5 resultados para interwoven activity recognition

em CentAUR: Central Archive University of Reading - UK


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The coordination of design is a multi-faceted problem in construction. In design interactions in particular the real-time coordination of design activity is a persistent concern. The use of objects to coordinate the activity of design is studied as this happens in interactions between an architect and a building user group, in a setting where maintaining awareness of the design situation is important. An account of ways in which this was accomplished and how design activity is coordinated through interactional practices is provided. The empirical analyses examine design interaction from an ethnomethodological/conversation analysis (EM/CA) informed perspective to examine: ways in which mutual orientation to design issues are accomplished, how objects can provide a resource for the recognition of the activities of others and ways in which objects might be observable as momentarily intelligible. Subtle interactional practices involving talk, gesture and gaze were some of the small ways in which mutual orientation to the design actions of others became observable. The production of actions sequentially, in response to another's action, marked the real-time coordination of design moves in this setting. The relevance of accounts of micro-interaction to develop understanding of design activity and how it is coordinated are considered.

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We are developing computational tools supporting the detailed analysis of the dependence of neural electrophysiological response on dendritic morphology. We approach this problem by combining simulations of faithful models of neurons (experimental real life morphological data with known models of channel kinetics) with algorithmic extraction of morphological and physiological parameters and statistical analysis. In this paper, we present the novel method for an automatic recognition of spike trains in voltage traces, which eliminates the need for human intervention. This enables classification of waveforms with consistent criteria across all the analyzed traces and so it amounts to reduction of the noise in the data. This method allows for an automatic extraction of relevant physiological parameters necessary for further statistical analysis. In order to illustrate the usefulness of this procedure to analyze voltage traces, we characterized the influence of the somatic current injection level on several electrophysiological parameters in a set of modeled neurons. This application suggests that such an algorithmic processing of physiological data extracts parameters in a suitable form for further investigation of structure-activity relationship in single neurons.

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In a study looking at the culturable, aerobic Actinobacteria associated with the human gastrointestinal tract, the vast majority of isolates obtained from dried human faeces belonged to the genus Bacillus and related bacteria. A total of 124 isolates were recovered from the faeces of 10 healthy adult donors. 16S rRNA gene sequence analyses showed the majority belonged to the families Bacillaceae (n = 81) and Paenibacillaceae (n = 3), with Bacillus species isolated from all donors. Isolates tentatively identified as Bacillus clausii (n = 32) and B. licheniformis (n = 28) were recovered most frequently, with the genera Lysinibacillus, Ureibacillus, Oceanobacillus, Ornithinibacillus and Virgibacillus represented in some donors. Phenotypic data confirmed the identities of isolates belonging to well-characterized species. Representatives of the phylum Actinobacteria were recovered in much lower numbers (n = 11). Many of the bacilli exhibited antimicrobial activity against one or more strains of Clostridium difficile, C. perfringens, Listeria monocytogenes and Staphylococcus aureus, with some (n = 12) found to have no detectable cytopathic effect on HEp-2 cells. This study has revealed greater diversity within gut-associated aerobic spore-formers than previous studies, and suggests that bacilli with potential as probiotics could be isolated from the human gut.

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We present a method for the recognition of complex actions. Our method combines automatic learning of simple actions and manual definition of complex actions in a single grammar. Contrary to the general trend in complex action recognition that consists in dividing recognition into two stages, our method performs recognition of simple and complex actions in a unified way. This is performed by encoding simple action HMMs within the stochastic grammar that models complex actions. This unified approach enables a more effective influence of the higher activity layers into the recognition of simple actions which leads to a substantial improvement in the classification of complex actions. We consider the recognition of complex actions based on person transits between areas in the scene. As input, our method receives crossings of tracks along a set of zones which are derived using unsupervised learning of the movement patterns of the objects in the scene. We evaluate our method on a large dataset showing normal, suspicious and threat behaviour on a parking lot. Experiments show an improvement of ~ 30% in the recognition of both high-level scenarios and their composing simple actions with respect to a two-stage approach. Experiments with synthetic noise simulating the most common tracking failures show that our method only experiences a limited decrease in performance when moderate amounts of noise are added.