2 resultados para gray level probabilty density functions
em Repositorio Institucional de la Universidad de Málaga
Resumo:
Background: Previous studies have reported errors in Activities of Daily Living (ADL) under the presence of distracting objects in dementia and brain injury patients. However, little is known about which distractor-target objects relation might be more harmful for performance. Method: We compared the ADL execution in frontal brain injured patients and control participants under two conditions: One in which target objects were mixed with distractor objects that constituted an alternative semantically related but non-required task (contextual condition) and another in which target objects were mixed with related but isolated distractors that did not constituted a coherent task (non-contextual condition). We separately analyzed ADL commission errors (repetitions, substitutions, objects manipulations, failures in sequence, extra actions) and omissions. In addition, the participants were evaluated with a neuropsychological protocol including a very specific executive functions task (Selective attention, Stimulus-Stimulus and Stimulus-Response conflict). Results: We found that frontal patients produced more commission errors compared to control participants, but only under the contextual condition. No between groups significant differences were found in omissions in both conditions or commission errors in non-contextual conditions. Scores in the Stimulus-Response conflict was significantly correlated with commission errors in the contextual condition. Conclusion: The presence of different non-target objects in ADL performance could require different cognitive process. Contextual ADL conditions required a higher level of executive functions, especially at the level of response (Stimulus-Response conflict). Application to Practice: Occupational therapists should control the presence of objects related to the target task according to the intervention objectives with the patients.
Resumo:
Efficient hill climbers have been recently proposed for single- and multi-objective pseudo-Boolean optimization problems. For $k$-bounded pseudo-Boolean functions where each variable appears in at most a constant number of subfunctions, it has been theoretically proven that the neighborhood of a solution can be explored in constant time. These hill climbers, combined with a high-level exploration strategy, have shown to improve state of the art methods in experimental studies and open the door to the so-called Gray Box Optimization, where part, but not all, of the details of the objective functions are used to better explore the search space. One important limitation of all the previous proposals is that they can only be applied to unconstrained pseudo-Boolean optimization problems. In this work, we address the constrained case for multi-objective $k$-bounded pseudo-Boolean optimization problems. We find that adding constraints to the pseudo-Boolean problem has a linear computational cost in the hill climber.