Abstract: A system and method of using electrochemical additive manufacturing to add interconnection features, such as wafer bumps or pillars, or similar structures like heatsinks, to a plate such as a silicon wafer. The plate may be coupled to a cathode, and material for the features may be deposited onto the plate by transmitting current from an anode array through an electrolyte to the cathode. Position actuators and sensors may control the position and orientation of the plate and the anode array to place features in precise positions. Use of electrochemical additive manufacturing may enable construction of features that cannot be created using current photoresist-based methods. For example, pillars may be taller and more closely spaced, with heights of 200 ?m or more, diameters of 10 ?m or below, and inter-pillar spacing below 20 ?m. Features may also extend horizontally instead of only vertically, enabling routing of interconnections to desired locations.

Abstract: A method of additive manufacturing that deposits material onto a cathode by transmitting current from an anode array through an electrolyte to the cathode; the method uses feedback to control the manufacturing of successive layers of a part. For example, feedback signals may be a map of current across the anode array; this current map may be processed using morphological analysis or Boolean operations to determine the extent of deposition across the layer. Feedback data may be used to determine when a layer is complete, and to adjust process parameters such as currents and voltages during layer construction. Layer descriptions may be preprocessed to generate maps of desired anode current, to manipulate material density, and to manage features such as overhangs. Feedback signals may also trigger execution of maintenance actions during the build, such as replenishment of anodes or removal of films or bubbles.

Abstract: Techniques and systems are described for analyzing components of digital content. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital content.

Abstract: A method of additive manufacturing that deposits material onto a cathode by transmitting current from an anode array through an electrolyte to the cathode; the method uses feedback to control the manufacturing of successive layers of a part. For example, feedback signals may be a map of current across the anode array; this current map may be processed using morphological analysis or Boolean operations to determine the extent of deposition across the layer. Feedback data may be used to determine when a layer is complete, and to adjust process parameters such as currents and voltages during layer construction. Layer descriptions may be preprocessed to generate maps of desired anode current, to manipulate material density, and to manage features such as overhangs. Feedback signals may also trigger execution of maintenance actions during the build, such as replenishment of anodes or removal of films or bubbles.

Abstract: Process for manufacturing a printhead for a 3D manufacturing system that uses metal electrodeposition to construct parts. The printhead may be constructed by depositing layers on top of a backplane that contains control and power circuits. Deposited layers may include insulating layers and an anode layer that contain deposition anodes that are in contact with the electrolyte to drive electrodeposition. Insulating layers may for example be constructed of silicon nitride or silicon dioxide; the anode layer may contain an insoluble conductive material such as platinum group metals and their associated oxides, highly doped semiconducting materials, and carbon based conductors. The anode layer may be deposited using chemical vapor deposition or physical vapor deposition. Alternatively in one or more embodiments the printhead may be constructed by manufacturing a separate anode plane component, and then bonding the anode plane to the backplane.

Abstract: Process for manufacturing a printhead for a 3D manufacturing system that uses metal electrodeposition to construct parts. The printhead may be constructed by depositing layers on top of a backplane that contains control and power circuits. Deposited layers may include insulating layers and an anode layer that contain deposition anodes that are in contact with the electrolyte to drive electrodeposition. Insulating layers may for example be constructed of silicon nitride or silicon dioxide; the anode layer may contain an insoluble conductive material such as platinum group metals and their associated oxides, highly doped semiconducting materials, and carbon based conductors. The anode layer may be deposited using chemical vapor deposition or physical vapor deposition. Alternatively in one or more embodiments the printhead may be constructed by manufacturing a separate anode plane component, and then bonding the anode plane to the backplane.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: A method includes detecting control of an active content creation tool of an interactive computing system in response to a user input received at a user interface of the interactive computing system. The method also includes automatically updating a video search query based on the detected control of the active content creation tool to include context information about the active content creation tool. Further, the method includes performing a video search of video captions from a video database using the video search query and providing search results of the video search to the user interface of the interactive computing system.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Techniques and systems are described for analyzing components of digital content. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital content.

Abstract: Attribute control for updating digital content in a digital medium environment is described. The digital content is updated by incorporating new digital content components from a service provider system, such as a stock content service, to keep the digital content from seeming stale to client device users. The service provider system controls provision of digital content components based on fixed and variable attributes specified for these digital content components. Initially, the service provider system receives a component request, requesting that the service provider system provide the digital content components for incorporation with the digital content. The component request specifies fixed and variable content attributes for the provided digital content components. A fixed content attribute is an attribute that is to be included in the provided digital content components.

Abstract: Printhead for a 3D manufacturing system that uses metal electrodeposition to construct parts; embodiments utilize a grid of anodes to achieve high quality parts with features that may be small and detailed. To support grids with thousands or millions of anodes, the printhead may use matrix control with row and column drivers similar to display backplanes. Unlike display backplanes where the design goal is to display images using minimal current, the printhead may be optimized for high current density for fast electrodeposition, and for anode longevity. Current density may exceed 1000 mA per cm-squared, at least an order of magnitude greater than that of display backplanes. Anode longevity may be enhanced by using relatively large anodes compared to the grid pitch of the printhead, by lengthening the conductive paths through anodes, or both. Embodiments may be constructed by adding anode and insulation layers on top of matrix-controlled switching circuits.

Abstract: An update basis for updating digital content in a digital medium environment is described. The digital content is updated by incorporating new digital content components from a service provider system, such as a stock content service, to keep the digital content from seeming stale to client device users. The service provider system controls provision of digital content components according to an update basis described in a component request. In part, component requests ask that the service provider system provide digital content components for incorporation with digital content. Component requests also describe a timing basis with which digital content components are to be provided as updates. By way of example, the timing basis may correspond to a time interval (e.g., daily, weekly, monthly, seasonally, times of day, and so on), receiving user input in relation to the digital content (e.g., a navigation input to a web page), and so forth.

Abstract: Techniques and systems are described for analyzing components of digital marketing content as part of a digital marketing campaign. A computing device of an analytics system receives user interaction data that describes an effect of user interaction with a plurality of items of digital marketing content on achieving an action. The analytics system identifies which of a plurality of components are included in respective items of digital marketing content. The analytics system then generates outcome data describing a likely effect of the plurality of components on achieving the action based on association with respective items of digital marketing content. Additionally, the analytics system generates a recommendation to configure a subsequent item of digital marketing content based on the outcome data. The recommendation is based on the likely effect of the different ones of the plurality of components, to generate more effective digital marketing content items for digital marketing campaigns.

Abstract: An update basis for updating digital content in a digital medium environment is described. The digital content is updated by incorporating new digital content components from a service provider system, such as a stock content service, to keep the digital content from seeming stale to client device users. The service provider system controls provision of digital content components according to an update basis described in a component request. In part, component requests ask that the service provider system provide digital content components for incorporation with digital content. Component requests also describe a timing basis with which digital content components are to be provided as updates. By way of example, the timing basis may correspond to a time interval (e.g., daily, weekly, monthly, seasonally, times of day, and so on), receiving user input in relation to the digital content (e.g., a navigation input to a web page), and so forth.

Abstract: Systems and methods provide a recommendation system for recommending sequential content. The training of a reinforcement learning (RL) agent is bootstrapped from passive data. The RL agent of the sequential recommendations system is trained using the passive data over a number of epochs involving interactions between the sequential recommendation system and user devices. At each epoch, available active data from previous epochs is obtained, and transition probabilities are generated from the passive data and at least one parameter derived from the currently available active data. Recommended content is selected based on a current state and the generated transition probabilities, and the active data is updated from the current epoch based on the recommended content and a resulting new state. A clustering approach can also be employed when deriving parameters from active data to balance model expressiveness and data sparsity.

Abstract: Systems and methods for searching digital content are disclosed. A method includes receiving, from a user, a base search constraint. A search constraint includes search values or criteria. A recall set is generated based on the base search constraint. Recommended search constraints are determined and provided to the user. The recommended search constraints are statistically associated with the base search constraint. The method receives, from the user, a selection of a first search constraint included in the plurality of recommend search constraints. The method generates and provides search results to the user that include a re-ordering of the recall set. The re-ordering is based on a search constraint set that includes both the base search constraint and the selected first search constraint. The re-ordering is further based on a weight associated with the base search constraint and another user-provided weight associated with the first search constraint.

Abstract: Systems and methods for searching digital content, such as digital images, are disclosed. A method includes receiving a first search constraint and generating search results based on the first search constraint. A search constraint includes search values or criteria. The search results include a ranked set of digital images. A second search constraint and a weight value associated with the second search constraint are received. The search results are updated based on the second search constraint and the weight value. The updated search results are provided to a user. Updating the search results includes determining a ranking (or a re-ranking) for each item of content included in the search results based on the first search constraint, the second search constraint, and the weight value. Re-ranking the search results may further be based on a weight value associated with the first search constraint, such as a default or maximum weight value.

Abstract: Application personalization techniques and systems are described that leverage an embedded machine learning module to preserve a user's privacy while still supporting rich personalization with improved accuracy and efficiency of use of computational resources over conventional techniques and systems. The machine learning module, for instance, may be embedded as part of an application to execute within a context of the application to learn user preferences to train a model using machine learning. This model is then used within the context of execution of the application to personalize the application, such as control access to digital content, make recommendations, control which items of digital marketing content are exposed to a user via the application, and so on.

Abstract: Techniques and system are described to control output of digital marketing content with respect to a digital video that address the added complexities of digital video over other types of digital content, such as webpages. In one example, the techniques and systems are configured to control a time, at which, digital marketing content is to be output with respect to the digital video, e.g., by selecting a commercial break or output as a banner ad in conjunction with the video.