Abstract: An embodiment senses a raw data sequence by a central processing unit, responsive to the raw data sequence, computes by the central processing unit a transfer data size of the raw data sequence based at least in part on the comparison of a data size of the raw data sequence to a memory size of a graphics processing unit. The embodiment transfers by the central processing unit of the raw data sequence to the graphics processing unit based on the transfer data size. The embodiment trains a foundation model on the raw data sequence where a sliding window algorithm is executed on the raw data sequence by the graphics processing unit, where generating a window of the sliding window algorithm is based on a memory pointer to the raw data sequence.

Abstract: A method, apparatus, computer system, and computer program product for parallelizing quantum processes for processing a problem. A computer system identifies subproblems in the problem based on a structure of the problem. The computer system identifies quantum circuits in a set of quantum computers to process the subproblems. The computer system executes jobs on the quantum circuits to solve the subproblems.

Abstract: A method for process control using predictive long short term memory includes obtaining historical post-process measurements taken on prior products of the manufacturing process; obtaining historical in-process measurements taken on prior workpieces during the manufacturing process; training a neural network to predict each of the historical post-process measurements, in response to the corresponding historical in-process measurements and preceding historical post-process measurements; obtaining present in-process measurements on a present workpiece during the manufacturing process; predicting a future post-process measurement for the present workpiece, by providing the present in-process measurements and the historical post-process measurements as inputs to the neural network; and adjusting at least one controllable variable of the manufacturing process in response to the prediction of the future post-process measurement.

Abstract: A method, system, and compute program product are configured to: receive a dataset comprising a multivariate time series that includes plural channels; generate an original forecast of the multivariate time series using a channel-independent backbone and a prediction head; and generate a revised forecast of the multivariate time series using a cross-channel reconciliation head with the original forecast, wherein the cross-channel reconciliation head generates the revised forecast based on correlations between the channels of the multivariate time series.

Abstract: A method for lightweight and efficient long sequence time-series forecasting and representation learning includes segmenting a time-series dataset from a plurality of sensors into a plurality of patches. The method further includes applying gated multilayer perceptron (MLP) mixing across different directions of the patched input time-series. The method further includes capturing local and global and interrelated correlations across the plurality of patches and within the plurality of patches. The method further includes applying a patch-time aggregated hierarchy to guide lowest-level predictions based on aggregated hierarchy signals at a patch-level. The method further includes chaining MLP-mixers in a patch length context aware hierarchy fashion to enhance time-series short and long-term correlation capture.

Abstract: A method, system, and computer program product that is configured to: receive an input time series from an external device in a first system, divide the input time series to a set of univariate time subseries, transform the set of univariate time subseries into a univariate prediction result series using a transformer model, concatenate the univariate prediction result series to a multivariate predictive result, and output the multivariate predictive result for providing time series forecasting to a second system.

Abstract: A prediction system may identify a first set of features of training data and a second set of features of the training data. The prediction system may train a deep learning model using the training data. Training the deep learning model may comprise training a first function to determine a relationship between the first set of features and the second set of features. Training the deep learning model may further comprise training a second function to determine a relationship between missing data of a first period of time and complete data of a second period of time that follows the first period of time. The prediction system may generate imputation time series data and forecasted time series data using the trained deep learning model. The imputation time series data is generated based on an imputation task and the forecasted time series data is generated based on a forecasting task.

Abstract: Mechanisms are provided for automatic identification of a reconciliation computer tool for producing coherent reconciled data from base data generated by a computer model. A machine learning training operation is executed on one or more performance prediction computer model(s) (PPCMs) based on first input features of at least one hierarchical dataset, and second input features of a plurality of different reconciliation computer tools. The PPCM(s) generate a prediction of performance of a corresponding reconciliation computer tool based on the first and second input features. Features are extracted from a runtime hierarchical dataset and input into the trained PPCM(s) which generate predictions of performance of a plurality of reconciliation computer tools based on the extracted features of the runtime hierarchical dataset. The reconciliation computer tools are ranked relative to one another based on the predictions of performance.

Abstract: Mechanisms are provided for performing automated and dynamic reconciliation of forecasts for hierarchical datasets. A machine learning training is executed on a dynamic reconciliation computer model engine to train the dynamic reconciliation computer model engine, based on historical data and forecast data, to learn an association of reconciliation computer models with structural changes in a hierarchical dataset. Runtime forecast data is generated based on a runtime hierarchical dataset, and the trained dynamic reconciliation computer model engine is executed on the runtime forecast data to reconcile the runtime forecast data across a hierarchy of the runtime forecast data. The trained dynamic reconciliation computer model applies different reconciliation computer models to the runtime forecast data based on structural changes in the runtime forecast data.

Abstract: In an approach for compressing a neural network, a processor receives a neural network, wherein the neural network has been trained on a set of training data. A processor receives a compression ratio. A processor compresses the neural network based on the compression ratio using an optimization model to solve for sparse weights. A processor re-trains the compressed neural network with the sparse weights. A processor outputs the re-trained neural network.

Abstract: A method of improving at least one of quality and yield of a physical process comprises: obtaining values, from respective performances of the physical process, for a plurality of variables associated with the physical process; determining at least one Gaussian mixture model (GMM) representing the values for the variables for the performances of the physical process; based at least in part on the at least one GMM, computing at least one anomaly score for at least one of the variables for at least one of the performances of the physical process; based on the at least one anomaly score, identifying the at least one of the performances of the physical process as an outlier; and, based at least in part on the outlier identification, modifying the at least one of the variables for one or more subsequent performances of the physical process.

Abstract: A method, apparatus, computer system, and computer program product for parallelizing quantum processes for processing a problem. A computer system identifies subproblems in the problem based on a structure of the problem. The computer system identifies quantum circuits in a set of quantum computers to process the subproblems. The computer system executes jobs on the quantum circuits to solve the subproblems.

Abstract: A computer implemented method of administering a complex system includes receiving multivariate data from a plurality of sensors of the system in an ambient state. Event sequences in the received multivariate data are identified. The multivariate event sequences are projected to a lower stochastic latent embedding. A temporal structure of the sequences is learned in a lower latent space. A probabilistic prediction in the lower latent space is provided. The probabilistic prediction in the lower stochastic latent space is decoded to an event prediction in the ambient state.

Abstract: In some examples, a power adapter comprises a first connector having multiple power setting keys, the first connector to couple to a first device; a second connector to couple to a second device; and a processor coupled to the first connector and the second connector, the processor to determine if the second device is configured to supply power in accordance with a power setting of the first device.

Abstract: An example electronic device includes a controller and a connector coupled to the controller. The connector includes a first set of pins and a second set of pins. The controller concurrently is to communicate with a first module of a modular computing system via the first set of pins and to communicate with a second module of the modular computing system, coupled to the first module, via the second set of pins.

Abstract: An example of an apparatus is provided. The apparatus includes a power supply to connect to a power source. The power supply is to receive and to distribute a total power from the power source. The apparatus further includes a first device to receive a first portion of the total power from the power supply. The apparatus further includes a second device to receive a second portion of the total power from the power supply. A sum of the first portion and the second portion is the total power. In addition, the apparatus includes a controller to control the second device, wherein the controller is to determine the total power demanded by the first device and the second device. The controller is to reduce the second portion of the total power and to restore the second portion of the total power.

Abstract: An example system may include a processor and a non-transitory machine-readable storage medium storing instructions executable by the processer to determine, by referencing a firmware configuration setting, a universal serial bus (USB) port role to be assigned to a USB port when a condition is detected by the firmware; and send, to a power delivery (PD) controller of the USB port, a command to assign the USB port role to the USB port responsive to a detection of the condition.

Abstract: Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.