Abstract: A time series forecasting system is disclosed that obtains a time series forecast request requesting a forecast for a particular time point. The forecast request identifies a primary time series dataset for generating the requested forecast and a set of features related to the primary time series dataset. The system provides the primary time series dataset and the set of features to a model to be used for generating the forecast. The model computes a feature importance score for one or more features and selects a subset of features based on their feature importance scores. The model determines attention scores for a set of data points in the primary time series dataset based on the selected subset of features. The system predicts an actual forecast for the particular time point based on the attention scores and outputs the actual forecast and explanation information associated with the actual forecast.

Abstract: The present disclosure relates to a computer-implemented method for automatically determining pin placement associated with an electronic design. Embodiments may include receiving, using at least one processor, at least one layout associated with the electronic design and separating the at least one layout into one or more grids. Embodiments may also include extracting one or more connectivity features from the one or more grids, wherein the one or more connectivity features include instance-pin and pin information. Embodiments may also include training a machine learning model, based upon, at least in part, the one or more connectivity features and receiving the machine learning model and a test layout at a predictor engine. Embodiments may further include providing a user with a pin placement recommendation based upon, at least in part, the machine learning model and the test layout.

Abstract: A computing device may access a graph comprising one or more model nodes, one or more dataset nodes, and one or more edges, the model nodes having a plurality of features. The device may add one or more test dataset nodes and test edges to the graph. The device may perform a series of iterative steps until a threshold is reached. For each iterative step: a selection probability is determined, the selection probability being based at least in part on a plurality of selection criteria; a particular model node is selected, the particular model node being selected based at least in part on the selection probability; the selection criteria is updated based at least in part on the particular model; and the plurality of features are updated based at least in part on the particular model. The device may provide the particular model node selected in the last iterative step.

Abstract: The present embodiments relate to generating input parameters for selecting a forecasting model. An example method includes a computing device receiving a time series comprising a plurality of data points, wherein each data point of the time series comprises a time associated with the data point and a value. The device can identify a first season and a second season from the time series, wherein a length of the first season is a factor of a length of the second season. The device can estimate a Fourier order and a seasonality mode for the first season based at least in part on the length of the first season and the length of the second season. The device can select a forecasting model to forecast a value of a future time step of the time series based at least in part on the Fourier order and the seasonality mode.

Abstract: The present disclosure relates to a computer-implemented method for electronic design is provided. Embodiments may include receiving, using at least one processor, an electronic design having an original schematic associated therewith and extracting one or more features for each net from the schematic. Embodiments may include storing one or more resistance or capacitance values for each net and applying the one or more resistance or capacitance values as labels for a machine learning model. Embodiments may also include training the machine learning model using one or more actual values to generate a trained model. Embodiments may further include receiving the trained model to predict parasitics for a stitching engine and generating a stitched schematic.

Abstract: A method for providing augmented reality based driver assistance. includes: receiving, from a plurality of sensors, information corresponding to an environment external to a vehicle; receiving position information indicating a position of the vehicle; generating an environmental model of the environment external to the vehicle; analyzing data associated with the environmental model; identifying information relevant to a current situation of the vehicle; monitoring at least one driver related parameter of a driver of the vehicle; generating actionable information based on the information relevant to the current situation of the vehicle and the at least one driver related parameter; generating an output based on the actionable information; and outputting the actionable information.