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An apparatus and method are provided for deterring or hindering any operation or dispensing of fluid through one or more taps by an unauthorized user. The apparatus is a tap block, the tap block including a block wall having a block exterior surface and a block interior surface, the block interior s

An apparatus and method are provided for deterring or hindering any operation or dispensing of fluid through one or more taps by an unauthorized user. The apparatus is a tap block, the tap block including a block wall having a block exterior surface and a block interior surface, the block interior surface defining a nozzle receiving cavity sized and shaped relative to the nozzle exterior surface such that at least a portion of the nozzle adjacent to the nozzle outlet may be received therein, the block wall including a nozzle receiving aperture opening from the block exterior surface through the block wall to the nozzle receiving cavity, the nozzle receiving aperture opening sized and shaped relative to the nozzle exterior surface such that at least the nozzle portion insertable into the nozzle receiving cavity may be received therethrough; and a securing device, the securing device is capable of maintaining said position of the tap block relative to the tap nozzle.

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An apparatus and method are provided for deterring or hindering any operation or dispensing of fluid through one or more taps by an unauthorized user. The apparatus is a tap block, the tap block including a block wall having a block exterior surface and a block interior surface, the block interior s

An apparatus and method are provided for deterring or hindering any operation or dispensing of fluid through one or more taps by an unauthorized user. The apparatus is a tap block, the tap block including a block wall having a block exterior surface and a block interior surface, the block interior surface defining a nozzle receiving cavity sized and shaped relative to the nozzle exterior surface such that at least a portion of the nozzle adjacent to the nozzle outlet may be received therein, the block wall including a nozzle receiving aperture opening from the block exterior surface through the block wall to the nozzle receiving cavity, the nozzle receiving aperture opening sized and shaped relative to the nozzle exterior surface such that at least the nozzle portion insertable into the nozzle receiving cavity may be received therethrough; and a securing device, the securing device is capable of maintaining said position of the tap block relative to the tap nozzle. als; recomputing the learning weights based on the measured input values and the computed output values for a plurality of time trials; recomputing the connection specifications based on the measured input values and the computed output values for a plurality of time trials; recomputing the output feature specifications based on the measured input values and the computed output values for a plurality of time trials; and reassigning functionality among the kernels. 6. The method of claim 4, wherein computing the output feature values includes: imputing out feature values for a current time trial based on input feature values for one or more historical time trials, computing monitored output feature values based on the input feature values for the current time travel; computing deviance values based on the imputed output feature values and the monitored output feature values; and basing the computed output values on the monitored output feature values. 7. The method of claim 6, wherein responding to the vector of computed output values includes: comparing each deviance value to an associated threshold values; and if one of the deviance values exceeds its associated threshold value, performing one or more deviance operation selected from the group including, indicating an alarm condition, and basing the computed the output values on the imputed output feature values rather than the monitored the output feature values for the output feature value associated with the deviance value that exceeds its associated threshold value. 8. The method of claim 1, wherein computing output feature values includes predicting output feature values for future time trials. 9. The method of claim 1, wherein computing output feature values includes: imputing output feature values for the current time trial based on the input feature values for one or more historical time trials; and predicting output feature values for future time trials. 10. The method of claim 1, wherein responding to the vector of computed output values includes performing one or more control operations selected from the group including: displaying a representation of the computed output values on a display device, and actuating a controlled parameter to compensate for a condition indicated by the computed output values. 11. The method of claim 1, wherein the multi-kernel processor comprises: an array of spatially-dedicated kernels corresponding to a spatially-contiguous field from which input values are measured and for which output values are predicted; and each kernel configured to compute one of the computed output values based on a set of adjacent measured input values. 12. The method of claim 11, wherein: each kernel of the multi-kernel processor corresponds to a mutually-exclusive time-specific price forecast based on the measured input values; and each kernel configured to predict its corresponding mutually-exclusive time-specific price forecast based on the assured input values. 13. The method of claim 1, wherein: each kernel of the multi-kernel processor corresponds to a pixel in a visual image; each measured input value corresponds to a measured intensity of one of the pixels in the visual image; and each computed output value corresponds to a computed intensity of one of the pixels of the visual image. 14. The method of claim 1, wherein the multi-kernel processor comprises: an array of temporally-dedicated kernels corresponding to a time-based index from which input values are measured and for which output values are predicted; and each kernel configured to predict a mutually-exclusive one of the time-based index values based on the measured input values. 15. The method of claim 14, wherein the measured input values comprise: the commodity price index; and price indices for currencies and other commodities. 16. The method of claim 1, wherein the multi-kernel processor comprises: a plurality of kernel groups each comprising a plurality of temporally-dedicated kernels corresponding to a time-based index from which input values are measured and for which output values are predicted; each kernel group comprising a plurality of individual kernels, each configured to predict a component of a mutually-exclusive time-based index value based on the measured input values; the plurality of groups of temporarily-dedicated kernels defining an array of spatially-dedicated kernel groups; and each kernel group configured to compute a component of the time-based index. 17. The method of claim 1, wherein: the time-based index comprises an electricity demand index; each kernel group corresponds to a plurality of electricity delivery points; and the measured input values comprise electricity demand and weather data. 18. The method of claim 1, wherein: each kernel of the processor is operative for computing updated connection weights based on the received input values, the connection weights, the connection specifications, and learning weights; the connection weights comprise the elements of an inverse covariance matrix; and computing updated connection weights comprise one or more steps from the group consisting of, updating the inverse covariance matrix, and inverting the updated covariance matrix. 19. An apparatus for determining whether refinement operations are indicated using learning correlations, comprising: (a) means for receiving an iteration of measured input values for a current trial; (b) means for providing a vector of input feature values based on the measured input values to a multi-kernel processor, each kernel of the processor operative for: receiving one or more of the input feature values, receiving correction specifications, connection weights, and learning weights, and computing output feature values based on the received input feature values, the connection weights, and the connection specifications; (c) means for responding to a vector of computed output values based on the output feature values computed by each kernel; and (d) means for determining whether the refinement operations are indicated using the learning correlations and based on the output feature values. rticular instance in time. "Remote" time represents the time zones of the other individuals (i.e., other than the user). The system may show events and appointments in the user's own "local" time (or other user-selected type of time), regardless of where the user is presently located. Using these three types of time (i.e., "local" time, "home" time, and "remote" time), the system provides improved interface and methods for scheduling and managing activities, such a phone conference, across multiple time zones. ion of Neural Network Techniques to Automatic License Plate Recognition," IEEE European convention on security and detection, 1995. St. Petersburg Times, "Put a Stop to Red Light violators," Sep. 9, 1998. Star Tribune, "Many favor use of photo cop// readers say red light runners should be caught on camera," Dec. 18, 1997. Rockey Mountain News, "Running red light to cost more . . . for some traffic violators," Nov. 27, 1997. 1983, pp. 926-940. 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