Abstract: Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Abstract: Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Abstract: The behavior of a machine learning model and the training dataset used to train the model are monitored to determine when the accuracy of the model's predictions indicate that the model should be retrained. The retraining is determined from one or more precision metrics and a coverage metric that are generated during operation of the model. A precision metric measures the ability of the model to make predictions that are accepted by an inference system and the coverage metric measures the ability of the model to make predictions given a set of input features. In addition, changes made to the training dataset are analyzed and used as an indication of when the model should be retrained.

Abstract: A collaboration session is provided in which an owner computer system and a participant computer system are both members. While working within this session, the participant computer system is provided access to a multi-file workspace that is stored locally on the owner computer system. The owner computer system receives a request from the participant computer system. The request is used to gain access to the owner computer system's language service. In response to this request, the owner computer system remotes its language service so that the language service is accessible to the participant computer system.

Abstract: Improved techniques for asynchronously displaying the results of a codebase analysis service are provided herein. Initially, machine learning is applied to a corpus of model code. In doing so, a machine learning model is generated, where the model identifies coding practices that are included in the corpus of model code. After this model is generated, then the model is applied to a current codebase by comparing coding practices of the current codebase to the identified coding practices that were extracted, or rather identified, from the corpus of model code. Then, in response to detecting one or more differences between the current codebase's coding practices and the identified coding practices, where the differences satisfy a pre-determined difference threshold, a user interface is caused to display one or more insights. These insights beneficially provide additional detailed information describing the differences.

Abstract: A collaboration session is provided in which an owner computer system and a participant computer system are both members. Within this collaboration session, both the owner and the participant computer systems are provided access to a multi-file workspace's build instance. Here, the multi-file workspace and the build instance are both stored locally on the owner computer system. Further, this workspace includes multiple files of source code. As a result, the build instance is a build of that source code. Various debug commands that are directed to the build instance may be received. Some of these commands originate from the owner computer system while others originate from the participant computer system. These debug commands are then multiplexed, and the build instance is executed in accordance with the multiplexed debug commands. As a result of executing the build instance, debugging data is generated.

Abstract: Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Abstract: A method for diagnosing computer readable instructions related to transfers of control is disclosed. A state transition of a unit of execution within a logical operation providing an event handler for a Document Object Model is logged during runtime of the computer readable instructions. Diagnostic information of the logical operation is associated with the state transition. The state transition and associated diagnostic information is provided for retrieval at a selected point in the computer readable instructions.

Abstract: Improving how a codebase, which may include source code, related databases, test files, code history, and/or changes, is drafted, edited, debugged, or otherwise developed. Machine learning is performed on a model codebase to establish a machine learning model. When a change to a codebase occurs, the machine learning model may be applied to evaluate that change. A change context providing context for this change is accessed. An analyzer then analyzes the change using the machine learning model and at least a part of the change context to generate an analysis result. Some information about the result is rendered. After rendering that information, a determination regarding how a user responded to the information is performed, and a subsequent analysis is then modified based on the user's response.

Abstract: Improved techniques for asynchronously displaying the results of a codebase analysis service are provided herein. Initially, machine learning is applied to a corpus of model code. In doing so, a machine learning model is generated, where the model identifies coding practices that are included in the corpus of model code. After this model is generated, then the model is applied to a current codebase by comparing coding practices of the current codebase to the identified coding practices that were extracted, or rather identified, from the corpus of model code. Then, in response to detecting one or more differences between the current codebase's coding practices and the identified coding practices, where the differences satisfy a pre-determined difference threshold, a user interface is caused to display one or more insights. These insights beneficially provide additional detailed information describing the differences.

Abstract: Improving how a codebase, which may include source code, related databases, test files, code history, and/or changes, is drafted, edited, debugged, or otherwise developed. Machine learning is performed on a model codebase to establish a machine learning model. When a change to a codebase occurs, the machine learning model may be applied to evaluate that change. A change context providing context for this change is accessed. An analyzer then analyzes the change using the machine learning model and at least a part of the change context to generate an analysis result. Some information about the result is rendered. After rendering that information, a determination regarding how a user responded to the information is performed, and a subsequent analysis is then modified based on the user's response.

Abstract: A collaboration session is provided in which an owner computer system and a participant computer system are both members. Within this session, the collaborators are provided access to a multi-file workspace that is stored locally on the owner computer system. Initially, a set of development tools are identified. These tools are hosted by the owner computer system and are able to operate on the workspace's files. After the tools are identified, they are made accessible to the participant computer system. Later, a request is received from the participant computer system. In some instances, the request is directed to a particular file within the multi-file workspace and is generated using one of the development tools. In this manner, the collaboration session enables the owner computer system's development tools to become accessible to the participant computer system.

Abstract: An application may be built on an owner computer system, and that build may be saved on the owner computer system. Then, an instance of the build may be run on the owner computer system. A collaboration session in which the owner computer system and a participant computer system are both members may also be established. Within this collaboration session, the participant computer system is provided access to a port that may be used to access the build instance. Next, this build instance is made accessible to the participant computer system via the port. In response to a change to the build, the participant computer system is then provided access to a new instance of the build via the port.

Abstract: A collaboration session is provided in which an owner computer system and a participant computer system are both members. While working within this session, the participant computer system is provided access to a multi-file workspace that is stored locally on the owner computer system. The owner computer system receives a request that is directed to the multi-file workspace. Here, the request originates from the participant computer system. In response to the request, the owner computer system handles the request and then returns a response to the participant computer system.

Abstract: A collaboration session is provided in which an owner computer system and a participant computer system are both members. While working within this session, the participant computer system is provided access to a multi-file workspace that is stored locally on the owner computer system. The owner computer system receives a request from the participant computer system. The request is used to gain access to the owner computer system's language service. In response to this request, the owner computer system remotes its language service so that the language service is accessible to the participant computer system.

Abstract: A collaboration session is provided in which an owner computer system and a participant computer system are both members. Within this collaboration session, both the owner and the participant computer systems are provided access to a multi-file workspace's build instance. Here, the multi-file workspace and the build instance are both stored locally on the owner computer system. Further, this workspace includes multiple files of source code. As a result, the build instance is a build of that source code. Various debug commands that are directed to the build instance may be received. Some of these commands originate from the owner computer system while others originate from the participant computer system. These debug commands are then multiplexed, and the build instance is executed in accordance with the multiplexed debug commands. As a result of executing the build instance, debugging data is generated.

Abstract: A method for diagnosing computer readable instructions related to transfers of control is disclosed. A state transition of a unit of execution within a logical operation providing an event handler for a Document Object Model is logged during runtime of the computer readable instructions. Diagnostic information of the logical operation is associated with the state transition. The state transition and associated diagnostic information is provided for retrieval at a selected point in the computer readable instructions.

Abstract: A method for diagnosing computer readable instructions related to transfers of control is disclosed. A state transition of a unit of execution within a logical operation is logged during runtime of the computer readable instructions. Diagnostic information of the logical operation is associated with the state transition. The state transition and associated diagnostic information is provided for retrieval at a selected point in the computer readable instructions.

Abstract: In a combined MRI and radiation therapy system, a magnet structure and radiation therapy equipment are provided. The magnet structure comprises a single substantially cylindrical field coil structure comprising a number of superconducting coils joined by a support structure and extending axially of a central region. An outer vacuum chamber encloses the field coil structure in an evacuated volume. A cooling arrangement comprising cooling tubes is in thermal contact with the superconducting coils and receives a cryogen flowing through the cooling tubes. The radiation therapy equipment comprises a gamma radiation source rotatable about an axis of the field coil structure to direct a radiation beam substantially radially through the field coil structure.

Abstract: A cylindrical superconducting magnet has a number of axially-aligned annular coils of superconducting wire, arranged for cooling by thermal conduction through a cooled surface in mechanical contact with the coils. The coils are provided with a cryogenic radiation shield located between respective radially inner surfaces of the coils and respective axes of the coils. The cryogenic radiation shield is formed of a metal layer in thermal contact with the cooled surface.