Abstract: Improved sub-assemblies and methods of control for use in a diagnostic assay system adapted to receive an assay cartridge are provided herein. Such sub-assemblies include: a brushless DC motor, a door opening/closing mechanism and cartridge loading mechanism, a syringe and valve drive mechanism assembly, a sonication horn, a thermal control device and optical detection/excitation device. Such systems can further include a communications unit configured to wirelessly communicate with a mobile device of a user so as to receive a user input relating to functionality of the system with respect to an assay cartridge received therein and relaying a diagnostic result relating to the assay cartridge to the mobile device.

Abstract: Software that performs the following operations: (i) receiving a set of graph predictions corresponding to an input text, where graph predictions of the set of graph predictions are generated by different respective machine learning models; (ii) blending the graph predictions of the set of graph predictions to generate a plurality of candidate blended graphs, where nodes and edges of the candidate blended graphs have respective selection metric values, generated using a selection metric function, that meet a minimum threshold; and (iii) selecting as an output blended graph a candidate blended graph of the plurality of candidate blended graphs having a highest total combination of selection metric values among the plurality of candidate blended graphs.

Abstract: A DC electric motor having a stator mounted to a substrate, the stator having a coil assembly having a magnetic core, a rotor mounted to the stator with permanent magnets distributed radially about the rotor, the permanent magnets extending beyond the magnetic core, and sensors mounted to the substrate adjacent the permanent magnets. During operation of the motor passage of the permanent magnets over the sensors produces a substantially sinusoidal signal of varying voltage substantially without noise and/or saturation, allowing an angular position of the rotor relative the substrate to be determined from linear portions of the sinusoidal signal without requiring use of an encoder or position sensors and without requiring noise-reduction or filtering of the signal.

Abstract: Embodiments are provided for generating a reasonable language model learning for text data in a knowledge graph in a computing system by a processor. One or more data sources and one or more triples may be analyzed from a knowledge graph. Training data having one or more candidate labels associated with one or more of the triples may be generated. One or more reasonable language models may be trained based on the training data.

Abstract: In some implementations, an optimization system may obtain health information identifying different measures of health of an asset. The health information identifies end of life information regarding an end of life curve of the asset and an effective age of the asset. The optimization system may determine, based on the health information, a hazard curve for the asset. The hazard curve indicates a predicted failure rate of the asset over a period of time. The optimization system may provide the hazard curve and the effective age of the asset as inputs to an optimization model. The optimization system may use the optimization model to determine a particular time for replacing the asset, wherein the particular time is determined based on the hazard curve and the effective age. The optimization system may cause the asset to be replaced at the particular time.

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: Thermal control devices adapted to provide improved control and efficiency in temperature cycling are provided herein. Such thermal control device can include a thermoelectric cooler controlled in coordination with another thermal manipulation device to control an opposing face of the thermoelectric cooler and/or a microenvironment. Some such thermal control devices include a first and second thermoelectric cooler separated by a thermal capacitor. The thermal control devices can be configured in a planar configuration with a means for thermally coupling with a planar reaction vessel of a sample analyzer for use in thermal cycling in a polymerase chain reaction of the fluid sample in the reaction vessel. Methods of thermal cycling using such a thermal control devices are also provided.

Abstract: Generative modeling from phylogenetic data is provided. The method comprises creating a multi-sequence alignment (MSA) based on a nucleic acid or protein sequence and generating a phylogenetic tree based on the MSA. The phylogenetic tree is fed into a number of machine learning models, which generate vector representations of the nucleic acid or protein sequences based on the phylogenetic tree. The machine learning models generate from the vector representation predicted nucleic acid or protein sequences for at least one of an evolution sequence, regression sequence, or sibling sequences of nucleic acids or proteins according to the phylogenetic tree.

Abstract: A computer implemented method for estimating environmental impact for industrial assets is provided. A number of processor units receive data for an industrial asset. The data for the industrial asset includes a number of variables associated with sustainability of the industrial asset. The sustainability of the industrial asset includes energy consumption, leakage, and energy loss associated with operations for the industrial asset. The number of processor units determines a relationship between environmental impact for the industrial asset and the number of variables according to the data. The number of processor units forecast energy consumption, leakage, and energy loss over a period of time for the industrial asset based on the data. The number of processor units estimate environmental impact for the industrial asset over the period of time based on the forecasted energy consumption, the forecasted energy loss, forecasted leakage, and the relationship.

Abstract: In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.

Abstract: A computer implemented method for controlling determining an optimization solution for controlling a physical system. An optimization model is formed using an objective function and a set of constraints, a machine learning model that predicts a target value for a target variable for a physical system in response to receiving inputs for input variables for the physical system, and a first principle model that predicts the target value for the target variable for the physical system in response to receiving the inputs for the input variables for the physical system. Set points are determined for the input variables for an extremum for the target value for the target variable in the optimization model using regions in which the set points result in agreement between the target value predicted by the machine learning model and the target value predicted by the first principle model. The set points form the optimization solution.

Abstract: Techniques for generating model ensembles are provided. A plurality of models trained to generate predictions at each of a plurality of intervals is received. A respective prediction accuracy of each respective model of the plurality of models is determined for a first interval of the plurality of intervals by processing labeled evaluation data using the respective model. Additionally, a model ensemble specifying one or more of the plurality of models for each of the plurality of intervals is generated, comprising selecting, for the first interval, a first model of the plurality of models based on (i) the respective prediction accuracies and (ii) at least one non-error metric.

Abstract: A relationship between an input, a set-point of a plurality of processes and an output of a corresponding process is learned using machine learning. A regression function is derived for each process based upon historical data. An autoencoder is trained for each process based upon the historical data to form a regularizer and the regression functions and regularizers are merged together into a unified optimization problem. System level optimization is performed using the regression functions and regularizers and a set of optimal set-points of a global optimal solution for operating the processes is determined. An industrial system is operated based on the set of optimal set-points.

Abstract: A method includes obtaining, by a processor set, labeled training data associated with a system; identifying, by the processor set, a first region and a second region in the labeled training data, wherein the first region is associated with a failure of the system and the second region is exclusive of the first region; and creating, by the processor set, re-labeled training data by altering one or more labels of the labeled training data in the first region based on data in the second region.

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: In some implementations, an optimization system may obtain health information identifying different measures of health of an asset. The health information identifies end of life information regarding an end of life curve of the asset and an effective age of the asset. The optimization system may determine, based on the health information, a hazard curve for the asset. The hazard curve indicates a predicted failure rate of the asset over a period of time. The optimization system may provide the hazard curve and the effective age of the asset as inputs to an optimization model. The optimization system may use the optimization model to determine a particular time for replacing the asset, wherein the particular time is determined based on the hazard curve and the effective age. The optimization system may cause the asset to be replaced at the particular time.

Abstract: An embodiment includes receiving, by a transformer monitoring system associated with a transformer, sensor data from one or more sensors during operation of the transformer. The embodiment also includes generating, by the transformer monitoring system, energy loss data representative of a predicted energy loss of the transformer based at least in part on the sensor data. The embodiment also includes training, by the transformer monitoring system, a failure rate prediction model using failure data, resulting in a trained failure rate prediction model that calculates failure probability distribution data indicative of a time at which a failure of the transformer is most likely to occur. The embodiment also includes generating, by the transformer monitoring system, replacement data representative of an optimal time for replacing the transformer based at least in part on the energy loss data, the failure probability distribution data, and specification data for the transformer.

Abstract: A method, computer program product and system are provided for feature engineering and synthetic data generation. A processor retrieves a plurality of data tables, where the plurality of data tables are heterogeneous in format and content. A processor trains a variational auto-encoder (VAE) model on the plurality of data tables. A processor receives an input data table. A processor generates a synthetic data table based on the input data table and the trained VAE model.

Abstract: A graph representing a current state of a set of assets is constructed, a weighted node in the graph representing an asset in the set of assets, a weighted edge in the graph representing a connection between two assets in the set of assets, a weight of the weighted node determined using an asset health score of the asset, a weight of the weighted edge determined according to an importance of the connection. A divergence between the graph and a previous graph representing a previous state of the set of assets is scored, the scoring resulting in a divergence score. Responsive to the divergence score being above a threshold score, a current maintenance schedule of the set of assets is adjusted, the adjusting resulting in an adjusted maintenance schedule.

Abstract: A DC electric motor having a stator mounted to a substrate, the stator having a coil assembly having a magnetic core, a rotor mounted to the stator with a first set of permanent magnets distributed radially about the rotor to facilitate rotation of the rotor and a second set of permanent magnets on the rotor to facilitate determination of an absolute position of the rotor. The motor further includes first and second set of sensors for detection of the magnets of the inner and outer rings. During operation of the motor passage of the permanent magnets over the sensors produces a substantially sinusoidal signal of varying voltage substantially without noise and/or saturation, allowing an absolute position of the rotor relative the substrate to be determined from the sinusoidal signals without requiring use of an encoder or position sensors and without requiring noise-reduction or filtering of the signal.