Leveraging human grasping skills to teach a robot to perform a manipulation task is appealing, but there are several limitations to this approach: time-inefficient data capture procedures, limited generalization of the data to other grasps and objects, and inability to use that data to learn more about how humans perform and evaluate grasps. This paper presents a data capture protocol that partially addresses these deficiencies by asking participants to specify ranges over which a grasp is valid. The protocol is verified both qualitatively through online survey questions (where within-range grasps are identified correctly with the nearest extreme grasp) and quantitatively by showing that there is small variation in grasps ranges from different participants as measured by joint angles and position. We demonstrate that these grasp ranges are valid through testing on a physical robot (93.75% of grasps interpolated from grasp ranges are successful).

We must properly model attacks and the preferences of the electorate for the computational study of attacks on elections to give us insight into the hardness of attacks in practice. Theoretical and empirical analysis are equally important methods to understand election attacks. I discuss my recent work on domain restrictions on partial preferences and on new election attacks. I propose further study into modeling realistic election attacks and the advancement of the current state of empirical analysis of their hardness by using more advanced statistical techniques.

Mesoscale ocean eddies are a critical component of the Earth System as they dominate the ocean's kinetic energy and impact the global distribution of oceanic heat, salinity, momentum, and nutrients. Therefore, accurately representing these dynamic features is critical for our planet's sustainability. The majority of methods that identify eddies from satellite observations analyze the data in a frame-by-frame basis despite the fact that eddies are dynamic objects that propagate across space and time. We introduce the notion of spatio-temporal consistency to identify eddies in a continuous spatio-temporal field, to simultaneously ensure that the features detected are both spatially and temporally consistent. Our spatio-temporal consistency approach allows us to remove most of the expert criteria used in traditional methods to reduce false negatives. The removal of arbitrary heuristics enables us to render more complete eddy dynamics by identifying smaller and longer lived eddies compared to existing methods.

Scoring systems are an extremely important class of election systems. A length-m (so-called) scoring vector applies only to m-candidate elections. To handle general elections, one must use a family of vectors, one per length. The most elegant approach to making sure such families are "family-like'' is the recently introduced notion of (polynomial-time uniform) pure scoring rules, where each scoring vector is obtained from its precursor by adding one new coefficient. We obtain the first dichotomy theorem for pure scoring rules for a control problem. In particular, for constructive control by adding voters (CCAV), we show that CCAV is solvable in polynomial time for k-approval with k<=3, k-veto with k<=2, every pure scoring rule in which only the two top-rated candidates gain nonzero scores, and a particular rule that is a "hybrid" of 1-approval and 1-veto. For all other pure scoring rules, CCAV is NP-complete. We also investigate the descriptive richness of different models for defining pure scoring rules, proving how more rule-generation time gives more rules, proving that rationals give more rules than do the natural numbers, and proving that some restrictions previously thought to be "w.l.o.g." in fact do lose generality.

Robotics is becoming more integrated into society and small user-friendly robots are becoming more common in office spaces and homes. This increases the importance of trust in human-robot interaction, which is essential to understand in order to design systems that foster appropriate levels of trust. Too much or not enough trust in a robotic system can lead to inefficiencies, risks, and other damages. The robot in this experiment was used as a navigational system to guide a participant through an arrow maze. This experiment examined human trust in robots, the decision between doing a task or relying on a robot, and inconsistencies between human awareness and robot guidance.

Probabilistic models were fit to logs of player actions in the card game Dominion in an attempt to find evidence of personality types that could be used to classify player behavior as well as generate probabilistic bot behavior. Expectation Maximization seeded with players' self-assessments for their motivations was run for two different model types — Naive Bayes and a trigram model — to uncover three clusters each. For both model structures, most players were classified as belonging to a single large cluster that combined the goals of splashy plays, clever combos, and effective play, cross-cutting the original categories — a cautionary tale for research that assumes players can be classified into one category or another. However, subjects qualitatively report that the different model structures play very differently, with the Naive Bayes model more creatively combining cards.

We present a dual decomposition approach to the tree-reweighted belief propagation objective. Each tree in the tree-reweighted bound yields one subproblem, which can be solved with the sum-product algorithm. The master problem is a simple differentiable optimization, to which a standard optimization method can be applied. Experimental results on 10x10 Ising models show the dual decomposition approach using L-BFGS is similar in settings where message-passing converges quickly, and one to two orders of magnitude faster in settings where message-passing requires many iterations, specifically high accuracy convergence, and strong interactions.

The results of 2,256 neuroimaging experiments were an- alyzed using singular value decomposition (SVD) and non-negative matrix factorization (NMF) to extract pat- terns in the data. To evaluate the techniques’ efficacy at capturing regularities in the data, one positive and one negative result from each of 100 random experi- ments were treated as missing, and the values were it- eratively reconstructed using each technique for dimen- sionality reduction. Under the best conditions, preci- sion and recall of roughly 78% was achieved for each method. Weighting the domain matrix and area matrix to have equal first eigenvalues before combining them, a technique known as blending, significantly improved re- sults for both methods. While using unnormalized data appeared to produce a peak in results for 10-15 dimen- sions, normalizing to take into account variation in the popularity of experiment types removed the effect. The basis vectors produced by each method do not support the idea that current cognitive ontologies map well to individual brain areas.

Games are an integral part of the human experience. Starting in our childhood and continuing throughout our lives they teach us about the world through the concepts of rules, strategies, and outcomes. They help prepare us for our future, provide entertainment, bring us together socially, and give us characters to root for -- making ordinary people heroes for a moment. Digital games build on centuries of play and interaction bringing to the modern age a unique and creative form. Fully integrated into modern life, the video game industry now rivals that of the motion picture and music industries and their products are fully integrated into our digital lifestyles. Computers with advanced graphics capabilities have contributed to the immersive interactive experience that attracts many to spend as much of their leisure time playing video games as watching television or listening to music.

Fast mapping is a phenomenon by which children learn the meanings of novel adjectives after a very small number of exposures when the new word is contrasted with a known word. The present study was a preliminary test of whether machine learners could use such contrasts in unconstrained speech to learn adjective meanings and categories. Six decision tree-based learning methods were evaluated that use contrasting examples in order to work toward an adjective fast-mapping system for machine learners. Subjects tended to compare objects using adjectives of the same category, implying that such contrasts may be a useful source of data about adjective meaning, though none of the learning algorithms showed strong advantages over any other.