971 resultados para Navy Center for Applied Research in Artificial Intelligence (U.S.)


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The purpose of the current study was to examine two different trajectories of sport participation and explore any similarities or differences that may result regarding per­sonal development and sport outcomes. Seventy-four youth athletes (40 “specializ­ers” and 34 “samplers”) were recruited for the current study and four measures were employed to assess sport experiences and outcomes. Discriminant function analyses revealed no differences between groups in asset possession or sources of enjoyment however, differences were reported in sport experiences and burnout. The “samplers” reported more experiences regarding the integration of sport and family as well as linkages to the community. Although the “specializers” reported higher levels of physical/emotional exhaustion than did the “samplers,” they also reported more expe­riences related to diverse peer groups. The differences highlight the importance of examining specific pathways of development in sport to gain a deeper understanding of youths’ experiences in sport.

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Bibliographical footnotes.

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In this paper a prior knowledge representation for Artificial General Intelligence is proposed based on fuzzy rules using linguistic variables. These linguistic variables may be produced by neural network. Rules may be used for generation of basic emotions – positive and negative, which influence on planning and execution of behavior. The representation of Three Laws of Robotics as such prior knowledge is suggested as highest level of motivation in AGI.

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The purpose of the current study was to examine two different trajectories of sport participation and explore any similarities or differences that may result regarding per­sonal development and sport outcomes. Seventy-four youth athletes (40 “specializ­ers” and 34 “samplers”) were recruited for the current study and four measures were employed to assess sport experiences and outcomes. Discriminant function analyses revealed no differences between groups in asset possession or sources of enjoyment however, differences were reported in sport experiences and burnout. The “samplers” reported more experiences regarding the integration of sport and family as well as linkages to the community. Although the “specializers” reported higher levels of physical/emotional exhaustion than did the “samplers,” they also reported more expe­riences related to diverse peer groups. The differences highlight the importance of examining specific pathways of development in sport to gain a deeper understanding of youths’ experiences in sport.

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This paper analyzes the inner relations between classical sub-scheme probability and statistic probability, subjective probability and objective probability, prior probability and posterior probability, transition probability and probability of utility, and further analysis the goal, method, and its practical economic purpose which represent by these various probability from the perspective of mathematics, so as to deeply understand there connotation and its relation with economic decision making, thus will pave the route for scientific predication and decision making.

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Theories of sparse signal representation, wherein a signal is decomposed as the sum of a small number of constituent elements, play increasing roles in both mathematical signal processing and neuroscience. This happens despite the differences between signal models in the two domains. After reviewing preliminary material on sparse signal models, I use work on compressed sensing for the electron tomography of biological structures as a target for exploring the efficacy of sparse signal reconstruction in a challenging application domain. My research in this area addresses a topic of keen interest to the biological microscopy community, and has resulted in the development of tomographic reconstruction software which is competitive with the state of the art in its field. Moving from the linear signal domain into the nonlinear dynamics of neural encoding, I explain the sparse coding hypothesis in neuroscience and its relationship with olfaction in locusts. I implement a numerical ODE model of the activity of neural populations responsible for sparse odor coding in locusts as part of a project involving offset spiking in the Kenyon cells. I also explain the validation procedures we have devised to help assess the model's similarity to the biology. The thesis concludes with the development of a new, simplified model of locust olfactory network activity, which seeks with some success to explain statistical properties of the sparse coding processes carried out in the network.

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A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.