Abstract: An industrial automation controller includes a housing with a forced convection chamber. First and second fans are releasably connected to the housing and are adapted to induce airflow through the forced convection chamber. The first and second fans are each connected to the housing by respective first and second latch systems that each include a primary latch and a secondary latch. The secondary latch imposes a time delay during removal and replacement of a fan to facilitate hot swapping of the fan with a replacement fan. A make-last/break-first contact system is provided for each fan such that the fan is shutdown in a controlled manner prior to removal of the fan from the housing. The controller monitors internal temperature and fan speed. The controller initiates, logs, and reports fault conditions based upon the monitored temperature and/or fan speed. The controller is shut down if the monitored temperature exceeds a select temperature level.

Abstract: This invention relates to novel whitening agents for cellulosic substrates. The whitening agents are comprised of at least two components: at least one chromophore component and at least one polymeric component. Suitable chromophore components generally fluoresce blue, red, violet, or purple color when exposed to ultraviolet light, or they may absorb light to reflect these same shades. The whitening agents are further characterized by having a dispersion component value of the Hansen Solubility Parameter of less than or equal to about 17 MPa0.5. This invention also relates to laundry care compositions including but not limited to liquid and/or powder laundry detergent formulations and rinse added fabric softening (RAFS) compositions that comprise such whitening agents.

Abstract: A system, method and/or computer usable program product for dynamically optimizing inventory among multiple distribution warehouses across multiple time periods within a time span, including receiving an identified configuration of the multiple distribution warehouses including associated shipping lanes, receiving an identified set of good types, receiving an identified set of constraints and capacities applicable to the identified configuration, receiving a set of incentive based weights associated with inventory levels of the set of goods and with shipments on the shipping lanes, generating a model of the multiple distribution warehouses including the identified configuration, the identified set of good types, the identified set of constraints and capacities, and the incentive based weights, the model including incoming shipment destinations and outgoing shipment originations as decision variables and including transfer shipments with good types and quantities thereof as decision variables, receiving curren

Abstract: A system, method and/or computer usable program product for automatically and dynamically planning and executing reward based schedules and operations of a distribution warehouse locality including accessing a set of customer orders for fulfillment from the distribution warehouse locality within a predetermined timeframe, each customer order including a priority for fulfillment; accessing current inventory levels for the distribution warehouse locality; accessing expected shipments of inventory to the distribution warehouse locality; accessing a set of expected labor resources at the distribution warehouse locality for fulfilling the set of customer orders within the predetermined timeframe; automatically generating a set of tasks for completing each customer order; automatically optimizing an allocation of the set of tasks with the set of expected labor resources for each customer order; automatically scheduling the allocated set of expected labor resources and the set of tasks for fulfilling the set of cust

Abstract: A system, method and/or computer usable program product for automatically and dynamically planning and executing schedules and operations of a distribution warehouse locality including accessing a set of customer orders for fulfillment from the distribution warehouse locality within a predetermined timeframe, each customer order including a priority for fulfillment; accessing current inventory levels for the distribution warehouse locality; accessing expected shipments of inventory to the distribution warehouse locality; accessing a set of expected labor resources at the distribution warehouse locality for fulfilling the set of customer orders within the predetermined timeframe; automatically generating a set of tasks for completing each customer order; automatically optimizing an allocation of the set of tasks with the set of expected labor resources for each customer order; automatically scheduling the allocated set of expected labor resources and the set of tasks for fulfilling the set of customer orders;

Abstract: A method includes generating, by a processor of a computing device, a first plurality of models (including a first number of models) based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm. The method includes determining whether to modify an epoch size for the genetic algorithm during a second epoch of the genetic algorithm based on a convergence metric associated with at least one epoch that is prior to the second epoch. The second epoch is subsequent to the first epoch. The method further includes, based on determining to modify the epoch size, generating a second plurality of models (including a second number of models that is different than the first number) based on the genetic algorithm and corresponding to the second epoch. Each model of the first plurality of models and the second plurality of models includes data representative of neural networks.

Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

Abstract: Diverse clustering of a data set, including: generating a first plurality of clustering models based on a same data set; selecting, based on a novelty search of the first plurality of clustering models, a second plurality of clustering models; and generating a report based on the second plurality of clustering models.

Abstract: A method, system and/or computer usable program product for managing optimization of a transportation network system including identifying a set of items for transportation between a set of origins and a set of destinations during a set of time periods, prioritizing each of the set of items, utilizing a set of network constraints and costs, utilizing a set of loading constraints for a set of transportation units, the loading constraints limiting the quantity and placement of items for each transportation unit, optimizing a quantity of the set of transportation units for transporting the set of items between the set of origins and the set of destinations during the set of time periods based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit, and adjusting the optimized quantity of transportation units to a discrete quantity based on the prioritization of each of the set of items and the network constraints and costs.

Abstract: A method includes determining, by a processor of a computing device, a subset of models included in a plurality of models generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes aggregating the subset of models to generate an ensembler. The ensembler, when executed on an input, provides at least a portion of the input to each model of the subset of models to generate a plurality of intermediate outputs. An ensembler output of the ensembler is based on the plurality of intermediate outputs. The method further includes executing the ensembler on input data to determine the ensembler output.

Abstract: A method includes generating, by a processor of a computing device, an output set of models corresponding to a first epoch of a genetic algorithm and based on an input set of models of the first epoch. The input set and the output set includes data representative of a neural network. The method includes determining a particular model of the output set based on a fitness function. A first topological parameter of a first model of the input set is modified to generate the particular model of the output set. The method includes modifying a probability that the first topological parameter is to be changed by a genetic operation during a second epoch of the genetic algorithm that is subsequent to the first epoch. The method includes generating a second output set of models corresponding to the second epoch and based on the output set and the modified probability.

Abstract: A method includes determining a trainable model to provide to a trainer, the trainable model determined based on modification of one or more models of a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes providing the trainable model to the trainer. The method further includes adding a trained model, output by the trainer based on the trainable model, as input to a second epoch of the genetic algorithm, the second epoch subsequent to the first epoch.

Abstract: A method includes generating, by a processor of a computing device, a first plurality of models (including a first number of models) based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm. The method includes determining whether to modify an epoch size for the genetic algorithm during a second epoch of the genetic algorithm based on a convergence metric associated with at least one epoch that is prior to the second epoch. The second epoch is subsequent to the first epoch. The method further includes, based on determining to modify the epoch size, generating a second plurality of models (including a second number of models that is different than the first number) based on the genetic algorithm and corresponding to the second epoch. Each model of the first plurality of models and the second plurality of models includes data representative of neural networks.

Abstract: A method, system and/or computer usable program product for managing optimization of a transportation network system including identifying a set of items for transportation between a set of origins and a set of destinations during a set of time periods, prioritizing each of the set of items, utilizing a set of network constraints and costs, utilizing a set of loading constraints for a set of transportation units, the loading constraints limiting the quantity and placement of items for each transportation unit, optimizing a quantity of the set of transportation units for transporting the set of items between the set of origins and the set of destinations during the set of time periods based on the prioritization of each of the set of items, the network constraints and costs, and the loading constraints for each transportation unit, and adjusting the optimized quantity of transportation units to a discrete quantity based on the prioritization of each of the set of items and the network constraints and costs.

Abstract: A method includes generating, by a processor of a computing device, an output set of models corresponding to a first epoch of a genetic algorithm and based on an input set of models of the first epoch. The input set and the output set includes data representative of a neural network. The method includes determining a particular model of the output set based on a fitness function. A first topological parameter of a first model of the input set is modified to generate the particular model of the output set. The method includes modifying a probability that the first topological parameter is to be changed by a genetic operation during a second epoch of the genetic algorithm that is subsequent to the first epoch. The method includes generating a second output set of models corresponding to the second epoch and based on the output set and the modified probability.

Abstract: A method includes determining a trainable model to provide to a trainer, the trainable model determined based on modification of one or more models of a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes providing the trainable model to the trainer. The method further includes adding a trained model, output by the trainer based on the trainable model, as input to a second epoch of the genetic algorithm, the second epoch subsequent to the first epoch.

Abstract: A method includes generating, by a processor of a computing device, a first plurality of models (including a first number of models) based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm. The method includes determining whether to modify an epoch size for the genetic algorithm during a second epoch of the genetic algorithm based on a convergence metric associated with at least one epoch that is prior to the second epoch. The second epoch is subsequent to the first epoch. The method further includes, based on determining to modify the epoch size, generating a second plurality of models (including a second number of models that is different than the first number) based on the genetic algorithm and corresponding to the second epoch. Each model of the first plurality of models and the second plurality of models includes data representative of neural networks.

Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

Abstract: A method includes performing, by a computing device, a clustering operation to group documents of a document corpus into clusters in a feature vector space. The document corpus includes one or more labeled documents and one or more unlabeled documents. Each of the one or more labeled documents is assigned to a corresponding class in classification data associated with the document corpus, and each of the one or more unlabeled document is not assigned to any class in the classification data. The method also includes generating, by the computing device, a prompt requesting classification of a particular document of the document corpus, where the particular document is selected based on a distance between the particular document and a labeled document of the one or more labeled documents.

Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.