Abstract: A device for use in determining the position (location and/or orientation) of at least one dental implant, the device comprising a central body with an attachment portion adapted to attach to the dental implant. One or more arms extend away from the central body of the device and are adapted to bond to at least one other device. In particular, the one or more arms of the device bond to the central body and/or one or more arms of another device.

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 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 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 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 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: 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: 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.

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, 0.5 to 25 weight percent of a carboxylic acid containing monomer, 0 to 10 weight percent of a sulfonic acid containing monomer, and 10 to 60 weight percent of a cationic monomer and makes up from 10 to 35 weight percent of the water-in-oil emulsion.

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 system for discharging residue from an agricultural combine to a ground surface. The system including a spreader operatively connected to a rear end of the agricultural combine. The spreader having one or more impellers each having an axis of rotation and a housing. The impellers are operatively connected to the housing for rotating therein. The housing has an inlet for receiving a flow of residue, an outlet configured about a lateral side of the housing for discharging the flow of residue, and one or more residue flow distributors each pivotably movable in a direction generally parallel with the axis of rotation. Each flow distributor is configured to guide the discharged flow of residue sidewardly away from the spreader to the ground surface. The rotation of the one or more impellers discharge a flow of residue received through the inlet out through the outlet.