425 research outputs found
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
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