Abstract: Provided are industrial processes for producing an organic acid alky ester from a feedstock containing organic acids and/or saponifiables, comprising: countercurrently contacting a feedstock with an organic alkylating reagent over two or more vessels or stages at temperature between 100° C. and 400° C. and pressure between 0.1 barg and 355 barg while simultaneously removing water and/or glycerin with unreacted alkylating reagent from the final vessel or stage to result in a first reaction method product containing organic acid alkyl esters, followed by a choice of using the alkyl esters as-is, purifying the organic acid alkyl esters from the first reaction product mixture or subjecting the first reaction product mixture to an additional transesterification reaction to convert saponifiables into additional organic acid alkyl esters, then purifying the organic acid alkyl esters from this second reaction method product.

Abstract: In an example embodiment, an apparatus for making pouch products includes a conveyor system. The conveyor system includes a first receiving location along a path of the conveyor system, and a dosing location along the path of the conveyor system. The apparatus also includes a first material dispensing station configured to transfer a first material to the first receiving location. The first material includes a first elastic layer and a first support layer. The first material dispensing station includes a dispenser roller configured to hold a roll of the first material, a plurality of rollers configured to convey the first material from the dispenser roller to the first receiving location, and a stripper plate configured remove at least a portion of the first support layer from a portion of the first elastic layer.

Abstract: According to one embodiment, a method, computer system, and computer program product for probabilistic inference from imprecise knowledge is provided. The embodiment may include identifying a knowledge base of one or more statements and first probability distributions corresponding to each of the one or more statements. The embodiment may also include identifying one or more queries. The embodiment may further include determining logical inferences about and second probability distributions for queries from the one or more queries or statements from the one or more statements based on information in the knowledge base.

Abstract: In an example embodiment, an apparatus for making pouch products includes a conveyor system. The conveyor system includes a first receiving location along a path of the conveyor system, and a dosing location along the path of the conveyor system. The apparatus also includes a first material dispensing station configured to transfer a first material to the first receiving location. The first material includes a first elastic layer and a first support layer. The first material dispensing station includes a dispenser roller configured to hold a roll of the first material, a plurality of rollers configured to convey the first material from the dispenser roller to the first receiving location, and a stripper plate configured remove at least a portion of the first support layer from a portion of the first elastic layer.

Abstract: A method and system facilitate self-service processing of a jackpot win by a player of a gaming machine. A jackpot processing system receives notification of a jackpot win, generates jackpot processing information including an amount to be awarded to the player, such as a jackpot amount less offsets thereto, and generates and sends a message to the player's mobile communication device. The message allows the player to confirm the jackpot processing information and provide necessary confirmations, such as a signature. The jackpot processing system generates a jackpot win record, may generate one or more associated win forms, and facilitates payment of the award amount to the player.

Abstract: Processes disclosed are capable of converting biomass into high-crystallinity nanocellulose with surprisingly low mechanical energy input. In some variations, the process includes fractionating biomass with an acid (such as sulfur dioxide), a solvent (such as ethanol), and water, to generate cellulose-rich solids and a liquid containing hemicellulose and lignin; and mechanically treating the cellulose-rich solids to form nanofibrils and/or nanocrystals. The crystallinity of the nanocellulose material may be 80% or higher, translating into good reinforcing properties for composites. The nanocellulose material may include nanofibrillated cellulose, nanocrystalline cellulose, or both. In some embodiments, the nanocellulose material is hydrophobic via deposition of some lignin onto the cellulose surface. Optionally, sugars derived from amorphous cellulose and hemicellulose may be separately fermented, such as to monomers for various polymers.

Abstract: Techniques for identifying mention identifiers within a message using a mentions processor are discussed herein. In some examples, a communication platform maintains a mentions processor configured to receive a message including a mention identifier, analyze the message to determine a type of the mention identifier included in the message, identify a second user account or a virtual space referenced by the mention identifier, determine whether a notification should be sent to the second user account or the virtual space, and, upon determining that the notification should be sent, send the notification associated with the message to the second user profile or the virtual space. In some examples, an observability class associated with the mentions processor allows the use of a mentions lookup tool to view one or more stored mention identifiers associated with the message and one or more stored notifications enqueued for the message.

Abstract: Physical object boundary detection techniques and systems are described. In one example, an augmented reality module generates three dimensional point cloud data. This data describes depths at respective points within a physical environment that includes the physical object. A physical object boundary detection module is then employed to filter the point cloud data by removing points that correspond to a ground plane. The module then performs a nearest neighbor search to locate a subset of the points within the filtered point cloud data that correspond to the physical object. Based on this subset, the module projects the subset of points onto the ground plane to generate a two-dimensional boundary. The two-dimensional boundary is then extruded based on a height determined from a point having a maximum distance from the ground plane from the filtered cloud point data.

Abstract: A computer-implemented method may include processors configured for receiving input data corresponding to a knowledge base comprising a plurality of propositional logic clauses, generating output data corresponding to a first set of logical rules and a first set of facts based on the plurality of propositional logic clauses, accumulating the first set of facts to generate accumulated facts, generating a model graph based on the accumulated facts and the first set of logical rules, alternating reasoning and learning passes at the model graph until convergence to generate a second set of logical rules and a second set of facts, and generating a third set of logical rules and a third set of facts, wherein the third set of logical rules and the third set of facts exceed a first predetermined threshold.

Abstract: Physical object boundary detection techniques and systems are described. In one example, an augmented reality module generates three dimensional point cloud data. This data describes depths at respective points within a physical environment that includes the physical object. A physical object boundary detection module is then employed to filter the point cloud data by removing points that correspond to a ground plane. The module then performs a nearest neighbor search to locate a subset of the points within the filtered point cloud data that correspond to the physical object. Based on this subset, the module projects the subset of points onto the ground plane to generate a two-dimensional boundary. The two-dimensional boundary is then extruded based on a height determined from a point having a maximum distance from the ground plane from the filtered cloud point data.

Abstract: Physical object boundary detection techniques and systems are described. In one example, an augmented reality module generates three dimensional point cloud data. This data describes depths at respective points within a physical environment that includes the physical object. A physical object boundary detection module is then employed to filter the point cloud data by removing points that correspond to a ground plane. The module then performs a nearest neighbor search to locate a subset of the points within the filtered point cloud data that correspond to the physical object. Based on this subset, the module projects the subset of points onto the ground plane to generate a two-dimensional boundary. The two-dimensional boundary is then extruded based on a height determined from a point having a maximum distance from the ground plane from the filtered cloud point data.

Abstract: Word sense disambiguation using a glossary layer embedded in a deep neural network includes receiving, by one or more processors, input sentences including a plurality of words. At least two words in the plurality of words are homonyms. The one or more processors convert the plurality of words associated with each input sentence into a first vector including possible senses for the at least two words. The first vector is then combined with a second vector including a domain-specific contextual vector associated with the at least two words. The combination of the first vector with the second vector is fed into a recurrent deep logico-neural network model to generate a third vector that includes word senses for the at least two words. A threshold is set for the third vector to generate a fourth vector including a final word sense vector for the at least two words.

Abstract: To improve the technological process of computerized answering of logical queries over incomplete knowledge bases, obtain a first order logic query; with a trained, computerized neural network, convert the first order logic query into a logic embedding; and answer the first order logic query using the logic embedding.

Abstract: Physical object boundary detection techniques and systems are described. In one example, an augmented reality module generates three dimensional point cloud data. This data describes depths at respective points within a physical environment that includes the physical object. A physical object boundary detection module is then employed to filter the point cloud data by removing points that correspond to a ground plane. The module then performs a nearest neighbor search to locate a subset of the points within the filtered point cloud data that correspond to the physical object. Based on this subset, the module projects the subset of points onto the ground plane to generate a two-dimensional boundary. The two-dimensional boundary is then extruded based on a height determined from a point having a maximum distance from the ground plane from the filtered cloud point data.

Abstract: An aerial vehicle system, such as an unmanned aerial vehicle, that includes a camera system with replaceable camera modules that have different spectral, optical and/or sensing characteristics from one another. By replacing one camera module with another camera module, different information can be gathered and analyzed by the camera system of the aerial vehicle system.

Abstract: Maximum expressivity can be received representing a ratio between maximum and minimum input weights to a neuron of a neural network implementing a weighted real-valued logic gate. Operator arity can be received associated with the neuron. Logical constraints associated with the weighted real-valued logic gate can be determined in terms of weights associated with inputs to the neuron, a threshold-of-truth, and a neuron threshold for activation. The threshold-of-truth can be determined as a parameter used in an activation function of the neuron, based on solving an activation optimization formulated based on the logical constraints, the activation optimization maximizing a product of expressivity representing a distribution width of input weights to the neuron and gradient quality for the neuron given the operator arity and the maximum expressivity. The neural network of logical neurons can be trained using the activation function at the neuron, the activation function using the determined threshold-of-truth.

Abstract: Word sense disambiguation using a glossary layer embedded in a deep neural network includes receiving, by one or more processors, input sentences including a plurality of words. At least two words in the plurality of words are homonyms. The one or more processors convert the plurality of words associated with each input sentence into a first vector including possible senses for the at least two words. The first vector is then combined with a second vector including a domain-specific contextual vector associated with the at least two words. The combination of the first vector with the second vector is fed into a recurrent deep logico-neural network model to generate a third vector that includes word senses for the at least two words. A threshold is set for the third vector to generate a fourth vector including a final word sense vector for the at least two words.

Abstract: A system for configuring and using a logical neural network including a graph syntax tree of formulae in a represented knowledgebase connected to each other via nodes representing each proposition. One neuron exists for each logical connective occurring in each formula and, additionally, one neuron for each unique proposition occurring in any formula. All neurons return pairs of values representing upper and lower bounds on truth values of their corresponding subformulae and propositions. Neurons corresponding to logical connectives accept as input the output of neurons corresponding to their operands and have activation functions configured to match the connectives' truth functions. Neurons corresponding to propositions accept as input the output of neurons established as proofs of bounds on the propositions' truth values and have activation functions configured to aggregate the tightest such bounds.

Abstract: Maximum expressivity can be received representing a ratio between maximum and minimum input weights to a neuron of a neural network implementing a weighted real-valued logic gate. Operator arity can be received associated with the neuron. Logical constraints associated with the weighted real-valued logic gate can be determined in terms of weights associated with inputs to the neuron, a threshold-of-truth, and a neuron threshold for activation. The threshold-of-truth can be determined as a parameter used in an activation function of the neuron, based on solving an activation optimization formulated based on the logical constraints, the activation optimization maximizing a product of expressivity representing a distribution width of input weights to the neuron and gradient quality for the neuron given the operator arity and the maximum expressivity. The neural network of logical neurons can be trained using the activation function at the neuron, the activation function using the determined threshold-of-truth.