Abstract: A database management engine provides a user interface that allows users to access and modify employee information in a database. The database includes entries for employees, and each database entry includes identifying information about the associated employee. A user can request to modify data within database entries, for instance in order to update information associated with an employee. Responsive to the request, the database management engine identifies liabilities associated with the database modification stemming from associated tax laws. Based on the identified tax liabilities, the engine computes the aggregate tax liability owed by the employer and/or employee. Before modifying a database entry, the engine modifies the user interface to include interface elements detailing the computed aggregate tax liability. The user explicitly can be required to confirm the database modification in view of the aggregate tax liability.

Abstract: A compressor system includes a compression module having an inlet for receiving air; and a filter housing in fluid communication with the compression module. The filter housing is constructed to house a filter, the filter having a sealing feature disposed at a first end of the filter and having an engagement feature disposed at a second end of the filter opposite the first end. The filter housing includes a trap door constructed to contact the engagement feature, and constructed to drive the engagement feature in a direction parallel to an axis of the filter toward the sealing feature and urge the sealing feature into sealing engagement with the filter housing.

Abstract: Disclosed are examples of systems, apparatus, methods and computer program products for sharing and publishing files. In one aspect, the database system can maintain a user database, a file database and a library. The database system can receive a first request initiated by a first user to share a first file with one or more second users and, responsive to the first request, enable a second set of one or more permissions for each of the second users. The database system also can receive a second request initiated by the first user to publish the first file to the library and, responsive to the second request, publish the first file to the library. The database system additionally can restrict access to the published file based on permissions associated with the library.

Abstract: Systems and methods are directed to improvements for secure communications between client systems and a vehicle integration platform associated with a service provider entity. In one example, a communication infrastructure is provided which includes a vehicle integration platform that includes a plurality of application programming interfaces configured to facilitate communication among clients. The communication infrastructure includes a security integration system which is configured to receive and validate a client certificate forwarded to the vehicle integration platform from a client and determine an identity of the client and an origin of a request associated with the client certificate.

Abstract: Systems and methods use sound waves for evaluating a fuel. The fuel supplied from a storage tank to an engine by a feed pipe can be evaluated by determining its properties based on the velocity of one or more sound waves in the fuel.

Abstract: Provided is a combustor of a gas turbine including a combustion liner where combustion occurs, a transition piece connected to the combustion liner to allow combustion gases to pass through, and a honeycomb seal ring disposed between the combustion liner and the transition piece to prevent compressed air from leaking through a gap between the combustion liner and the transition piece. The honeycomb seal ring is inserted into the exit portion of the combustion liner to prevent compressed air from leaking when the combustion liner and the transition piece are coupled with each other.

Abstract: Disclosed is a technique for providing one or more virtual machines or one or more software containers provided by cloud services to manage a horticultural operation. The techniques include transmitting, from the image dispatcher service, the image data to a first computing instance that executes a first subprocess of an image processing pipeline. Using the first subprocess of the image processing pipeline, partially processed image data is generated from the image data. The partially processed image data resulting from the first subprocess is then transmitted from the first computing instance to a second computing instance that executes a second subprocess of the image processing pipeline. Thereafter, a fully image processed image associated with the image data is produced via at least the second subprocess of the image processing pipeline.

Abstract: A process for manufacturing an optoelectronic device having a diode matrix with semiconductor stacks involves providing a growth substrate having a support substrate coated with a nucleation layer defining a nucleation surface. A dielectric layer is deposited on the nucleation surface. A plurality of through-holes, extending to the nucleation surface, are formed in the dielectric layer. The nucleation layer, located in the through-holes, is etched to free up an upper surface of the support surface and expose a lateral surface of the nucleation layer forming a lateral nucleation surface. A dielectric region is formed extending in the support substrate such that, during a subsequent epitaxial growth stage, each first doped portion is formed especially from the lateral nucleation surface. In the through-holes and from the nucleation surface, the semiconductor stacks are epitaxially grown such that at least the first doped portions and active zones thereof are located in the through-holes.

Abstract: A database management engine provides a user interface that allows users to access and modify employee information in a database. The database includes entries for employees, and each database entry includes identifying information about the associated employee. A user can request to modify data within database entries, for instance in order to update information associated with an employee. Responsive to the request, the database management engine identifies liabilities associated with the database modification stemming from associated tax laws. Based on the identified tax liabilities, the engine computes the aggregate tax liability owed by the employer and/or employee. Before modifying a database entry, the engine modifies the user interface to include interface elements detailing the computed aggregate tax liability. The user explicitly can be required to confirm the database modification in view of the aggregate tax liability.

Abstract: A method of forming a tube assembly includes forming arms on a tube end portion by forming a plurality of slots or slits on such tube end and mating such arms with a tapered outer surface of a fitting. The fitting may include at least one annular step mated with an annular step formed on the tube inner surface.

Abstract: Provisioning a cloud based high performance computing cluster. The method includes from a cloud based provisioning service deployed in a cloud based computing provider, providing a user interface. The method further includes receiving user input at the cloud based provisioning service from the user interface. The method further includes from the user input, determining a configuration for a cloud based user service for the user. Using the cloud based provisioning service, the method further includes provisioning the cloud based user service for the user, in the cloud, based on the determined configuration.

Abstract: A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.

Abstract: The present disclosure relates to systems and methods for a machine-learning platform for the safe serialization of a machine-learning application. Individual library components (e.g., a pipeline, a microservice routine, a software module, and an infrastructure model) can be encrypted using one or more keys. The keys can be stored in a location different from the storage location of the machine-learning application. Prior to incorporation of the library component into a machine-learning model, one or more keys can be retrieved from the remote storage location to authenticate that the one or more encrypted library components are authentic. The process can reject any of the one or more component, when the encrypted library component fails authentication. If a component is rejected, the system can roll back to a previous, authenticated version of the library component. The authenticated library components can be compiled into machine-learning software.

Abstract: The present disclosure relates to systems and methods for a self-adjusting corporation-wide discovery and integration feature of a machine learning system that can review a client's data store, review the labels for the various data schema, and effectively map the client's data schema to classifications used by the machine learning model. The various techniques can automatically select the features that are predictive for each individual use case (i.e., one client), effectively making a machine learning solution client-agnostic for the application developer. A weighted list of common representations of each feature for a particular machine learning solution can be generated and stored. When new data is added to the data store, a matching service can automatically detect which features should be fed into the machine-learning solution based at least in part on the weighted list. The weighted list can be updated as new data is made available to the model.

Abstract: The present disclosure relates to systems and methods for using existing data ontologies for generating machine learning solutions for a high-precision search of relevant services to compose pipelines with minimal human intervention. Data ontologies can be used to create a combination of non-logic based and logic-based sematic services that can significantly outperform both kinds of selection in terms of precision. Quality of Service (QoS) and product Key Performance Indicator (KPI) constraints can be used as part of architecture selection in developing, training, validating, and improving machine learning models. For data sets without existing ontologies, one or more ontologies be generated and stored for future use.

Abstract: A server system may match a segment of code for a code integration request to metadata about similar segments of code, wherein the metadata qualifies one or more outcomes of previous integration requests. The server may determine usage rights and rules based on the metadata, wherein some of the usage rights and rules have previously have been approved by a multi-approval workflow that enforces a predetermined process to authorize use of the segment of code for code segment integrations. The server may analyze the metadata to predict an integration score based at least in part on the usage rights and rules of the segments of code. If the integration score of the segment of code exceeds a threshold, the system may automatically generate a data structure for deploying the segment of code, wherein the automatically generating the data structure is performed without the multi-approval workflow.

Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Abstract: A server system can receive an input identifying a problem to generate a solution using a machine-learning application. The method selects a machine-learning model template from a plurality of templates based at least in part on the input. The method analyzes one or more formats of the customer data to generate a customer data schema based at least in part a data ontology that applies to the identified problem. The method determines whether the customer data schema is misaligned with one or more key features of the selected machine-learning model template. Based on this determination, the method analyzes the metadata for the selected machine-learning model template to determine what additional information is required to re-align the customer data with the data expectations. The method can include gathering the addition information required to re-align the customer data with the data expectations of the selected machine-learning model template.

Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.

Abstract: The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.