Grounding the Lexical Semantics of Verbs in Visual Perception using Force Dynamics and Event Logic

This paper presents an implemented system for recognizing the occurrence of events described by simple spatial-motion verbs in short image sequences. The semantics of these verbs is specified with event-logic expressions that describe changes in the state of force-dynamic relations between the participants of the event. An efficient finite representation is introduced for the infinite sets of intervals that occur when describing liquid and semi-liquid events. Additionally, an efficient procedure using this representation is presented for inferring occurrences of compound events, described with event-logic expressions, from occurrences of primitive events. Using force dynamics and event logic to specify the lexical semantics of events allows the system to be more robust than prior systems based on motion profile

Promoting Strategies to Overcome Low Health Literacy and Improve Patient Understanding in Outpatient Setting

Over 36% of US adults have low health literacy. This contributes to poorer health outcomes and increased costs for individuals and health care systems. Many strategies can be used to overcome the barrier of low health literacy and improve patient understanding in clinical encounters. As health care providers have been shown to underestimate patient\u27s needs for information and overestimate their own ability to communicate effectively with patients, these strategies should be used universally. We prepared a presentation on health literacy, its epidemiology, risk factors and implications, and strategies to overcome low health literacy and improve patient understanding. We focused most heavily on Teach-Back, a strategy to assess patient understanding. We presented this to a group of residents and attendings at EMMC Center for Family Medicine and Residency. We prepared pre-presentation and post-presentation surveys to evaluate effect of presentation.https://scholarworks.uvm.edu/fmclerk/1250/thumbnail.jp

Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video

We develop, analyze, and evaluate a novel, supervised, specific-to-general learner for a simple temporal logic and use the resulting algorithm to learn visual event definitions from video sequences. First, we introduce a simple, propositional, temporal, event-description language called AMA that is sufficiently expressive to represent many events yet sufficiently restrictive to support learning. We then give algorithms, along with lower and upper complexity bounds, for the subsumption and generalization problems for AMA formulas. We present a positive-examples--only specific-to-general learning method based on these algorithms. We also present a polynomial-time--computable ``syntactic'' subsumption test that implies semantic subsumption without being equivalent to it. A generalization algorithm based on syntactic subsumption can be used in place of semantic generalization to improve the asymptotic complexity of the resulting learning algorithm. Finally, we apply this algorithm to the task of learning relational event definitions from video and show that it yields definitions that are competitive with hand-coded ones

A low-altitude satellite interaction study

Two computer programs calculate interaction effects of high speed spacecraft on the environment at altitudes from 90 km to 150 km. EXT program determines fluid field in bodies of arbitrary geometries in transient flow regime. INT program uses EXT output and measures flow conditions inside spacecraft body

A low-altitude satellite interaction study /Neutral gases/ Final report

Low-altitude satellite interaction study of neutral gases and Monte Carlo computer techniques for describing flow field and spacecraft interaction

Putting the Automatic Back into AD: Part I, What’s Wrong (CVS: 1.1)

Current implementations of automatic differentiation are far from automatic. We survey the difficulties encountered when applying four existing AD systems, ADIFOR, TAPENADE, ADIC, and FADBAD++, to two simple tasks, minimax optimization and control of a simulated physical system, that involve taking derivatives of functions that themselves take derivatives of other functions. ADIC is not able to perform these tasks as it cannot transform its own generated code. Using FADBAD++, one cannot compute derivatives of different orders with unmodified code, as needed by these tasks. One must either manually duplicate code for the different derivative orders or write the code using templates to automate such code duplication. ADIFOR and TAPENADE are both able to perform these tasks only with significant intervention: modification of source code and manual editing of generated code. A companion paper presents a new AD system that handles both tasks without any manual intervention yet performs as well as or better than these existing systems