Abstract: A rotor lock assembly for locking a rotor of a wind turbine. The rotor lock assembly has at least one relocatable rotor lock. The relocatable rotor lock has a housing, a bushing element, a pin shaft position within the bushing element, and a locking mechanism. The housing includes a mounting portion adapted for mounting to a bearing housing adjacent to a rotor lock plate of the rotor.

Abstract: Systems, computer program products, and methods are described herein for implementing dynamic network channel switching for secure communication. The present invention is configured to receive, from a first user input device, a resource transfer request via a first communication channel; determine, using a secure channel monitoring engine, that the first communication channel does not meet one or more preset channel requirements for secure communication; determine a second communication channel associated with a second user input device, wherein the second user input device is within a preset geographic radius of the first user input device, wherein the second user input device is associated with the resource distribution platform; trigger, via the second communication channel, the second user input device to establish a communication link with the first user input device to form an alternate communication channel; and execute, via the alternate communication channel, the resource transfer request.

Abstract: Systems, computer program products, and methods are described herein for dynamic communication channel switching based on preconfigured network security protocols.

Abstract: Systems, computer program products, and methods are described herein for the parallel testing of multiple data processing channels. The present invention may be configured to generate a data packet, send the data packet to a processing channel for processing at a send time, receive the processed data from the processing channel at a return time, determine the percent accuracy for the processed data from the processing channel, and record the send time, return time, and percent accuracy in an analytics system. The present invention may also be configured to determine the security of the processing channel and record the security in the analytics system.

Abstract: A water-in-oil emulsion that includes an oil phase (O) and an aqueous phase (A) at an O/A ratio of from about 1:8 to about 10:1; wherein the water-in-oil emulsion includes the oil phase as a continuous phase that includes an inert hydrophobic liquid, at least one water-insoluble hydrophobic monomer, and at least one surfactant, and the aqueous phase as a dispersed phase of distinct particles in the oil phase that includes water and a water soluble polymer that includes: (i) at least one acrylamide monomer and (ii) at least one acrylic acid monomer; wherein the water soluble polymer is present in an amount from about 10 to about 35 weight percent of the water-in-oil emulsion.

Abstract: A water-in-oil emulsion that includes an oil phase (O) and an aqueous phase (A) at an O/A ratio of from about 1:8 to about 10:1; wherein the water-in-oil emulsion includes the oil phase as a continuous phase that includes an inert hydrophobic liquid, at least one water-insoluble hydrophobic monomer, and at least one surfactant, and the aqueous phase as a dispersed phase of distinct particles in the oil phase that includes water and a water soluble polymer that includes: (i) at least one acrylamide monomer and (ii) at least one acrylic acid monomer; wherein the water soluble polymer is present in an amount from about 10 to about 35 weight percent of the water-in-oil emulsion.

Abstract: A water-in-oil emulsion, having an oil phase (0) and an aqueous phase (A) at an O/A ratio of from about 1:8 to about 10:1, may include the oil phase as a continuous phase that includes an inert hydrophobic liquid, and the aqueous phase as a dispersed phase of distinct particles in the oil phase that includes water, a water soluble polymer, and at least one surfactant: wherein the water soluble polymer includes from about 1 to about 60 weight percent of one or more cationic monomers, wherein the amount is by total weight of the water soluble polymer; wherein the water soluble polymer is present in an amount from about 5 to about 40 weight percent of the water-in-oil emulsion; and wherein an aqueous solution prepared by inverting the water- in-oil emulsion by adding it to water has at least comparable viscosity build to an aqueous solution made from a water-in-oil emulsion of the same composition containing 15 weight percent more water soluble polymer.

Abstract: A water-in-oil emulsion having an oil phase (O) and an aqueous phase (A) at an O/A ratio of from about 1:8 to about 10:1; wherein the water-in-oil emulsion includes the oil phase as a continuous phase that includes an inert hydrophobic liquid, and the aqueous phase as a dispersed phase of distinct particles in the oil phase that includes water, a water soluble polymer, and at least one surfactant; wherein the water soluble polymer includes from about 1 to about 60 weight percent of one or more cationic monomers, wherein the amount is by total weight of the water soluble polymer; wherein the water soluble polymer is present in an amount from about 5 to about 40 weight percent of the water-in-oil emulsion; and wherein an aqueous solution prepared by inverting the water-in-oil emulsion by adding it to water has at least comparable viscosity build to an aqueous solution made from a water-in-oil emulsion of the same composition containing 15 weight percent more water soluble polymer.

Abstract: One embodiment of the present invention sets forth a technique for performing inference operations associated with a trained machine learning model. The technique includes comparing a first input image with a plurality of image representations that are associated with a plurality of output classes predicted by the trained machine learning model. The technique also includes determining that the first input image does not match any image representation included in the plurality of image representations and subsequently determining that the first input image does match a first alternative representation that is associated with a first output class included in the plurality of output classes. The technique further includes generating a first prediction that indicates that the first input image is a member of the first output class.

Abstract: One embodiment of the present invention sets forth a technique for simplifying a trained machine learning model. The technique includes determining a first set of images associated with a first output class predicted by the trained machine learning model. The technique also includes generating a first aggregated representation of the first set of images, wherein the first aggregated representation includes a first plurality of representative pixel values for a plurality of pixel locations included in the first set of images. The technique further includes generating a simplified representation of the trained machine learning model that includes a first mapping of the first aggregated representation to the first output class, wherein the first mapping indicates that the trained machine learning model predicts the first output class for one or more input images.

Abstract: One embodiment of the present invention sets forth a technique for processing requests associated with one or more services. The technique includes deploying a first container within an environment, wherein the first container includes a first service that implements a first interface and a first shim that implements a second interface. The technique also includes receiving, at the first shim, a first request associated with the second interface. The technique further includes converting the first request into a second request associated with the first interface, and transmitting the second request over the first interface to the first service, wherein the second request is processed by the first service.

Abstract: One embodiment of the present invention sets forth a technique for executing one or more services in a technology stack. The technique includes deploying a first set of containers within an environment, wherein each container included in the first set of containers includes a first service that implements a first interface and a first shim that implements a second interface. The technique also includes transmitting a first request associated with the second interface to a first container included in the first set of containers, wherein the first request is processed by an instance of the first shim and an instance of the first service executing within the first container.

Abstract: Various embodiments set forth systems and techniques for generating domain-specific question answering models. The techniques include receiving a set of input text corresponding to a particular domain; generating, based on the set of input text, a question-answer dataset corresponding to the particular domain, the question-answer dataset comprising a plurality of question-answer pairs; and causing one or more machine learning algorithms to be applied to the question-answer dataset to generate a question answering model associated with the particular domain.

Abstract: Various embodiments set forth systems and techniques for adaptive visualization of a quantized neural network. The techniques include generating one or more network visualizations of a neural network; determining, based on the one or more network visualizations, one or more quantization schemes associated with the neural network; and re-training the neural network or approximating the neural network, based on adjusting one or more quantization coefficients associated with the one or more quantization schemes.

Abstract: Various embodiments set forth systems and techniques for adaptive generation and visualization of a quantized neural network. The techniques include extracting, based on one or more input features and one or more non-quantized network parameters, one or more attributes; calculating, based on the one or more attributes, one or more quantization coefficients; generating, based on the one or more quantization coefficients, one or more quantized input features; and generating, based on the one or more quantized input features and one or more quantization techniques, a neural network.

Abstract: A friction reducing treatment solution that includes water, from 100 to 500,000 ppm of total dissolved solids, and from 0.5 to 3 gallons per thousand gallons of a water-in-oil emulsion containing a water soluble polymer. The total dissolved solids include at least 10 weight percent of a multivalent cation. The water-in-oil emulsion includes an oil phase and an aqueous phase, where the oil phase is a continuous phase containing an inert hydrophobic liquid and the aqueous phase is present as dispersed distinct particles in the oil phase and contains water, the water soluble polymer, and surfactants and an inverting surfactant. The water soluble polymer is made up of 30 to 60 weight percent of a non-ionic monomer, 5 to 50 weight percent of a sulfonic acid containing monomer, and 10 to 60 weight percent of a cationic monomer and makes up from 5 to 40 weight percent of the water-in-oil emulsion.

Abstract: A water-in-oil emulsion having an oil phase (O) and an aqueous phase (A) at an O/A ratio of from about 1:8 to about 10:1; wherein the water-in-oil emulsion includes the oil phase as a continuous phase that includes an inert hydrophobic liquid, and the aqueous phase as a dispersed phase of distinct particles in the oil phase that includes water, a water soluble polymer, and at least one surfactant; wherein the water soluble polymer includes from about 1 to about 60 weight percent of one or more cationic monomers, wherein the amount is by total weight of the water soluble polymer; wherein the water soluble polymer is present in an amount from about 5 to about 40 weight percent of the water-in-oil emulsion; and wherein an aqueous solution prepared by inverting the water-in-oil emulsion by adding it to water has at least comparable viscosity build to an aqueous solution made from a water-in-oil emulsion of the same composition containing 15 weight percent more water soluble polymer.

Abstract: A water-in-oil emulsion having an oil phase (O) and an aqueous phase (A) at an O/A ratio of from about 1:8 to about 10:1; wherein the water-in-oil emulsion includes the oil phase as a continuous phase that includes an inert hydrophobic liquid, and the aqueous phase as a dispersed phase of distinct particles in the oil phase that includes water, a water soluble polymer, and at least one surfactant; wherein the water soluble polymer includes from about 1 to about 60 weight percent of one or more cationic monomers, wherein the amount is by total weight of the water soluble polymer; wherein the water soluble polymer is present in an amount from about 5 to about 40 weight percent of the water-in-oil emulsion; and wherein an aqueous solution prepared by inverting the water-in-oil emulsion by adding it to water has at least comparable viscosity build to an aqueous solution made from a water-in-oil emulsion of the same composition containing 15 weight percent more water soluble polymer.

Abstract: One embodiment of the present invention sets forth a technique for creating a machine learning model. The technique includes generating a user interface comprising one or more components for visually generating the machine learning model. The technique also includes modifying source code specifying a plurality of mathematical expressions that define the machine learning model based on user input received through the user interface. The technique further includes compiling the source code into compiled code that, when executed, causes one or more parameters of the machine learning model to be learned during training of the machine learning model.

Abstract: A water-in-oil emulsion that includes an oil phase (O) and an aqueous phase (A) at an O/A ratio of from about 1:8 to about 10:1; wherein the water-in-oil emulsion includes the oil phase as a continuous phase that includes an inert hydrophobic liquid, at least one water-insoluble hydrophobic monomer, and at least one surfactant, and the aqueous phase as a dispersed phase of distinct particles in the oil phase that includes water and a water soluble polymer that includes: (i) at least one acrylamide monomer and (ii) at least one acrylic acid monomer; wherein the water soluble polymer is present in an amount from about 10 to about 35 weight percent of the water-in-oil emulsion.