Abstract: A method of providing a machine learning model (MLM) for use in a process which uses a ML algorithm includes receiving image sensor data and performing processing modules. A first module includes receiving the sensor data, determining a source, parsing source information, converting the sensor data into data items, associating them with meta data information, and ingesting them with associated meta data information. The second module includes accessing a data warehouse and identifying an image keyframe per data item, and associating information indicative of the image keyframe with the respective data item. The third module includes presenting the image keyframe and associated information of the respective data item via an HMI for annotation, receiving an annotation and augmenting the image keyframe. The fourth module includes generating and/or updating a MLM using an annotated dataset, and the fifth module includes uploading the MLM as a task-specific MLM to a storage device.

Abstract: Method for automatic focusing and astigmatism correction for a specific microscope setup, in particular an electron microscope, the microscope being at least adjustable in microscope parameters a working distance, a stigmator in a x-direction and a stigmator in a y-direction, the method comprising the steps: a) capturing a first image of a sample with a first working distance perturbation and capturing a second image of the sample with a second working distance perturbation around a current working distance and current stigmator settings; b) selecting n subareas of the first image and n subareas of the second image, n?1, wherein an i-th subarea of the first image and an i-th subarea of the second image form an i-th input patch pair, 1?i?n; c) processing each i-th input patch pair and receiving an i-th correction term comprising a correction to the current working distance, stigmator in x-direction and stigmator in y-direction; d) receiving an output correction term as a function of all the correction terms; e

Abstract: A method of providing a machine learning model (MLM) for use in a process which uses a ML algorithm includes receiving image sensor data and performing processing modules. A first module includes receiving the sensor data, determining a source, parsing source information, converting the sensor data into data items, associating them with meta data information, and ingesting them with associated meta data information. The second module includes accessing a data warehouse and identifying an image keyframe per data item, and associating information indicative of the image keyframe with the respective data item. The third module includes presenting the image keyframe and associated information of the respective data item via an HMI for annotation, receiving an annotation and augmenting the image keyframe. The fourth module includes generating and/or updating a MLM using an annotated dataset, and the fifth module includes uploading the MLM as a task-specific MLM to a storage device.

Abstract: Embodiments of the disclosure provide systems and methods for performing a rapid failover in a cloud-based service with reduced interruption of ongoing communications. According to embodiments described herein, independent arbitration between servers can be used to indicate to a load balancer which of two or more servers should be considered the active one. This independent arbitration makes better state of health assessments than simple server failure. The load balancer can be explicitly controlled in this case. The health of individual containers, the completeness of the shadowed state as well as server health itself can be factored into the decision to make one server active over the other.

Abstract: Embodiments of the disclosure provide systems and methods for performing a rapid failover in a cloud-based service with reduced interruption of ongoing communications. According to embodiments described herein, independent arbitration between servers can be used to indicate to a load balancer which of two or more servers should be considered the active one. This independent arbitration makes better state of health assessments than simple server failure. The load balancer can be explicitly controlled in this case. The health of individual containers, the completeness of the shadowed state as well as server health itself can be factored into the decision to make one server active over the other.