Example apparatus to determine a state of a media presentation device are disclosed. A disclosed example apparatus is to determine contribution values from at least one of a signal measured from a sensing device or an output signal accessed from the presentation device, the contribution values indicative of a state of a presentation device. The disclosed example apparatus is also to sum a first plurality of the contribution values corresponding to a first measurement cycle to generate a first intermediate fuzzy score for the first measurement cycle, store the first intermediate fuzzy score in a buffer, the buffer including a plurality of intermediate fuzzy scores corresponding to respective measurement cycles, combine the intermediate fuzzy scores corresponding to a first time period to form a final fuzzy score, and when the final fuzzy score satisfies a threshold, set the state of the presentation device as on and enable crediting of media.

An authoritative candidate is selected for determining a location of a point of interest (POI). Source data including name, address, and location for POIs is received from multiple data sources. The received data is normalized for ease of comparison, and coordinates for each candidate are compared to coordinates of other candidates to determine which candidate if any is an authoritative location for the POI. The candidate locations are compared using two models a metric-based scoring system and a machine learning model that may utilize a gradient boosted decision tree. The authoritative candidate can be used to render digital maps that include the POI. In addition, the authoritative candidate's location can be used to provide vehicle route guidance to the POI.

Methods and apparatus for determining whether a media presentation device is in an on state or an off state are disclosed. A disclosed example method comprises determining contribution values from at least one of a signal measured from a sensing device or an output signal accessed from the presentation device, wherein the contribution values are indicative of a state of a presentation device. Summing, via a logic circuit, a first plurality of the contribution values corresponding to a first measurement cycle to generate a first intermediate fuzzy score for the first measurement cycle. Storing the first intermediate fuzzy score in a buffer including a plurality of intermediate fuzzy scores corresponding to respective measurement cycles. Combining, via the logic circuit, the intermediate fuzzy scores corresponding to a first time period to form a final fuzzy score. When the final fuzzy score satisfies a threshold, setting the state of the presentation device as on and enabling crediting of media presented by the presentation device.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural translation systems with rare word processing. One of the methods is a method training a neural network translation system to track the source in source sentences of unknown words in target sentences, in a source language and a target language, respectively and includes deriving alignment data from a parallel corpus, the alignment data identifying, in each pair of source and target language sentences in the parallel corpus, aligned source and target words; annotating the sentences in the parallel corpus according to the alignment data and a rare word model to generate a training dataset of paired source and target language sentences; and training a neural network translation model on the training dataset.

Methods and systems for generating output of a simulation model in a simulation system are described. In an example, a processor may retrieve observed output data from a memory. The observed output data may be generated based on a simulation operator of a simulation model. The processor may further optimize a generalization error of a distance measure between the observed output data and model output data. The model output data may be generated based on a high-fidelity operator. The processor may further determine a correction operator based on the optimized generalization error of the distance measure. The processor may further append the correction operator to the simulation operator to produce a supplemented operator. The processor may further generate supplemented output data by applying the simulation model with the supplemented operator on a set of inputs.

A method of adapting tuning parameter settings of a system (2) functionality (3) for road vehicle (1) speed adjustment control starting from initially selected settings and applying a training set of speed adjustment profiles obtained from manually negotiated road segments and road segment data for these. For each of these road segments: a simulated speed adjustment profile is calculated using the selected settings and the road segment data; the manual and the simulated speed adjustment profiles are compared to obtain a residual; a norm of the residual is calculated. For all of the road segments of the training set: a norm of the norms of the residuals is calculated; at least one of optimization, regression analysis or machine-learning is performed to minimize the norm of the norms of the residuals by selecting different settings and iterating the above steps. Settings rendering a minimal training set norm are selected.

Techniques for auto weight scaling a bounded weight range of RPU devices with the size of the array during ANN training are provided. In one aspect, a method of ANN training includes: initializing weight values w.sub.init in the array to a random value, wherein the array represents a weight matrix W with m rows and n columns; calculating a scaling factor based on a size of the weight matrix W; providing digital inputs x to the array; dividing the digital inputs x by a noise and bound management factor to obtain adjusted digital inputs x; performing a matrix-vector multiplication of the adjusted digital inputs x with the array to obtain digital outputs y; multiplying the digital outputs y by the noise and bound management factor ; and multiplying the digital outputs y by the scaling factor to provide digital outputs y of the array.

The method includes receiving historical data from a first data source; analyzing the historical data for a desired characteristic; determining a representative value for the desired characteristic of the historical data; determining a first data expectation for the historical data based on the representative value; transmitting the first data expectation to a first data recipient; receiving first incoming data from the first data source; analyzing the desired characteristic of the first incoming data; determining a first incoming data value for the desired characteristic for the first incoming data; comparing the first incoming data value and the representative value; determining a first difference between the first incoming data value and the representative value; and/or comparing the first difference to a difference threshold which indicates whether a difference between an incoming data value and the representative value is significant.

Systems, methods, and computer-readable media are disclosed for systems and methods of determining quantitative values representative of user action automaticity. Example methods may include determining a first request for a first user interface from a user device, determining a user identifier associated with the first request, and determining user interaction history data using the user identifier. Example methods may include determining a first selectable option for presentation in a first position at the first user interface using the user interaction history, determining a second selectable option for presentation in a second position at the first user interface, generating the first user interface, and sending the first user interface to the user device.

Systems, methods, and articles of manufacture for evaluating colliding data records are provided. The system may ingest one or more data inputs from one or more data sources. The system may parse the data inputs and determine whether the data is preexisting in the system. In response to the data input and stored data at least partially conflicting (e.g., colliding), the system may generate a candidate dataset based on the data input and the stored data. The candidate dataset may comprise two or more data candidates comprising permutations of the data values in the data input and the stored data. The system may evaluate each data candidate, score each date candidate based on the evaluation, and determine the data candidate having the greatest score.