Abstract: A linear drive assembly configured for use within a power end assembly. The linear drive assembly is configured to interconnect a crankshaft and a pony rod and comprises a crosshead assembly attached to a connecting rod assembly. In one or more embodiments disclosed herein, the connecting rod assembly is configured to attach to opposite sides of the crosshead assembly so that no portion of the connecting rod assembly is disposed within an interior of a crosshead included in the crosshead assembly.

Abstract: A system receives information for a plurality of invoices having invoice amounts owed by a debtor-buyer to a plurality of creditor-suppliers, a cash pool amount for a cash pool available by the debtor-buyer for early payment of at least some of the invoice amounts, and a desired return rate, receives a plurality of offers, each offer from a corresponding creditor-supplier and comprising an offer return rate, determines whether each of the plurality of offers is eligible for acceptance based on each offer return rate from each offer and the desired return rate for the buyer and selects one or more invoices each having an invoice amount, and transmits award information to the debtor-buyer.

Abstract: A high pressure pump comprising a fluid end mechanically coupled to a power end. The power end is modular and comprises a crankshaft section, a crosshead section, and a connector section coupled together by a first set of stay rods. The fluid end comprises a plurality of fluid end sections positioned in a side-by-side relationship. Each of the plurality of fluid end sections are attached to the power end using a plurality of second set of stay rods.

Abstract: In one embodiment, a system includes at least one processor and logic configured to receive a request to process data stored in one or more index records in a keyed index of a database, determine an active key range for the one or more index records, and group the one or more index records into a number of groups for parallel processing thereof. In another embodiment, a method for utilizing a keyed index includes receiving a request to process data stored in index records in a keyed index of a database, determining an active key range for the index records, grouping the index records into a number of groups without prior knowledge of a key distribution for dividing the one or more index records into key ranges for parallel processing thereof; and processing the groups of index records in parallel.

Abstract: A question-and-answer system directed to a specific domain optimally utilizes reference documents that are semantically complete for that domain. Semantic completeness of a document is assessed using quality control questions (provided by subject matter experts) applied to the Q&A system followed by analysis of the proposed answers. That analysis is carried out using a cogency module having a feedforward neural network which receives metadata features of the document such as document ownership, document priority, and document type. A domain-optimized corpus for the Q&A system is built by so assessing multiple documents in a document collection, and adding each reference document that is reported as being semantically complete to the domain-optimized corpus. Thereafter, the deep learning question-and-answer system can receive a natural language query from a user, find a responsive answer in the documents while applying the domain-optimized corpus, and provide that answer to the user.

Abstract: A method of training a neural network includes receiving a text corpus containing a labeled portion and an unlabeled portion, extracting local n-gram features and a sequence of the local n-gram features from the text corpus, processing the text corpus, using convolutional layers, according to the local n-gram features to determine capsule parameters of capsules configured to preserve the sequence of the local n-gram features, performing a forward-oriented dynamic routing between the capsules using the capsule parameters to extract global characteristics of the text corpus, and processing the text corpus according to the global characteristics using a long short-term memory layer to extract global sequential text dependencies from the text corpus, wherein parameters of the neural network are updated according to the local n-gram features, the capsule parameters, global characteristics, and global sequential text dependencies.

Abstract: Method and apparatus for generating profiles using machine learning and influencing online interactions are provided. The methods include generating a user profile specifying a plurality of attribute values for a plurality of principle attributes, by processing a corpus of electronic documents using a first trained machine learning model. In an embodiment, the method further comprises generating a provider profile specifying a plurality of attribute values for the plurality of principle attributes, for each of a plurality of providers, by processing a respective corpus of electronic documents associated with each respective provider using a second trained machine learning model. A plurality of match coefficients based on comparing the user profile and the plurality of provider profiles are determined. Finally, one or more online interactions between the user and the target provider are influenced based on the determined match coefficients.

Abstract: Method and apparatus for generating profiles using machine learning and influencing online interactions are provided. The methods include receiving, from a first user of a plurality of users, a first set of electronic documents, where each electronic document in the first set of electronic documents corresponds to a respective user in the plurality of users. The methods also include identifying a plurality of user profiles, where each of the plurality of user profiles was generated by processing a corpus of electronic documents associated with each respective user using a first trained machine learning model. The methods include determining a plurality of match coefficients, based on comparing a plurality of user profiles associated with each respective user in the plurality of users, filtering the first set of electronic documents based on the plurality of match coefficients, and providing the filtered first set of electronic documents to the first user.

Abstract: A question-and-answer system directed to a specific domain optimally utilizes reference documents that are semantically complete for that domain. Semantic completeness of a document is assessed using quality control questions (provided by subject matter experts) applied to the Q&A system followed by analysis of the proposed answers. That analysis is carried out using a cogency module having a feedforward neural network which receives metadata features of the document such as document ownership, document priority, and document type. A domain-optimized corpus for the Q&A system is built by so assessing multiple documents in a document collection, and adding each reference document that is reported as being semantically complete to the domain-optimized corpus. Thereafter, the deep learning question-and-answer system can receive a natural language query from a user, find a responsive answer in the documents while applying the domain-optimized corpus, and provide that answer to the user.

Abstract: A method of training a neural network includes receiving a text corpus containing a labeled portion and an unlabeled portion, extracting local n-gram features and a sequence of the local n-gram features from the text corpus, processing the text corpus, using convolutional layers, according to the local n-gram features to determine capsule parameters of capsules configured to preserve the sequence of the local n-gram features, performing a forward-oriented dynamic routing between the capsules using the capsule parameters to extract global characteristics of the text corpus, and processing the text corpus according to the global characteristics using a long short-term memory layer to extract global sequential text dependencies from the text corpus, wherein parameters of the neural network are updated according to the local n-gram features, the capsule parameters, global characteristics, and global sequential text dependencies.

Abstract: Disclosed aspects relate to managing a set of offers using a dialogue. An adaptive profile may be received with respect to a client. The adaptive profile may indicate a set of client profile data, a set of client event data, and a set of client context data. A dialogue may be established with the client based on the adaptive profile. A set of offers may be resolved by an offer management engine based on the dialogue. The set of offers may be presented to the client.

Abstract: A computer-implemented mechanism is disclosed. The mechanism includes receiving a data signal, and comparing the data signal to one or more predefined patterns to determine one or more long/short term predictor scores. A discount factor is generated in response to the long/short term predictor scores. A set of expected rewards is generated. The set of expected rewards correspond to an action set specific to the data signal. The set of expected rewards are generated according to reinforced learning. The set of expected rewards are adjusted based on the discount factor. A selected action is selected from the action set based on the set of expected rewards. The selected action is initiated.

Abstract: Method and apparatus for generating profiles using machine learning and influencing online interactions are provided. The methods include receiving, from a first user of a plurality of users, a first set of electronic documents, where each electronic document in the first set of electronic documents corresponds to a respective user in the plurality of users. The methods also include identifying a plurality of user profiles, where each of the plurality of user profiles was generated by processing a corpus of electronic documents associated with each respective user using a first trained machine learning model. The methods include determining a plurality of match coefficients, based on comparing a plurality of user profiles associated with each respective user in the plurality of users, filtering the first set of electronic documents based on the plurality of match coefficients, and providing the filtered first set of electronic documents to the first user.

Abstract: Method and apparatus for generating profiles using machine learning and influencing online interactions are provided. The methods include generating a user profile specifying a plurality of attribute values for a plurality of principle attributes, by processing a corpus of electronic documents using a first trained machine learning model. In an embodiment, the method further comprises generating a provider profile specifying a plurality of attribute values for the plurality of principle attributes, for each of a plurality of providers, by processing a respective corpus of electronic documents associated with each respective provider using a second trained machine learning model. A plurality of match coefficients based on comparing the user profile and the plurality of provider profiles are determined. Finally, one or more online interactions between the user and the target provider are influenced based on the determined match coefficients.

Abstract: In one embodiment, a system includes at least one processor and logic configured to receive a request to process data stored in one or more index records in a keyed index of a database, determine an active key range for the one or more index records, and group the one or more index records into a number of groups for parallel processing thereof. In another embodiment, a method for utilizing a keyed index includes receiving a request to process data stored in index records in a keyed index of a database, determining an active key range for the index records, grouping the index records into a number of groups without prior knowledge of a key distribution for dividing the one or more index records into key ranges for parallel processing thereof; and processing the groups of index records in parallel.

Abstract: Disclosed aspects relate to managing a set of offers using a dialogue. An adaptive profile may be received with respect to a client. The adaptive profile may indicate a set of client profile data, a set of client event data, and a set of client context data. A dialogue may be established with the client based on the adaptive profile. A set of offers may be resolved by an offer management engine based on the dialogue. The set of offers may be presented to the client.

Abstract: In one embodiment, a system includes at least one processor and logic configured to receive a request to process data stored in one or more index records in a keyed index of a database, determine an active key range for the one or more index records, and group the one or more index records into a number of groups for parallel processing thereof. In another embodiment, a method for utilizing a keyed index includes receiving a request to process data stored in index records in a keyed index of a database, determining an active key range for the index records, wherein the active key range is determined based on any of: a type of the index records, a horizontal pointer to a next index record, and a low key value for each index record, and grouping the index records into a number of groups for parallel processing thereof.

Abstract: Disclosed aspects relate to managing a set of offers using a dialogue. An adaptive profile may be received with respect to a client. The adaptive profile may indicate a set of client profile data, a set of client event data, and a set of client context data. A dialogue may be established with the client based on the adaptive profile. A set of offers may be resolved by an offer management engine based on the dialogue. The set of offers may be presented to the client.

Abstract: Disclosed aspects relate to managing a set of offers using a dialogue. An adaptive profile may be received with respect to a client. The adaptive profile may indicate a set of client profile data, a set of client event data, and a set of client context data. A dialogue may be established with the client based on the adaptive profile. A set of offers may be resolved by an offer management engine based on the dialogue. The set of offers may be presented to the client.

Abstract: A resource reservoir is provided. The resource reservoir is a pool of resources that are allocated to and shared between a plurality of reserve virtual machines. A reserve virtual machine of the plurality of reserve virtual machines corresponds to a primary virtual machine from a plurality of primary virtual machines. Each reserve virtual machine of the plurality of reserve virtual machines fulfills a resource requirement from the resource reservoir. The sum of the resources requirements of the plurality of reserve virtual machines is greater than a sum of total resources within the resource reservoir. In response to determining a decrease in performance of the primary virtual machine, at least a portion of workload of the primary virtual machine is applied to the corresponding reserve virtual machine. The resources used by the reserve virtual are indicated as unavailable to the remaining reserve virtual machines from the resource reservoir.