Abstract: A method list is built for a currently executing application within a process virtual machine at a snapshot point, the method list comprising a set of methods capable of being executed by the currently executing application after the snapshot point, the snapshot point comprising an execution state of the currently executing application when a snapshot process is triggered. Profiling data of the currently executing application, collected prior to reaching the snapshot point, is committed, to a designated storage location. Using the profiling data and a just-in-time compiler of the process virtual machine, a method in the method list is compiled. Snapshot data comprising data of the execution state of the currently executing application at the snapshot point, including a result of the compiling, is stored.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for creating visualizations to explain the outputs produced by models designed for object detection. To accomplish this, a graphics editing platform can obtain a reference output that identifies a region of pixels in a digital image that allegedly contains an object. Then, the graphics editing platform can compute the similarity between the reference output and a series of outputs generated by a model upon being applied to masked versions of the digital image. A visualization component can be produced based on the similarity.

Abstract: Impedance matching in tunable circuits promotes consistent, high-efficiency wireless communication signal despite changing environmental conditions.

Abstract: A method and system for self-regulating memory of a JAVA virtual machine optimizes memory use by the JVM and by an operating system. A computer running a garbage collector extension monitors and records free committed memory of the JVM at predetermined intervals over time to define a historical record. The computer calculates an average allocation of the free committed memory in the historical record over predetermined intervals. An allocation rate is determined, wherein the allocation rate is based on the average allocation over the predetermined intervals. The computer calculates an estimated time to exhaust committed memory based on free committed heap memory and the allocation rate. Memory is recovered from the operating system if the time to exhaust committed memory is below a first predetermined threshold value and is released to the operating system when said time to exhaust committed memory is above a second predetermined threshold value.

Abstract: Various disclosed embodiments can resolve output inaccuracies produced by many machine learning models. Embodiments use content order as input to machine learning model systems so that they can process documents according to the position or rank of instances in a document or image. In this way, the model is less likely to misclassify or incorrectly detect instances or the ordering between predicted instances. The content order in various embodiments can be used as an additional signal to classify or make predictions.

Abstract: Techniques are disclosed for identifying document structural elements and correcting errors in the classification and/or location of the identified structural elements. An example method includes determining location and classification for a structural element on a page of the document using a machine learning (ML) model; determining one or more errors in the location and/or classification for the structural element; and correcting each instance of the one or more errors using other content in the document (e.g., content spatially adjacent to the corresponding structural element on the page of the document). The method may further include storing the document and the location and classification (as corrected), and/or generating a structural map of the page of the document based on the location and classification (as corrected). The use of the document content to correct errors greatly enhances the agreement between the identified structural elements and the original document.

Abstract: Techniques described herein implement heading identification and classification for a digital document in a digital medium environment. A document analysis system is leveraged to extract structural features from a digital document, identify heading candidates from among the structural features, validate the headings candidates, and classify validated headings into different headings types. The classified headings are then utilized to generate a sectioned version of the digital document (“sectioned document”) that is divided into different sections based on the headings. Further, a document directory is generated that includes the headings and that enables navigation to different sections of the sectioned document.

Abstract: Techniques described herein implement heading identification and classification for a digital document in a digital medium environment. A document analysis system is leveraged to extract structural features from a digital document, identify heading candidates from among the structural features, validate the headings candidates, and classify validated headings into different headings types. The classified headings are then utilized to generate a sectioned version of the digital document (“sectioned document”) that is divided into different sections based on the headings. Further, a document directory is generated that includes the headings and that enables navigation to different sections of the sectioned document.

Abstract: Techniques described herein implement identifying artifacts in digital documents in a digital medium environment. A document analysis system is leveraged to extract page features from a digital document and to determine whether certain page features represent page artifacts such as headers and footers. Those page features determined to be page artifacts can be extracted from the digital document to generate a reflowed version of the digital document that preserves primary content. The primary content, for instance, is rearranged in the reflowed document to compensate for the extracted page artifacts.

Abstract: A method and system for self-regulating memory of a JAVA virtual machine optimizes memory use by the JVM and by an operating system. A computer running a garbage collector extension monitors and records free committed memory of the JVM at predetermined intervals over time to define a historical record. The computer calculates an average allocation of the free committed memory in the historical record over predetermined intervals. An allocation rate is determined, wherein the allocation rate is based on the average allocation over the predetermined intervals. The computer calculates an estimated time to exhaust committed memory based on free committed heap memory and the allocation rate. Memory is recovered from the operating system if the time to exhaust committed memory is below a first predetermined threshold value and is released to the operating system when said time to exhaust committed memory is above a second predetermined threshold value.

Abstract: A method and system for self-regulating memory of a JAVA virtual machine optimizes memory use by the JVM and by an operating system. A computer running a garbage collector extension monitors and records free committed memory of the JVM at predetermined intervals over time to define a historical record. The computer calculates an average allocation of the free committed memory in the historical record over predetermined intervals. An allocation rate is determined, wherein the allocation rate is based on the average allocation over the predetermined intervals. The computer calculates an estimated time to exhaust committed memory based on free committed heap memory and the allocation rate. Memory is recovered from the operating system if the time to exhaust committed memory is below a first predetermined threshold value and is released to the operating system when said time to exhaust committed memory is above a second predetermined threshold value.

Abstract: A method and system for self-regulating memory of a JAVA virtual machine optimizes memory use by the JVM and by an operating system. A computer running a garbage collector extension monitors and records free committed memory of the JVM at predetermined intervals over time to define a historical record. The computer calculates an average allocation of the free committed memory in the historical record over predetermined intervals. An allocation rate is determined, wherein the allocation rate is based on the average allocation over the predetermined intervals. The computer calculates an estimated time to exhaust committed memory based on free committed heap memory and the allocation rate. Memory is recovered from the operating system if the time to exhaust committed memory is below a first predetermined threshold value and is released to the operating system when said time to exhaust committed memory is above a second predetermined threshold value.

Abstract: Garbage collection processes are automatically tuned on a system comprising a plurality of virtual machines running on a physical processing device with multiple physical processors. Tuning is implemented for each garbage collection process, by updating a load variable to reflect that a new garbage collection process has started, the load variable having a value that is based at least in part on a number of garbage collection processes that are currently running on the system. Tuning further comprises creating a number of garbage collection threads based at least in part on the current value of the load variable and/or number of processors, where the garbage collection threads are used to perform at least a portion of at least one phase of the new garbage collection process. Tuning further comprises re-updating the load variable to reflect completion of the new garbage collection process.

Abstract: Garbage collection processes are automatically tuned on a system comprising a plurality of virtual machines running on a physical processing device with multiple physical processors. Tuning is implemented for each garbage collection process, by updating a load variable to reflect that a new garbage collection process has started, the load variable having a value that is based at least in part on a number of garbage collection processes that are currently running on the system. Tuning further comprises creating a number of garbage collection threads based at least in part on the current value of the load variable and/or number of processors, where the garbage collection threads are used to perform at least a portion of at least one phase of the new garbage collection process. Tuning further comprises re-updating the load variable to reflect completion of the new garbage collection process.