terms of their impact on performance, hardware complexity and programmability. This paper adds a new dimension to this discussion: the impact of memory models on software reliability. By allowing some instructions to reorder, weak memory models may expand the window between critical memory operations. This can increase the chance of an undesirable thread-interleaving, thus allowing an otherwise-unlikely concurrency bug to manifest. To explore this phenomenon, we define and study a probabilistic model of shared-memory parallel programs that takes into account such reordering. We use this model to formally derive bounds on the vulnerability to concurrency bugs of different memory models. Our results show that for 2 concurrent threads, weaker memory models do indeed have a higher likelihood of allowing bugs. On the other hand, we show that as the number of parallel, buggy threads increases, the gap between the different memory models becomes proportionally insignificant, and thus the importance of using a strict memory model diminishes.

We study the properties of embeddings, multicommodity flows, and sparse cuts in minor-closed families of graphs which are also closed under 2-sums; this includes planar graphs, graphs of bounded treewidth, and constructions based on recursive edge replacement.

We describe a framework for automatically selecting a summary set of photographs from a large collection of geo-referenced photos. The summary algorithm is based on spatial patterns in photo sets, but can be expanded to support social, temporal, as well as textual-topical factors of the photo set. The summary set can be biased by the user, the content of the user's query, and the context in which the query is made. An initial evaluation on a set of geo-referenced photos shows that our algorithm performs well, producing results that are highly rated by users.