Abstract: Objects within an image are assigned a semantic interest that is indicative of their importance to the image as a whole. The objects are detected automatically, and sub-images that span each of the objects are excerpted from the image. An image embedding is determined for each of the sub-images as well as for the whole image by using an image encoder implemented as a trained multi-modal neural network. The degree of similarity between the image embeddings of a sub-image and that of the whole image is used as a measure of the semantic importance of the object portrayed in the sub-image. Objects of high semantic importance comprise the semantic regions of interest of the image. Knowledge of such regions may be used to enhance and improve the efficiency of downstream image-processing tasks, such as image compression, pan and scan, and contrast enhancement.

Abstract: Systems and methods are described for call management. Network information associated with a first service may be used for a wireless communication session associated with a second service. The wireless communication session may be caused, based on the network information, to be facilitated by a second network device instead of a first network device.

Abstract: Systems and methods are described for call management. A method may comprise determining at least a portion of a network topology. A handoff parameter may be determined based on one or more of network telemetry information or reliability information associated with the network topology. A handoff may be caused, based on at least the handoff parameter, between a first network device and a second network device associated with the network topology.

Abstract: Automatic editing of media compositions is performed using media editing applications equipped with neural-network-based deep-learning models. The automatic editing is adapted to the practices of local users of a media editing application by training the models on a combination of media compositions previously edited by third-party media editors and media compositions edited by local users. Training data input vectors for the model comprise representative portions of a composition's raw media, and corresponding output vectors include values of parameters that define editing functions applied to the raw media to generate an edited media composition. A user interface enabling a user to adjust and monitor machine learning parameters is provided. Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores.

Abstract: Described herein is a system and method for determining one or more data modulation profiles for one or more devices. The system and method described herein may measure signal quality, such as a modulation error ratio (MER), signal-to-noise ratio (SNR), receive power, transmit power, etc. Based on the signal quality, the system may determine one or more data modulation profile(s) (e.g., quadrature amplitude modulation (QAM) profiles) for a subcarrier, a plurality of subcarriers, a device, and/or a grouping of devices.

Abstract: Described herein is a system and method for determining one or more data modulation profiles for one or more devices. The system and method described herein may measure signal quality, such as a modulation error ratio (MER), signal-to-noise ratio (SNR), receive power, transmit power, etc. Based on the signal quality, the system may determine one or more data modulation profile(s) (e.g., quadrature amplitude modulation (QAM) profiles) for a subcarrier, a plurality of subcarriers, a device, and/or a grouping of devices.

Abstract: Automatic editing of media compositions is performed using media editing applications equipped with neural-network-based deep-learning models. The automatic editing is adapted to the practices of local users of a media editing application by training the models on a combination of media compositions previously edited by third-party media editors and media compositions edited by local users. Training data input vectors for the model comprise representative portions of a composition's raw media, and corresponding output vectors include values of parameters that define editing functions applied to the raw media to generate an edited media composition. A user interface enabling a user to adjust and monitor machine learning parameters is provided. Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores.

Abstract: Efficient processing of chroma-subsampled video is performed using convolutional neural networks (CNNs) in which the luma and chroma channels are processed separately. The luma channel is independently convolved and downsampled and, in parallel, the chroma channels are convolved and then merged with the downsampled luma to generate encoded chroma-subsampled video. Further processing of the encoded video that involves deconvolution and upsampling, splitting into two sets of channels, and further deconvolutions and upsampling is used in CNNs to generate decoded chroma-subsampled video in compression-decompression applications, to remove noise from chroma-subsampled video, or to upsample chroma-subsampled video to RGB 444 video. CNNs with separate luma and chroma processing in which the further processing includes additional convolutions and downsampling may be used for object recognition and semantic search in chroma-subsampled video.

Abstract: Efficient processing of chroma-subsampled video is performed using convolutional neural networks (CNNs) in which the luma and chroma channels are processed separately. The luma channel is independently convolved and downsampled and, in parallel, the chroma channels are convolved and then merged with the downsampled luma to generate encoded chroma-subsampled video. Further processing of the encoded video that involves deconvolution and upsampling, splitting into two sets of channels, and further deconvolutions and upsampling is used in CNNs to generate decoded chroma-subsampled video in compression-decompression applications, to remove noise from chroma-subsampled video, or to upsample chroma-subsampled video to RGB 444 video. CNNs with separate luma and chroma processing in which the further processing includes additional convolutions and downsampling may be used for object recognition and semantic search in chroma-subsampled video.

Abstract: Automatic editing of media compositions is performed using media editing applications equipped with neural-network-based deep-learning models. The automatic editing is adapted to the practices of local users of a media editing application by training the models on a combination of media compositions previously edited by third-party media editors and media compositions edited by local users. Training data input vectors for the model comprise representative portions of a composition's raw media, and corresponding output vectors include values of parameters that define editing functions applied to the raw media to generate an edited media composition. A user interface enabling a user to adjust and monitor machine learning parameters is provided. Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores.

Abstract: Systems and methods are described for call management. A method may comprise determining at least a portion of a network topology. A handoff parameter may be determined based on one or more of network telemetry information or reliability information associated with the network topology. A handoff may be caused, based on at least the handoff parameter, between a first network device and a second network device associated with the network topology.

Abstract: Automatic editing of media compositions is performed using media editing applications equipped with neural-network-based deep-learning models. The automatic editing is adapted to the practices of local users of a media editing application by training the models on a combination of media compositions previously edited by third-party media editors and media compositions edited by local users. Training data input vectors for the model comprise representative portions of a composition's raw media, and corresponding output vectors include values of parameters that define editing functions applied to the raw media to generate an edited media composition. A user interface enabling a user to adjust and monitor machine learning parameters is provided. Adaptive automatic editing may assist in the creation of video and audio compositions, as well as in the generation of musical scores.

Abstract: The present invention uses magnets and a wand to create a simple, attractive and functional way to get rid of the hanging cord and handle in an attic staircase. We call it the Magnetic Pulldown. It has an attractive fixture with magnets in the ceiling above and a three-foot wand with a powerful magnet nearby. The ceiling fixture houses the conventional rope and handle for pulling down the attic staircase. The handle is in a pocket and is visible from the floor below. The handle has weak magnets pointing up to keep it in place and a powerful magnet pointing down, awaiting the wand's powerful magnet to pull it out. To access the attic the user gets the wand and snaps it into the powerful magnet in the handle above. The user easily pulls the handle and rope down, snaps the bond between the magnets, z and uses the rope and handle to pull the attic staircase down. The Magnetic Pulldown needs no motor or electrical components. There is a prototype for this invention in the home of one of the inventors.

Abstract: Systems and methods are described for call management. A method may comprise determining at least a portion of a network topology. A handoff parameter may be determined based on one or more of network telemetry information or reliability information associated with the network topology. A handoff may be caused, based on at least the handoff parameter, between a first network device and a second network device associated with the network topology.

Abstract: Described herein is a system and method for determining one or more data modulation profiles for one or more devices. The system and method described herein may measure signal quality, such as a modulation error ratio (MER), signal-to-noise ratio (SNR), receive power, transmit power, etc. Based on the signal quality, the system may determine one or more data modulation profile(s) (e.g., quadrature amplitude modulation (QAM) profiles) for a subcarrier, a plurality of subcarriers, a device, and/or a grouping of devices.

Abstract: Described herein is a system and method for determining one or more data modulation profiles for one or more devices. The system and method described herein may measure signal quality, such as a modulation error ratio (MER), signal-to-noise ratio (SNR), receive power, transmit power, etc. Based on the signal quality, the system may determine one or more data modulation profile(s) (e.g., quadrature amplitude modulation (QAM) profiles) for a subcarrier, a plurality of subcarriers, a device, and/or a grouping of devices.

Abstract: A collaboration server hosts software for collaborative composition and editing of a media project with project collaborators using different media editing applications each having their own native data format. Project collaborators, such as video editors, sound editors, effects and graphics artists, and producers access a shared project workspace which contains a snapshot of the current state of the media project in a canonical format, as well as source media files, native application metadata, and change notes. Each editing application includes a module enabling it to read the canonical snapshot representation, and also to flatten its native data model representation into the canonical representation for writing to the shared project workspace. A collaboration server hosts the shared project space, and includes a workflow manager for issuing change notifications and handling versions, and an application server for the shared project user interface.

Abstract: Systems and methods are described for call management. A method may comprise determining at least a portion of a network topology. A handoff parameter may be determined based on one or more of network telemetry information or reliability information associated with the network topology. A handoff may be caused, based on at least the handoff parameter, between a first network device and a second network device associated with the network topology.

Abstract: A collaboration server hosts software for collaborative composition and editing of a media project with project collaborators using different media editing applications each having their own native data format. Project collaborators, such as video editors, sound editors, effects and graphics artists, and producers access a shared project workspace which contains a snapshot of the current state of the media project in a canonical format, as well as source media files, native application metadata, and change notes. Each editing application includes a module enabling it to read the canonical snapshot representation, and also to flatten its native data model representation into the canonical representation for writing to the shared project workspace. A collaboration server hosts the shared project space, and includes a workflow manager for issuing change notifications and handling versions, and an application server for the shared project user interface.

Abstract: A collaboration server hosts software for collaborative composition and editing of a media project with project collaborators using different media editing applications each having their own native data format. Project collaborators, such as video editors, sound editors, effects and graphics artists, and producers access a shared project workspace which contains a snapshot of the current state of the media project in a canonical format, as well as source media files, native application metadata, and change notes. Each editing application includes a module enabling it to read the canonical snapshot representation, and also to flatten its native data model representation into the canonical representation for writing to the shared project workspace. A collaboration server hosts the shared project space, and includes a workflow manager for issuing change notifications and handling versions, and an application server for the shared project user interface.