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

EDUCATIONAL PROGRAMS TO ADDRESS THE ECONOMIC ADJUSTMENTS FACING TOBACCO FARMERS AND RURAL COMMUNITIES

This paper discusses the context within which educational programs tailored to tobacco producers and related rural communities have developed. Discussion is expanded by examining current program approaches employed by various organizations. Many of these organizations have a manual stake in helping producers in tobacco communities develop their management capacity. A range of initiatives aimed at facilitating economic adjustment is compared, including the major issues addressed and expected outcomes. Many of the initiatives have made useful contributions; however, gaps and limitations remain. These are considered as future educational efforts and issues are discussed.educational programs, tobacco producers, Community/Rural/Urban Development,

Clinical identification of feeding and swallowing disorders in 0-6 month old infants with Down syndrome

Feeding and swallowing disorders have been described in children with a variety of neurodevelopmental disabilities, including Down syndrome (DS). Abnormal feeding and swallowing can be associated with serious sequelae such as failure to thrive and respiratory complications, including aspiration pneumonia. Incidence of dysphagia in young infants with DS has not previously been reported. To assess the identification and incidence of feeding and swallowing problems in young infants with DS, a retrospective chart review of 174 infants, ages 0-6 months was conducted at a single specialty clinic. Fifty-seven percent (100/174) of infants had clinical concerns for feeding and swallowing disorders that warranted referral for Videofluroscopic Swallow Study (VFSS); 96/174 (55%) had some degree of oral and/or pharyngeal phase dysphagia and 69/174 (39%) had dysphagia severe enough to warrant recommendation for alteration of breast milk/formula consistency or nonoral feeds. Infants with certain comorbidities had significant risk for significant dysphagia, including those with functional airway/respiratory abnormalities (OR = 7.2). Infants with desaturation with feeds were at dramatically increased risk (OR = 15.8). All young infants with DS should be screened clinically for feeding and swallowing concerns. If concerns are identified, consideration should be given to further evaluation with VFSS for identification of dysphagia and additional feeding modifications

Study of the Germination Dynamics for Plants using Advanced Image Processing Algorithms

Study of the Germination Dynamics for Plants using Advanced Image Processing Algorithm

Extreme State Aggregation Beyond MDPs

We consider a Reinforcement Learning setup where an agent interacts with an environment in observation-reward-action cycles without any (esp.\ MDP) assumptions on the environment. State aggregation and more generally feature reinforcement learning is concerned with mapping histories/raw-states to reduced/aggregated states. The idea behind both is that the resulting reduced process (approximately) forms a small stationary finite-state MDP, which can then be efficiently solved or learnt. We considerably generalize existing aggregation results by showing that even if the reduced process is not an MDP, the (q-)value functions and (optimal) policies of an associated MDP with same state-space size solve the original problem, as long as the solution can approximately be represented as a function of the reduced states. This implies an upper bound on the required state space size that holds uniformly for all RL problems. It may also explain why RL algorithms designed for MDPs sometimes perform well beyond MDPs.Comment: 28 LaTeX pages. 8 Theorem

Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes

Interval Markov decision processes (IMDPs) generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that prevents the knowledge of the exact transition probabilities. In this paper, we consider the problem of multi-objective robust strategy synthesis for interval MDPs, where the aim is to find a robust strategy that guarantees the satisfaction of multiple properties at the same time in face of the transition probability uncertainty. We first show that this problem is PSPACE-hard. Then, we provide a value iteration-based decision algorithm to approximate the Pareto set of achievable points. We finally demonstrate the practical effectiveness of our proposed approaches by applying them on several case studies using a prototypical tool.Comment: This article is a full version of a paper accepted to the Conference on Quantitative Evaluation of SysTems (QEST) 201