Abstract: A control system for use with an adjustable bone fixation device having a frame connectible to bone tissue and a plurality of struts connected to the frame, the control system comprising: a plurality of actuators associated with the plurality of struts, each actuator configured for adjusting a length of at least one strut; and circuitry configured to: signal the plurality of actuators to adjust the struts according to a predefined actuation plan which sets operational parameters and permitted ranges thereof; determine a situation of potential failure associated with at least one actuator and involving at least one of the operational parameters being out of the permitted range; and implement, by signaling the at least one actuator, a recovery protocol designed to attempt completion of strut adjustment according to the predefined actuation plan.

Abstract: Systems and methods are provided to determine a time to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a model or pattern. The time to deliver guidance may be calculated to be useful to a user in the management of a glucose concentration level.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.

Abstract: An adjustable bone fixation device for moving a bone includes at least two strut units, at least one meter and a system controller. Each strut unit includes a motor to move a strut. The meter measures a signal generated by the motor during the movement of its strut. The signal is useful in determining a torque or a current of the motor. The system controller activates at least two of the motors and uses the determined torque or the determined current to identify if there is a clinical situation of the bone or a system issue and provides an alert accordingly.

Abstract: The present disclosure relates to a color classification system that accurately classifies objects in digital images based on color. In particular, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color mappings to match objects to colors. Indeed, the color classification system can accurately and efficiently detect the color of an object utilizing one or more color similarity regions generated in the multidimensional color space.

Abstract: The present disclosure relates to an object selection system that accurately detects and optionally automatically selects user-requested objects (e.g., query objects) in digital images. For example, the object selection system builds and utilizes an object selection pipeline to determine which object detection neural network to utilize to detect a query object based on analyzing the object class of a query object. In particular, the object selection system can identify both known object classes as well as objects corresponding to unknown object classes.

Abstract: Disclosed are systems and methods for providing automated or semi-automated technical support for patients using medical devices, such as continuous glucose monitoring systems. Disclosed embodiments of automated tech support system include collection and storage of copies of streams of medical device data on multiple servers, analysis and comparison of data streams, remote tech support initiation and usage of the automated tech support system for providing improved products and services by storing and analyzing historical tech support data.

Abstract: Aspects related to a resource-constrained system are described herein that can provide object storage services after a service interruption is resolved, even if all of the transactions that were pending and incomplete prior to the service interruption have not yet been recovered and/or executed. For example, file systems implemented by computing systems of the resource-constrained system may treat each file or directory as a separate object. Thus, a transaction directed to one file may not affect the file's directory or other files in the directory. As a result, the resource-constrained system can achieve read-after-write consistency without first recovering and executing the pending, incomplete transactions. Instead, read-after-write consistency for an object can be achieved simply by completing any pending, incomplete transaction directed to that object.

Abstract: An apparatus for treating skin has a console with a user input device and a handpiece assembly. The handpiece assembly is configured to treat skin. A fluid line provides fluid communication between the console and the handpiece assembly. A manifold system is coupled to the console and controlled by the user input device. The manifold system is configured to hold releasably a plurality of fluid sources and deliver fluid from at least one of the plurality of fluid sources to the handpiece assembly.

Abstract: An apparatus for treating skin has a console with a user input device and a handpiece assembly. The handpiece assembly is configured to treat skin. A fluid line provides fluid communication between the console and the handpiece assembly. A manifold system is coupled to the console and controlled by the user input device. The manifold system is configured to hold releasably a plurality of fluid sources and deliver fluid from at least one of the plurality of fluid sources to the handpiece assembly.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems detect, via a graphical user interface of a client device, a user selection of an object portrayed within a digital image. The disclosed systems determine, in response to detecting the user selection of the object, a relationship between the object and an additional object portrayed within the digital image. The disclosed systems receive one or more user interactions for modifying the object. The disclosed systems modify the digital image in response to the one or more user interactions by modifying the object and the additional object based on the relationship between the object and the additional object.

Abstract: The present disclosure relates to an object selection system that accurately detects and automatically selects target instances of user-requested objects (e.g., a query object instance) in a digital image. In one or more embodiments, the object selection system can analyze one or more user inputs to determine an optimal object attribute detection model from multiple specialized and generalized object attribute models. Additionally, the object selection system can utilize the selected object attribute model to detect and select one or more target instances of a query object in an image, where the image includes multiple instances of the query object.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to synthesize shadows for object(s). For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access an object mask of the object depicting in the digital image. The disclosed systems further combine the object mask, the digital image, and a noise representation to generate a combined representation. Moreover, the disclosed systems generate a shadow for the object from the combined representation and further generates the modified digital image by combining the shadow with the digital image.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that provides a graphical user interface experience to move objects and generate new shadows within a digital image scene. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems receive a selection to position an object in a first location within the scene. Further, the disclosed systems composite an image by placing the object at the first location within the scene of the digital image. Moreover, the disclosed systems generate a modified digital image having a shadow of the object where the shadow is consistent with the scene and provides the modified digital image to the client device.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems receive a digital image from a client device. The disclosed systems detect, utilizing a shadow detection neural network, an object portrayed in the digital image. The disclosed systems detect, utilizing the shadow detection neural network, a shadow portrayed in the digital image. The disclosed systems generate, utilizing the shadow detection neural network, an object-shadow pair prediction that associates the shadow with the object.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to remove a shadow for an object. For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access a shadow mask of the shadow in a first location. Further, the disclosed systems generate the modified digital image without the shadow by generating a fill for the first location that preserves a visible location of the first location. Moreover, the disclosed systems generate the digital image without the shadow for the object by combining the fill with the digital image.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate utilizing a segmentation neural network, an object mask for each object of a plurality of objects of a digital image. The disclosed systems detect a first user interaction with an object in the digital image displayed via a graphical user interface. The disclosed systems surface, via the graphical user interface, the object mask for the object in response to the first user interaction. The disclosed systems perform an object-aware modification of the digital image in response to a second user interaction with the object mask for the object.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate, utilizing a segmentation neural network and without user input, object masks for objects in a digital image. The disclosed systems determine foreground and background abutting an object mask. The disclosed systems generate an expanded object mask by expanding the object mask into the foreground abutting the object mask by a first amount and expanding the object mask into the background abutting the object mask by a second amount that differs from the first amount. The disclosed systems inpaint a hole corresponding to the expanded object mask utilizing an inpainting neural network.