Abstract: Data characterizing inputs to a prediction process that classifies events, an output of the prediction process, and feedback data characterizing a performance of the outcome is monitored. A resource capacity affecting the outcome of the prediction process, and/or a cost-benefit affecting the outcome of the prediction process is determined from the monitoring. The determined resource capacity and/or the determined cost-benefit is provided. Related apparatus, systems, techniques and articles are also described.

Abstract: A method includes monitoring performance of a generated model while the generated model is being used for classification on live data, the monitoring including determining a first performance value of the generated model at a first point in time and determining a second performance value of the generated model at a second point in time; rendering, within a graphical user interface, a plot including a first axis and a second axis, the first axis including a characterization of a first performance metric and the second axis including a characterization of a second performance metric; and rendering, within the graphical user interface and the plot, a first graphical object at a first location characterizing the first performance value and a second graphical object at a second location characterizing the second performance value. Related apparatus, systems, techniques and articles are also described.

Abstract: A method includes receiving, via a model building platform, historical user behavior including historical data analysis characteristics; generating, based on the historical data analysis characteristics, a blueprint for guiding user action to accomplish a task, the generating including constructing the blueprint using the historical data analysis characteristics; receiving, via graphical user interface, user input requesting generation of a model and a task description; determining, using the blueprint and based on the task description, data analysis characteristics; and rendering, within the graphical user interface, a prompt to select the determined data analysis characteristics. Related apparatus, systems, techniques and articles are also described.

Abstract: A method includes receiving data characterizing a first output of one or more of a first set of models associated with a first organization, the first set of models trained on a first dataset using a first set of resourcing levels; training one or more of a second set of models associated with a second organization based on a second dataset using a second set of resourcing levels, global constraints and the first output, wherein the second set of resourcing levels specifying a second condition on outputs of the one or more of the second set of models; assessing, based on a second output of the one or more of the second set of models, performance of the one or more of second set of models; and retraining the first set of models or a subset thereof. Related apparatus, systems, articles, and techniques are also described.

Abstract: Data is received characterizing a population and a target trait characteristic for selecting candidates from the population. The population is segmented into at least a first subpopulation and a second subpopulation. A first number of candidates is selected from the first subpopulation and using a first model. The first number of candidates is selected according to the target trait characteristic. The first model having been trained using a first training population in which all members of the first training population are part of the first class of the two or more classes. A second number of candidates is selected from the second subpopulation and using a second model. The second model having been trained using a second training population in which all members of the second training population are part of the second class of the two or more classes. Related apparatus, systems, techniques and articles are also described.

Abstract: Performance of a first generated model can be monitored while the first generated model is deployed for use on live data. The monitoring can include determining a first performance value of the first generated model. Performance of a second generated model can be monitored while the second generated model is deployed for use on live data. The monitoring can include determining a second performance value of the second generated model. A plot including a first axis and a second axis can be rendered. The first axis can include a characterization of a first performance metric and the second axis can include a characterization of a second performance metric. A first graphical object at a first location characterizing the first performance value and a second graphical object at a second location characterizing the second performance value can be rendered. Related apparatus, systems, techniques and articles are also described.

Abstract: A method includes receiving input specifying a first selection of a first value of a variable of a dataset, the variable including a set of values associated with a model including a set of submodels, the set of submodels including a first submodel, the first value associated with the first submodel; determining a first routing rule specifying use of the first submodel associated with the selected first value when the model receives the selected first value as input; and deploying the model with the first routing rule. Related apparatus, systems, techniques and articles are also described.

Abstract: Performance of a first generated model can be monitored while the first generated model is deployed for use on live data. The monitoring can include determining a first performance value of the first generated model. Performance of a second generated model can be monitored while the second generated model is deployed for use on live data. The monitoring can include determining a second performance value of the second generated model. A plot including a first axis and a second axis can be rendered. The first axis can include a characterization of a first performance metric and the second axis can include a characterization of a second performance metric. A first graphical object at a first location characterizing the first performance value and a second graphical object at a second location characterizing the second performance value can be rendered. Related apparatus, systems, techniques and articles are also described.

Abstract: A method includes receiving, via a graphical user interface, data characterizing user input specifying a cost of error of a prediction, a model type requirement, and a computational resource allocation for model development; determining, based on the received data and using a model, a prediction of model performance; and rendering, within the graphical user interface, a plot including a first axis, a second axis, and a region characterizing the determined prediction of model performance, the first axis including a characterization of a first performance metric and the second axis including a characterization of a second performance metric. Related apparatus, systems, techniques and articles are also described.

Abstract: A method includes monitoring performance of a generated model while the generated model is being used for classification on live data, the monitoring including determining a first performance value of the generated model at a first point in time and determining a second performance value of the generated model at a second point in time; rendering, within a graphical user interface, a plot including a first axis and a second axis, the first axis including a characterization of a first performance metric and the second axis including a characterization of a second performance metric; and rendering, within the graphical user interface and the plot, a first graphical object at a first location characterizing the first performance value and a second graphical object at a second location characterizing the second performance value. Related apparatus, systems, techniques and articles are also described.

Abstract: A method includes receiving, via a model building platform, historical user behavior including historical data analysis characteristics; generating, based on the historical data analysis characteristics, a blueprint for guiding user action to accomplish a task, the generating including constructing the blueprint using the historical data analysis characteristics; receiving, via graphical user interface, user input requesting generation of a model and a task description; determining, using the blueprint and based on the task description, data analysis characteristics; and rendering, within the graphical user interface, a prompt to select the determined data analysis characteristics. Related apparatus, systems, techniques and articles are also described.

Abstract: Performance of a first generated model can be monitored while the first generated model is deployed for use on live data. The monitoring can include determining a first performance value of the first generated model. Performance of a second generated model can be monitored while the second generated model is deployed for use on live data. The monitoring can include determining a second performance value of the second generated model. A plot including a first axis and a second axis can be rendered. The first axis can include a characterization of a first performance metric and the second axis can include a characterization of a second performance metric. A first graphical object at a first location characterizing the first performance value and a second graphical object at a second location characterizing the second performance value can be rendered. Related apparatus, systems, techniques and articles are also described.

Abstract: A method includes receiving data characterizing a target accuracy and a performance metric of a model; rendering, within a graphical user interface display space, a plot including a first axis and a second axis, the first axis including a characterization of false positive and the second axis including a characterization of false negative; and rendering, within the graphical user interface display space and within the plot, a graphical object at a location characterizing the performance metric and a visualization indicative of the target accuracy. Related apparatus, systems, techniques and articles are also described.

Abstract: Pharmaceutical combinations of opioid analgesics and analgesics that act through non-opioid mechanisms are commonly used to provide pain relief. An example of this pharmaceutical combination is the product Vicodin™, where the opioid analgesic is hydrocodone and the non-opioid is acetaminophen. However, liver toxicity from the acetaminophen component is common. The invention provides an improvement over the opioid and acetaminophen pharmaceutical combinations for the management of pain by the concomitant administration of an opioid analgesics and the non-opioid analgesic 3-hydroxyacetanilide. This combination has been found to exhibit unexpectedly enhanced analgesic activity when dosed orally in a mammal.

Abstract: A request to access a file comprising content and metadata is received. In response to determining the request is from a source other than an owner of the content (or file), a copy of the content is generated and the content's metadata is modified in order to create modified metadata for the copy of the content. A Uniform Resource Locator (URL) that provides access to the copy of the content and the modified metadata is provided to the source of the request. However, in response to determining the request is from the owner of the content, a URL that provides access to the content and the metadata is provided to the owner of the content.

Abstract: N-Methyldiprenorphine, useful as an opioid antagonist.

Abstract: Compounds of formula (I) in which R1, R2, R3 and R4 have the meanings given in the specification, are receptor tyrosine kinase inhibitors useful in the treatment of proliferative disorders, such as cancer.

Abstract: Disclosed are compounds which inhibit or modulate the activity of protein kinases and pharmaceutical compositions containing such compounds. The disclosed compound contain two or more ligand moieties covalently linked together by one or more linking groups. Such compounds are useful for treating diseases or medical disorders mediated by protein kinases.

Abstract: EPG services are provided, which enables a device such as a computer, a set top box or the like to collect EPG data from multiple EPG data providers or sources. The EPG data is collected at the device by EPG loaders. Each EPG loader collects EPG data from a particular source. The EPG loaders deliver the EPG data to a writer module, which performs scaling and formatting functions to the EPG data. The writer module also stores the scaled and formatted EPG data in a database or other storage. Once stored, applications gain access to the EPG data through a control module. The writer module may also be implemented by chaining EPG loaders in a priority scheme. However, EPG loader interaction reduces independence and forces more rigid interface and implementation constraints upon the design of EPG loaders. The control module provides the accessed EPG data to the applications. EPG services function to isolate the operation of the applications from the EPG data sources.

Abstract: EPG services are provided, which enables a device such as a computer, a set top box or the like to collect EPG data from multiple EPG data providers or sources. The EPG data is collected at the device by EPG loaders. Each EPG loader collects EPG data from a particular source. The EPG loaders deliver the EPG data to a writer module, which performs scaling and formatting functions to the EPG data. The writer module also stores the scaled and formatted EPG data in a database or other storage. Once stored, applications gain access to the EPG data through a control module. The writer module may also be implemented by chaining EPG loaders in a priority scheme. However, EPG loader interaction reduces independence and forces more rigid interface and implementation constraints upon the design of EPG loaders. The control module provides the accessed EPG data to the applications. EPG services function to isolate the operation of the applications from the EPG data sources.