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.

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 β 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 α 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 α; and multiplying the digital outputs y′ by the scaling factor β to provide digital outputs y of the array.

The disclosed technology provides solutions for finding samples from image data that are similar to failure cases, by constructing N-dimensional vectors of the failure cases. The vectors of failure cases are compared to other image data, with the objective of identifying groups of images that can be labeled. The labeled images are then used to retrain a model. Systems and machine-readable media are also provided.

Circuitry, systems, and methods are provided for an integrated circuit that includes digital filter circuitry. The digital filtering circuitry includes a first partial filter that includes a first number of taps corresponding to coefficients of a first bit depth and a second partial filter that includes a second number of taps corresponding to coefficients of a second bit depth.

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.

A processor may receive a submission of a command. The processor may analyze the command for at least one commonality with a previous command and predict a predicted reason for the submission of the command based on historical learning. The processor may integrate the predicted reason into a corpus specific to a user, wherein the corpus includes user preference data, and wherein the processor predicts one or more orders of the user using the corpus.

An electronic device: displays an electronic form with a plurality of fields; detects an autofill input that corresponds to a field of the plurality of fields in the electronic form; and in response to detecting the autofill input, updates the electronic form to display fields that have been populated based on a user profile. If the autofill input is associated with a first category of information in the user profile, updating the electronic form includes populating at least two of the plurality of fields using information from the user profile that corresponds to the first category of information. If the autofill input is associated with a second category of information in the user profile, updating the electronic form includes populating at least two of the plurality of fields using information from the user profile that corresponds to the second category of information.

A fine-grained analog memory device includes: 1) a charge-trapping transistor including a gate and a high-k gate dielectric; and 2) a pulse generator connected to the gate and configured to apply a positive or negative pulse to the gate to change an amount of charges trapped in the high-k gate dielectric.

Systems for dynamically performing pattern recognition and data reconciliation functions are provided. In some examples, a system may receive data, from one or more computing systems. In some examples, one or more machine learning datasets may be used to identify datasets, data elements, or the like, for comparison. The identified datasets, data elements, and the like, may be compared to pre-stored patterns to determine whether the pattern matches a pre-stored pattern. If not, the pattern may be flagged as a new pattern and instructions for further processing may be requested. In some arrangements, the identified datasets, data elements, or the like, may be compared to determine whether a pattern and/or value of the datasets, data elements, or the like, matches. If not, one or more machine learning datasets may be used to generate a corrective action to align the data. In some examples, the generated corrective action may be automatically executed to align the data.

A device may identify a set of features associated with the unknown object. The device may determine, based on inputting the set of features into a threat prediction model associated with a set of security functions, a set of predicted threat scores. The device may determine, based on the set of predicted threat scores, a set of predicted utility values. The device may determine a set of costs corresponding to the set of security functions. The device may determine a set of predicted efficiencies, associated with the set of security functions, based on the set of predicted utility values and the set of costs. The device may identify, based on the set of predicted efficiencies, a particular security function, and may cause the particular security function to be executed on the unknown object. The device may determine whether another security function is to be executed on the unknown object.