Abstract: A cotton topping unmanned aerial vehicle having cutter discs and front grain lifting baffle plates is provided, the unmanned aerial vehicle includes an unmanned aerial vehicle body, a carrying component, a grain lifting component, and a cutting component. The carrying component includes a suspension, an electric push rod mounting base, an electric push rod, a motor mounting base, and motors. An upper end of the electric push rod is bolted to the electric push rod mounting base. The motor mounting base is welded to a lower end of the electric push rod. The grain lifting component is grain lifting baffle plates which include a left baffle plate and a right baffle plate, and both the left baffle plate and the right baffle plate are welded to guard plates.

Abstract: An oil management controller for heating, ventilation, or air conditioning (HVAC) equipment. The controller includes a processing circuit. The processing circuit is configured to analyze operating data for the HVAC equipment using a machine learning model to predict a variable state or condition of oil used by the HVAC equipment. The processing circuit is configured to identify an oil deficiency based on the variable state or condition of the oil. The processing circuit is configured to automatically initiate a corrective action responsive to identifying the oil deficiency.

Abstract: A method comprising receiving data associated with a business, the data comprising first values for first attributes; processing the data, in accordance with a common data attribute schema that indicates second attributes, to generate second values for at least some of the second attributes including a group of attributes, the second values including a group of attribute values for the group of attributes; identifying, using the common data attribute schema and from among pre-existing software codes, software code implementing an ML data processing pipeline configured to generate a group of feature values; processing the group of attribute values with the software code to obtain the group of feature values; and either providing the group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or using the group of feature values to train the ML model.

Abstract: A method comprising: obtaining information about training data comprising first inputs and first outputs, comprising a first representation of a first distribution of the first inputs and first performance data indicative of a measure of performance of a trained machine learning (ML) model on the first inputs; obtaining information about new data comprising second inputs and second outputs, comprising a second representation of a second distribution of the second inputs and second performance data indicative of the measure of performance of the trained ML model on the second inputs; determining, using the first representation, the second representation, the first performance data, and the second performance data, whether to update the trained ML model or to generate a supplemental ML model; and when it is determined to update the trained ML model or to generate the supplemental ML model, updating the trained ML model or generating the supplemental ML model.

Abstract: A method comprising receiving data associated with a business, the data comprising first values for first attributes; processing the data, in accordance with a common data attribute schema that indicates second attributes, to generate second values for at least some of the second attributes including a group of attributes, the second values including a group of attribute values for the group of attributes; identifying, using the common data attribute schema and from among pre-existing software codes, software code implementing an ML data processing pipeline configured to generate a group of feature values; processing the group of attribute values with the software code to obtain the group of feature values; and either providing the group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or using the group of feature values to train the ML model.

Abstract: The present invention discloses a digital photolithography method for a fiber optic device (FOD) based on a DMD combination. In this method, reflected light modulated by two DMDs is simultaneously projected onto a same position on an optical fiber end surface through one reduction projection lens. The two DMDs form a primary and secondary digital mask for joint control of an exposure dose distribution formed when patterns are shrunk and projected onto the optical fiber end surface. After the optical fiber end surface coated with photoresist is subject to this dose of exposure, developing, fixing, and etching are conducted, to form a micro-optic device on the optical fiber end surface. In the present invention, distribution of the exposure dose jointly modulated by a digital mask combination formed by the primary and secondary DMD exceeds an order of modulation of an exposure dose by a single DMD.

Abstract: The present invention discloses a digital photolithography method for a fiber optic device (FOD) based on a DMD combination. In this method, reflected light modulated by two DMDs is simultaneously projected onto a same position on an optical fiber end surface through one reduction projection lens. The two DMDs form a primary and secondary digital mask for joint control of an exposure dose distribution formed when patterns are shrunk and projected onto the optical fiber end surface. After the optical fiber end surface coated with photoresist is subject to this dose of exposure, developing, fixing, and etching are conducted, to form a micro-optic device on the optical fiber end surface. In the present invention, distribution of the exposure dose jointly modulated by a digital mask combination formed by the primary and secondary DMD exceeds an order of modulation of an exposure dose by a single DMD.

Abstract: A processing unit can acquire datasets from respective data sources, each having a respective unique data domain. The processing unit can determine values of a plurality of features based on the plurality of datasets. The processing unit can modify input-specific parameters or history parameters of a computational model based on the values of the features. In some examples, the processing unit can determine an estimated value of a target feature based at least in part on the modified computational model and values of one or more reference features. In some examples, the computational model can include neural networks for several input sets. An output layer of at least one of the neural networks can be connected to the respective hidden layer(s) of one or more other(s) of the neural networks. In some examples, the neural networks can be operated to provide transformed feature value(s) for respective times.

Abstract: A processing unit can acquire datasets from respective data sources, each having a respective unique data domain. The processing unit can determine values of a plurality of features based on the plurality of datasets. The processing unit can modify input-specific parameters or history parameters of a computational model based on the values of the features. In some examples, the processing unit can determine an estimated value of a target feature based at least in part on the modified computational model and values of one or more reference features. In some examples, the computational model can include neural networks for several input sets. An output layer of at least one of the neural networks can be connected to the respective hidden layer(s) of one or more other(s) of the neural networks. In some examples, the neural networks can be operated to provide transformed feature value(s) for respective times.

Abstract: An object oriented search mechanism extracts structural metadata and data based on type of document contents and data sources connected to the documents. Relationships between textual and non-textual elements within documents as well as metadata associated with the elements and data sources are utilized to generate a unified object model with the addition of semantic information derived from metadata and taxonomy, which are used to enhance search indexing, ranking of search results, and dynamic adjustment of result rendering user interface with fine tuned relevancy. Additional data from data sources connected to the documents may also be used to unlock hidden data such as data that has been filtered out in an original document.

Abstract: Technologies are described herein for defining the scope of a sub-cube based upon a database query. A request is received to perform a query of a multi-dimensional database, such as a cube, stored at a server computer. In response to the request, the scope for a sub-portion, called a sub-cube, of the multi-dimensional database is determined based upon the query. The scope of the sub-cube may be determined by identifying the minimum sub-portion of the cube necessary to perform the query. Once the scope has been determined, the sub-cube is generated at the server computer and transmitted to a client computer. The client computer stores the sub-cube locally and performs the requested database query on the sub-cube stored at the client computer. A user interface may be provided through which a user can modify the scope of the sub-cube determined based upon the query.

Abstract: An object oriented search mechanism extracts structural metadata and data based on type of document contents and data sources connected to the documents. Relationships between textual and non-textual elements within documents as well as metadata associated with the elements and data sources are utilized to generate a unified object model with the addition of semantic information derived from metadata and taxonomy, which are used to enhance search indexing, ranking of search results, and dynamic adjustment of result rendering user interface with fine tuned relevancy. Additional data from data sources connected to the documents may also be used to unlock hidden data such as data that has been filtered out in an original document.

Abstract: Relationships between linked and/or embedded documents as well as documents sharing data source(s) are captured and rendered through virtual documents. Virtual documents are created representing linked/embedded documents and data sources associated with a relevant document. Relationships between real and virtual documents are preserved and rendered along with search results providing a user a comprehensive picture of search results.

Abstract: Data processing architecture where submitters can write data and/or metadata changes to a destination through an asynchronous interface. The architecture includes a ticketing system that issues a ticket (a unique identifier) to the submitter in response to receiving a changelist from the submitter. When the changes are successfully completed at the destination, the cached destination data can be re-downloaded to the submitter, and the changes cached in the submitter, discarded. Absent this capability, a user has to manually manage the cached changes and cached data. The architecture can be distributed in that multiple submitters can submit changelists. The destination provides data integrity by managing submissions to assist in ensuring data integrity across multiple submissions. Automatic changelist cleanup is provided on the submitter so that changelists do not impact performance, memory, and storage.

Abstract: An object oriented search mechanism extracts structural metadata and data based on type of document contents and data sources connected to the documents. Relationships between textual and non-textual elements within documents as well as metadata associated with the elements and data sources are utilized to generate a unified object model with the addition of semantic information derived from metadata and taxonomy, which are used to enhance search indexing, ranking of search results, and dynamic adjustment of result rendering user interface with fine tuned relevancy. Additional data from data sources connected to the documents may also be used to unlock hidden data such as data that has been filtered out in an original document.

Abstract: An interface for designing a report is provided, with elements of the report being disposed at intersections of report positions and configured to present information derived from data in a database. A formula is associated with one or more selected elements in the report to generate a result for the one or more selected elements. A tuple identifying the report positions of the one or more selected elements is associated and stored with the formula in a formula list associated with the report. The report is configured so that, when the report is generated, it is determined whether identifying report positions corresponding to an identified element in the report correspond with the tuple in the formula list. Upon determining the tuple corresponds with the identifying report positions for the identified element, the formula associated with the tuple is applied to generate the result.

Abstract: Data processing architecture where submitters can write data and/or metadata changes to a destination through an asynchronous interface. The architecture includes a ticketing system that issues a ticket (a unique identifier) to the submitter in response to receiving a changelist from the submitter. When the changes are successfully completed at the destination, the cached destination data can be re-downloaded to the submitter, and the changes cached in the submitter, discarded. Absent this capability, a user has to manually manage the cached changes and cached data. The architecture can be distributed in that multiple submitters can submit changelists. The destination provides data integrity by managing submissions to assist in ensuring data integrity across multiple submissions. Automatic changelist cleanup is provided on the submitter so that changelists do not impact performance, memory, and storage.

Abstract: Techniques to manage information for dynamic reports are described. An apparatus may comprise a business planning application module to update a current period reference to a time member of a time dimension, generate a rolling data entry form with actual data members and forecast data members based on the current period reference, and set a data locking mode for the actual data members and a data access mode for the forecast data members. Other embodiments are described and claimed.

Abstract: Data processing architecture where submitters can write data and/or metadata changes to a destination through an asynchronous interface. The architecture includes a ticketing system that issues a ticket (a unique identifier) to the submitter in response to receiving a changelist from the submitter. When the changes are successfully completed at the destination, the cached destination data can be re-downloaded to the submitter, and the changes cached in the submitter, discarded. Absent this capability, a user has to manually manage the cached changes and cached data. The architecture can be distributed in that multiple submitters can submit changelists. The destination provides data integrity by managing submissions to assist in ensuring data integrity across multiple submissions. Automatic changelist cleanup is provided on the submitter so that changelists do not impact performance, memory, and storage.

Abstract: Technologies are described herein for defining the scope of a sub-cube based upon a database query. A request is received to perform a query of a multi-dimensional database, such as a cube, stored at a server computer. In response to the request, the scope for a sub-portion, called a sub-cube, of the multi-dimensional database is determined based upon the query. The scope of the sub-cube may be determined by identifying the minimum sub-portion of the cube necessary to perform the query. Once the scope has been determined, the sub-cube is generated at the server computer and transmitted to a client computer. The client computer stores the sub-cube locally and performs the requested database query on the sub-cube stored at the client computer. A user interface may be provided through which a user can modify the scope of the sub-cube determined based upon the query.