Abstract: Methods and systems for structuring, storing and displaying time series data in a user interface. One system includes processors executing instructions to determine, from time series data from a first sensor, a first subset of time series data for the first batch from the first start time and the first end time, determine, from the time series data from the first sensor, a second subset of time series data for the second batch from the second start time and the second end time, generate a time series user interface comprising a chart, the chart including a first plot for the first subset of time series data and a second plot for the second subset of time series data, the first plot being aligned to the second plot, and cause presentation of the time series user interface.

Abstract: Implementations are described herein for leveraging an agricultural knowledge graph to generate messages automatically. In various implementations, an agricultural event may trigger proactive performance of one or more of the following operations. Nodes of the agricultural knowledge graph may be identified as related to the agricultural event, including field node(s) representing subject agricultural field(s) to which the agricultural event is relevant and other node(s) connected to one or more of the field nodes by edge(s). Machine learning model(s) may be accessed based on the identified nodes and/or the edges that connect the identified nodes. Data relevant to the subject agricultural field(s) may be retrieved from data source(s) controlled by an agricultural entity and processed based on the machine learning model(s) to generate inference(s) about the subject agricultural field(s).

Abstract: The present disclosure provides methods for treating hematological malignancies and Ewings sarcoma using menin inhibitors. Compositions for use in these methods are also provided.

Abstract: Systems, devices and methods are provided for building learning machines using learning machines. The system generally includes a reference learning machine, a target learning machine being built, a component analyzer module configured to analyze inputs from the reference learning machine, the target learning machine, a set of test signals, and a list of components in the reference learning machine and the target learning machine, and return a set of output values for each component on the list of components. The system further includes a component tuner module configured to modify different components in the target learning machine based on the set of output values and a component mapping, thereby resulting in a tuned learning machine.

Abstract: Systems, devices and methods are provided for building learning machines using learning machines. The system generally includes a reference learning machine, a target learning machine being built, a component analyzer module configured to analyze inputs from the reference learning machine, the target learning machine, a set of test signals, and a list of components in the reference learning machine and the target learning machine, and return a set of output values for each component on the list of components. The system further includes a component tuner module configured to modify different components in the target learning machine based on the set of output values and a component mapping, thereby resulting in a tuned learning machine.

Abstract: Systems, devices and methods are provided for building learning machines using learning machines. The system generally includes a reference learning machine, a target learning machine being built, a component analyzer module configured to analyze inputs from the reference learning machine, the target learning machine, a set of test signals, and a list of components in the reference learning machine and the target learning machine, and return a set of output values for each component on the list of components. The system further includes a component tuner module configured to modify different components in the target learning machine based on the set of output values and a component mapping, thereby resulting in a tuned learning machine.

Abstract: Described are methods and apparatuses, including computer program products, for quality control of investment performance calculations. Performance data associated with one or more investment accounts is received by a server computing device. A tolerance range associated with the performance data is generated. A portion of the performance data is identified for filtering based on the tolerance range. The identified performance data is processed through a plurality of filters, wherein the filters are based on predefined rules. A first portion of the filtered data is marked as requiring no further review. A second portion of the filtered data is marked as requiring further review.

Abstract: The invention discloses a system for enterprise service flows. The system comprises a database, a user interface and a processing module. The database records a plurality of enterprise resources and a plurality of service flow programs, in addition, the service flow programs correlate with at least one of the plurality of enterprise resources. The user interface is used for a user to select at least one enterprise resource; the processing module further comprises a flow management unit. The processing module performs correlative link based on the selected enterprise resource, the flow management unit of the processing module arranges the combination of the plurality of enterprise resources in correspondence to the plurality of service flow programs. Consequently, an enterprise is able to dynamically increase its entire competitiveness based on its service flows required.

Abstract: The present invention discloses a process-controlled enterprise resource planning system (herein referred to as a PERP) that features an operational interface allows an operator to operate a required process in at least one start process interface or a start enterprise resource management interface. If the operator selects the start process interface, a process processing unit controls successive operational actions required the operator to operate the operational interface according to an event control process or the process-step control information corresponding to the start process interface. If the operator selects the enterprise resource management interface, an enterprise resource management processing unit controls management and process of the enterprise resource database according to the process-step control information corresponding to the start enterprise resource management interface. Thereupon, operational mistakes can be prevented and time-consumption for training the operator can be reduced.

Abstract: A program distribution system includes a plurality of set-top boxes that receive broadcast programming and segmentation data from content and information providers. The segmentation information indicates portions of programs that are to be included in skimmed or condensed versions of the received programming, and is produced using manual or automated methods. Automated methods include the use of ancillary production data to detect the most important parts of a program. A user interface allows a user to control time scale modification and skimming during playback, and also allows the user to easily browse to different points within the current program.