Abstract: Systems and methods for material processing using wafer scale waves of precisely controlled electrons in a DC plasma is presented. A surface floating potential of a substrate placed atop a stage in a positive column of the DC plasma is adjusted and maintained to a reference potential. A periodic biasing signal referenced to the reference potential is capacitively coupled to the stage to control a surface potential at the substrate according to: an active phase for provision of kinetic energy to free electrons in the DC plasma for activation of targeted bonds at the surface of the substrate; a neutralization phase for repelling of the free electrons from the surface of the substrate; and an initialization phase for restoring an initial condition of the surface floating potential.

Abstract: A composite stage for electron enhanced material processing is presented. The composite stage provides capacitive coupling of a biasing signal to a substrate supported by the composite stage. The composite stage comprises a pedestal and a support plate that includes stacked layer construction. The stacked layer construction includes a plurality of layers of electrically conductive and dielectric materials. According to one aspect, the plurality of layers includes at least one electrically conductive layer for receiving a basing signal, and at least one dielectric layer in contact with and overlying the at least one electrically conductive layer. According to one aspect, the substrate is held in place via an electrically insulating clamp, the clamp providing an aperture for processing of a portion of the substrate. A matching circuit is arranged between a biasing signal generator and the composite stage. A shunting resistor is coupled to the matching circuit.

Abstract: Atomic layer etching of a substrate using a wafer scale wave of precisely controlled electrons is presented. A volume of gaseous plasma including diluent and reactive species and electrons of a uniform steady state composition is generated in a positive column of a DC plasma proximate the substrate. A corrosion layer is formed on the substrate by adsorption of the reactive species to atoms at the surface of the substrate. The substrate is positively biased to draw electrons from the volume to the surface of the substrate and impart an energy to the electrons so to stimulate electron transitions in the corrosion layer species, resulting in ejection of the corrosion layer species via electron stimulation desorption. The substrate is negatively biased to repel the electrons from the surface of the substrate back to the volume, followed by a zero bias to restore the steady state composition of the volume.

Abstract: Systems and methods for material processing using wafer scale waves of precisely controlled electrons in a DC plasma is presented. A surface floating potential of a substrate placed atop a stage in a positive column of the DC plasma is adjusted and maintained to a reference potential. A periodic biasing signal referenced to the reference potential is capacitively coupled to the stage to control a surface potential at the substrate according to: an active phase for provision of kinetic energy to free electrons in the DC plasma for activation of targeted bonds at the surface of the substrate; a neutralization phase for repelling of the free electrons from the surface of the substrate; and an initialization phase for restoring an initial condition of the surface floating potential.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Disclosed herein are multi-way oligonucleotide junctions for delivering one or more cargo molecules to a biological target and method of making such junctions. The oligonucleotide junctions are formed by two or more oligonucleotides and are stable outside the cell and easily dissociate inside the cell to release the cargo molecule(s). One or more cargo molecules as well as delivery ligand can be loaded to the junctions for targeted delivery. Also disclosed are nanostructures including one or more junctions attached to each other for delivering two or more cargo molecules.

Abstract: Atomic layer etching of a substrate using a wafer scale wave of precisely controlled electrons is presented. A volume of gaseous plasma including diluent and reactive species and electrons of a uniform steady state composition is generated in a positive column of a DC plasma proximate the substrate. A corrosion layer is formed on the substrate by adsorption of the reactive species to atoms at the surface of the substrate. The substrate is positively biased to draw electrons from the volume to the surface of the substrate and impart an energy to the electrons so to stimulate electron transitions in the corrosion layer species, resulting in ejection of the corrosion layer species via electron stimulation desorption. The substrate is negatively biased to repel the electrons from the surface of the substrate back to the volume, followed by a zero bias to restore the steady state composition of the volume.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems and methods describe techniques that search videos and media content to determine the presence of unknown objects, generate novel detectors trained to identify the unknown objects, and apply the novel detectors to historical media content to identify previous appearances of the unknown objects.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.