Abstract: The invention relates generally to production and handling of navigational data. In one aspect, a method is provided to increase the predictive ability over novel data of models on a computer processor. The method comprises the steps of using training values of system input/desired system output data to obtain a plurality of models corresponding to different parameter settings, measuring the ability of the obtained models to predict desired output values not used to obtain the models, choosing a subset of the models by preferentially selecting according to measured predictive ability, and averaging the outputs of the selected models over the novel data.

Abstract: A method of evaluating an electrophysical signal. The method utilizes a model-derived reconstruction over at least one cycle of the electrophysical signal to identify a pathological event, whereby at least one term in the model is differentiable. The invention is also directed to a non-transitory computer readable medium having instructions stored thereon for identifying a pathological event from a model-derived reconstruction of an electrophysical signal. The invention is further directed to a system for evaluating an electrophysical signal. The system includes a processor configured to identify a pathological event from a model-derived reconstruction of the electrophysical signal, a data input coupled to the processor for providing the processor with the electrophysical signal, and a user interface.

Abstract: A method of evaluating an electrophysical signal. The method utilizes a model-derived reconstruction over at least one cycle of the electrophysical signal to identify a pathological event, whereby at least one term in the model is differentiable. The invention is also directed to a non-transitory computer readable medium having instructions stored thereon for identifying a pathological event from a model-derived reconstruction of an electrophysical signal. The invention is further directed to a system for evaluating an electrophysical signal. The system includes a processor configured to identify a pathological event from a model-derived reconstruction of the electrophysical signal, a data input coupled to the processor for providing the processor with the electrophysical signal, and a user interface.

Abstract: A method of predicting a clinical outcome for a patient with congestive heart failure is disclosed. A plurality of nonlinear first PCI models are identified based on a biomarker dataset, each of the models having a number of distinct terms. One or more second PCI models are identified based on the biomarker dataset, each of these models having a number of distinct terms which corresponds to the number of distinct terms for one or more of the nonlinear first PCI models. Each of the plurality of nonlinear first PCI models are statistically compared to one of the one or more second PCI models having a corresponding number of distinct terms to determine a preference for higher versus lower degree of nonlinearity or preference for shorter versus longer memory length. The clinical outcome is predicted based on the preference for higher versus lower degree of nonlinearity or memory length preference.

Abstract: The present invention provides a method for class prediction in bioinformatics based on identifying a nonlinear system that has been defined for carrying out a given classification task. Information characteristic of exemplars from the classes to be distinguished is used to create training inputs, and the training outputs are representative of the class distinctions to be made. Nonlinear systems are found to approximate the defined input/output relations, and these nonlinear systems are then used to classify new data samples. In another aspect of the invention, information characteristic of exemplars from one class are used to create a training input and output. A nonlinear system is found to approximate the created input/output relation and thus represent the class, and together with nonlinear systems found to represent the other classes, is used to classify new data samples.

Abstract: The present invention provides a method for class prediction in bioinformatics based on identifying a nonlinear system that has been defined for carrying out a given classification task. Information characteristic of exemplars from the classes to be distinguished is used to create training inputs, and the training outputs are representative of the class distinctions to be made. Nonlinear systems are found to approximate the defined input/output relations, and these nonlinear systems are then used to classify new data samples. In another aspect of the invention, information characteristic of exemplars from one class are used to create a training input and output. A nonlinear system is found to approximate the created input/output relation and thus represent the class, and together with nonlinear systems found to represent the other classes, is used to classify new data samples.

Abstract: Hot-box signalling devices are disclosed for indicating the presence of overheated bearings in vehicles such as rail cars. The device includes means defining a closed compartment containing a fluid and pressure-sensitive means communicating with the compartment. The compartment is arranged in thermal communication with a bearing of the vehicle so that increases in the temperature of the bearing cause increases in the pressure exerted by the fluid. The pressure-sensitive means is adapted to produce a signal when the pressure exerted by the fluid exceeds a predetermined threshold representing an over-temperature condition in the bearing. Preferably, the pressure-sensitive means takes the form of a plug normally closing an opening in the compartment, and a marker material, the plug and marker material being expelled when the pressure in the compartment reaches said predetermined threshold. In one aspect of the invention, provision is made to compensate for ambient temperature.