Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: Technologies for memory on package with reduced package thickness are disclosed. In the illustrative embodiment, a die assembly includes a substrate with a processor die mounted on the top surface and a memory die mounted on the bottom surface. The die assembly is mounted on another substrate, such as a mainboard. A cavity is defined in the mainboard, and the memory die mounted on the bottom surface of the die assembly is positioned in the cavity. Positioning the memory die on the bottom surface of the die assembly can reduce the overall thickness of the die assembly and, therefore, can reduce the overall thickness of a device that incorporates the die assembly.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.

Abstract: Present disclosure relates to system and method for converting RTMP stream into HLS format for live stream. The solution architecture can include three main components: a Publisher, a Streamer and an API server. The publisher manages incoming RTMP streams and converts RTMP stream into multiple (resolutions) HLS streams with an adaptive bit rate. The streamer manages end users consuming HLS feed. As publisher can be busy doing transcoding, the streamer can serve HLS format to an end user. At streamer level, caching is done instead of sending all requests to publisher. The API server manages load on the publishers and the streamers, and makes sure that the servers are available all the time. Live stream API server keeps record of the streams which are available as well as the corresponding publishers having the streams.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: An aspect of the present disclosure facilitates mobility of user applications across cloud infrastructures. In one embodiment, a mobility server maintains a setup data which indicates that a user application is to be operative based on a specific set of application components when the user application is to be hosted on a cloud infrastructure. Upon receiving a request to move the user application currently executing in a source cloud infrastructure to the cloud infrastructure, mobility server provisions the specific set of application components in the cloud infrastructure in view of the setup data and then executes the user application in the cloud infrastructure based on the specific set of application components.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

Abstract: A computing system can determine moving objects in a sequence of images based on recursively calculating red-green-blue (RGB) eccentricity 249 k based on a video data stream. A vehicle can be operated based on the determined moving objects. The video data stream can be acquired by a color video sensor included in the vehicle or a traffic infrastructure system.

Abstract: Illustrative embodiments represent a dynamic on-demand approach to configuring destination storage for bare metal restore (BMR) operations without operator intervention, including destination storage that is smaller than source storage devices. The illustrative operations rely on system state information collected concurrently with or shortly after source data is backed up, thereby capturing current actual storage metrics for the source data. The illustrative embodiments further rely on enhanced data agent components to collect and restore system state information as well as to restore backup data, thereby streamlining the configurations needed for the BMR operation to proceed. Additional business logic matches source mount points with suitable smaller destination storage resources and ensures that the BMR operation successfully completes with diverse and/or smaller storage destinations.

Abstract: The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

Abstract: In one embodiment, an apparatus includes a host controller to implement one or more layers of a Universal Serial Bus (USB)-based protocol to provide an interconnect for a plurality of devices. The host controller is to monitor control plane messages on the interconnect, detect, in the control plane messages, a power state change command for a device coupled to the interconnect, wherein the devices utilizes a tunneled protocol on the interconnect, and modify power distribution for one or more other devices of the interconnect based on detecting the power state change command.

Abstract: An aspect of the present disclosure facilitates mobility of user applications across cloud infrastructures. In one embodiment, a mobility server maintains a setup data which indicates that a user application is to be operative based on a specific set of application components when the user application is to be hosted on a cloud infrastructure. Upon receiving a request to move the user application currently executing in a source cloud infrastructure to the cloud infrastructure, mobility server provisions the specific set of application components in the cloud infrastructure in view of the setup data and then executes the user application in the cloud infrastructure based on the specific set of application components.

Abstract: The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.