Abstract: Embodiments of the present disclosure provide for improved data imputation and use of imputed data in processing of downstream models. Some embodiments specially train a model that performs improved data imputation utilizing a specially-configured attention mechanism. Some embodiments train a model utilizing stratified masking. Some embodiments train a particular pre-processing layer of a downstream task-specific model to adaptively learn threshold values for imputing particular data. The pre-processing layer is usable to improve accuracy training and/or use of a downstream task-specific model based at least in part on the imputed data.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a plurality of training embeddings based on a pre-training dataset, wherein the plurality of training embeddings comprises one or more of descriptive embeddings, sequential ordering embeddings, age/time embeddings, locale embeddings, or encounter number embeddings; generating one or more initialized weights associated with respective one or more layers of a machine learning model based on the plurality of training embeddings; generating one or more fine-tuned weights for the machine learning model by updating at least a portion of the one or more initialized weights using a fine-tuning dataset associated with a target classification; and generating, using the machine learning model, one or more prediction scores for one or more prediction encounter data elements associated with the target classification, based on one or more input temporal sequence of encount

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving machine learning model training based on receiving labeled training data objects, generating a normal prediction loss parameter, generating a global classification loss parameter, generating a composite loss parameter, and initiating the performance of one or more prediction-based operations.

Abstract: A system for measuring a wound site including an image capture device, a touchscreen, a vibration motor, and a processor. The image capture device may be configured to capture a digital image and capture a depth map associated with the digital image. The processor may be configured to determine abounding box of the wound site, determine a wound mask, determine a wound boundary, determine whether the wound boundary is aligned within a camera frame, and generate a wound map. The processor and vibration motor may be configured to provide a series of vibrations in response to the processor determining that the wound is aligned within the camera frame.

Abstract: Embodiments provide for application of personalized or individualized sensor-based risk profiles for impacts of external events. An example method includes receiving sensor data from one or more sensors couplable with a subject body of a subject population comprising a plurality of subject bodies; receiving external factor data associated with the subject population; generating a population-level external event impact metric, where the population-level external event impact metric represents a predicted impact of one or more external events on a physiological or other metric of the subject population; generating a subject-level external impact metric, where the subject-level external event impact metric represents a predicted impact of the one or more external events on the physiological or other metric associated with the subject body; and initiating the performance of one or more prediction-based actions based on the subject-level external event impact metric.

Abstract: Embodiments of the present disclosure provide for improved data processing using interconnected variational autoencoder models, which may be used for any of a myriad of purposes. Some embodiments specially train the interconnected variational autoencoder models by utilizing different training scenarios corresponding to presence and/or absence of particular data in a training data set. Particular encoder(s) and/or decoder(s) from the specially trained interconnected variational autoencoder models may then be utilized to improve accuracy of the desired data processing tasks, for example, to generate particular output data.

Abstract: Embodiments of the disclosure provide for improved processing of data with different timescales, for example high-frequency data and low-frequency data. Embodiments specifically improve such processing of different timescale data processed by a machine learning model. Additionally or alternatively, some embodiments include improved processing of data with different timescales by selecting an optimal variant from a plurality of possible variants of a prediction model.

Abstract: A server includes a processor programmed to: acquire first metadata of a first media file recorded by a first mobile device; acquire second metadata of a second media file recorded by a second mobile device; determine that the first media file and the second medial file are likely recordings of the same event when a similarity exceeds a first threshold. The processor is further programmed to, when the first media file and the second medial file are likely recordings of the same event: determine, based on a comparison between the first media file and the second media file, which of the first media file and the second media file is a higher quality recording of the same event; and when the first media file is the higher quality recording, send a link to the first media file to the second mobile device.

Abstract: A server includes a processor programmed to: acquire first metadata of a first media file recorded by a first mobile device; acquire second metadata of a second media file recorded by a second mobile device; determine that the first media file and the second medial file are likely recordings of the same event when a similarity exceeds a first threshold. The processor is further programmed to, when the first media file and the second medial file are likely recordings of the same event: determine, based on a comparison between the first media file and the second media file, which of the first media file and the second media file is a higher quality recording of the same event; and when the first media file is the higher quality recording, send a link to the first media file to the second mobile device.

Abstract: Apparatus and related methods for assisting tool use includes one or more motors, a wrist support and a shaft coupled to the one or more motors. The shaft includes a coupling to couple to a tool to enable a user resting a hand with the wrist on the wrist support to hold and manipulate the tool with one or more fingers. The apparatus can include a controller configured to: receive position data indicative of a position of an active end of the tool; receive constraint data indicative of one or more regions of space in which the active end should not be positioned; and process the position and constraint data to control the one or more motors to bias the active end out of a region indicated by the constraint data when a position indicated by the position data is within the region indicated by the constraint data.

Abstract: Certain aspects of the present disclosure provide techniques for automated updates to code deployment pipelines. This includes identifying a proposed change to a plurality of source code repositories based on parsing a change template. It further includes determining one or more changes to one or more files in each respective source code repository, of the plurality of source code repositories, based on the proposed change. It further includes changing the one or more files in each respective source code repository, based on the determined one or more changes, and building a project in each respective source code repository using the changed one or more files.

Abstract: Certain aspects of the present disclosure provide techniques for automatic compute environment scheduling using machine learning (ML). This includes identifying a first compute resource among a plurality of compute resources operating in a compute infrastructure, where the first compute resource is in a first operational state. It further includes determining, based on comparing a first time with a compute resources schedule generated using an ML model, that the first compute resource should be placed in a second operational state different from the first operational state. It further includes determining whether the compute resources schedule should be disregarded, and either (1) in response to determining that the compute resources schedule should not be disregarded, placing the first compute resource in the second operational state, or (2) in response to determining that the compute resources schedule should be disregarded, allowing the first compute resource to remain in the first operational state.

Abstract: Certain aspects of the present disclosure provide techniques for automated updates to code deployment pipelines. This includes identifying a proposed change to a plurality of source code repositories based on parsing a change template. It further includes determining one or more changes to one or more files in each respective source code repository, of the plurality of source code repositories, based on the proposed change. It further includes changing the one or more files in each respective source code repository, based on the determined one or more changes, and building a project in each respective source code repository using the changed one or more files.

Abstract: A server includes a processor programmed to: acquire first metadata of a first media file recorded by a first mobile device; acquire second metadata of a second media file recorded by a second mobile device; determine that the first media file and the second medial file are likely recordings of the same event when a similarity exceeds a first threshold. The processor is further programmed to, when the first media file and the second medial file are likely recordings of the same event: determine, based on a comparison between the first media file and the second media file, which of the first media file and the second media file is a higher quality recording of the same event; and when the first media file is the higher quality recording, send a link to the first media file to the second mobile device.

Abstract: A server includes a processor programmed to: acquire first metadata of a first media file recorded by a first mobile device; acquire second metadata of a second media file recorded by a second mobile device; determine that the first media file and the second medial file are likely recordings of the same event when a similarity exceeds a first threshold. The processor is further programmed to, when the first media file and the second medial file are likely recordings of the same event: determine, based on a comparison between the first media file and the second media file, which of the first media file and the second media file is a higher quality recording of the same event; and when the first media file is the higher quality recording, send a link to the first media file to the second mobile device.

Abstract: The disclosed computer-implemented method may a strap adjustment mechanism having a first strap and a second strap positioned to at least partially overlap the first strap. The strap adjustment mechanism may also include a first strap adjuster, and a proximal portion of the first strap may be coupled to the first strap adjuster and the second strap is threaded through the first strap adjuster. In addition, the strap adjustment mechanism may include a second strap adjuster, and a proximal portion of the second strap is coupled to the second strap adjuster and the first strap is threaded through the second strap adjuster. Various other devices, systems, and methods are also disclosed.

Abstract: A head-mounted display may include a frame for supporting an electronic display for viewing by an intended user donning the head-mounted display. The frame may include a flange disposed on a perimeter area of the frame and a facial interface configured to abut against an intended user's face wherein the facial interface comprises a foam body and an undercut region in the foam body that is sized and shaped for detachably engaging with the flange of the frame. Various other apparatuses, systems, and methods are also disclosed.

Abstract: According to one exemplary implementation, a collector container includes processing hardware, an output device controlled by the processing hardware; and a memory storing a digital wallet. The collector container is configured to uniquely store a non-fungible token (NFT) in the digital wallet, the NFT certifying ownership of an NFT asset. The collector container is further configured to store the NFT asset in the memory, and to display the NFT asset using the output device.

Abstract: Latency of in-system test (IST) execution for a hardware component of an in-field (deployed) computing platform may be reduced when a value of a physical operating parameter can be changed without rebooting the computing platform. A test (e.g., patterns or vectors) is executed for varying values of the physical operating parameter (e.g., supply voltage, clock speed, temperature, noise magnitude/duration, operating current, and the like), providing the ability to detect faults in the hardware components.

Abstract: Systems and methods for providing a middleware application for user-interface-driven applications include receiving, at the middleware application, queries from different dynamic user interface modules associated with respective front-end applications. The front-end applications are authenticated using authentication data included in the query and verification data external to the middleware application. In response to each query, the middleware application receives data from different external data sources, each being a separate instance of the same back-end service. The data is used to generate objects declaring instances of user interface elements, which are sent by the middleware application to the requesting dynamic user interface module for rendering at the associated front-end application.