328 resultados para Dynamic classifiers

em Cambridge University Engineering Department Publications Database


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Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

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When learning a difficult motor task, we often decompose the task so that the control of individual body segments is practiced in isolation. But on re-composition, the combined movements can result in novel and possibly complex internal forces between the body segments that were not experienced (or did not need to be compensated for) during isolated practice. Here we investigate whether dynamics learned in isolation by one part of the body can be used by other parts of the body to immediately predict and compensate for novel forces between body segments. Subjects reached to targets while holding the handle of a robotic, force-generating manipulandum. One group of subjects was initially exposed to the novel robot dynamics while seated and was then tested in a standing position. A second group was tested in the reverse order: standing then sitting. Both groups adapted their arm dynamics to the novel environment, and this movement learning transferred between seated and standing postures and vice versa. Both groups also generated anticipatory postural adjustments when standing and exposed to the force field for several trials. In the group that had learned the dynamics while seated, the appropriate postural adjustments were observed on the very first reach on standing. These results suggest that the CNS can immediately anticipate the effect of learned movement dynamics on a novel whole-body posture. The results support the existence of separate mappings for posture and movement, which encode similar dynamics but can be adapted independently.

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We have constructed plasmids to be used for in vitro signature-tagged mutagenesis (STM) of Campylobacter jejuni and used these to generate STM libraries in three different strains. Statistical analysis of the transposon insertion sites in the C. jejuni NCTC 11168 chromosome and the plasmids of strain 81-176 indicated that their distribution was not uniform. Visual inspection of the distribution suggested that deviation from uniformity was not due to preferential integration of the transposon into a limited number of hot spots but rather that there was a bias towards insertions around the origin. We screened pools of mutants from the STM libraries for their ability to colonize the ceca of 2-week-old chickens harboring a standardized gut flora. We observed high-frequency random loss of colonization proficient mutants. When cohoused birds were individually inoculated with different tagged mutants, random loss of colonization-proficient mutants was similarly observed, as was extensive bird-to-bird transmission of mutants. This indicates that the nature of campylobacter colonization in chickens is complex and dynamic, and we hypothesize that bottlenecks in the colonization process and between-bird transmission account for these observations.

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The events that determine the dynamics of proliferation, spread and distribution of microbial pathogens within their hosts are surprisingly heterogeneous and poorly understood. We contend that understanding these phenomena at a sophisticated level with the help of mathematical models is a prerequisite for the development of truly novel, targeted preventative measures and drug regimes. We describe here recent studies of Salmonella enterica infections in mice which suggest that bacteria resist the antimicrobial environment inside host cells and spread to new sites, where infection foci develop, and thus avoid local escalation of the adaptive immune response. We further describe implications for our understanding of the pathogenic mechanism inside the host.

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