Abstract: A resource tagging service allows resource tags to be created and associated with computing resources in a service provider network. The resource tags can be utilized to search, collect, filter, organize and otherwise manage computing resources in the service provider network having matching tags, and/or to perform other types of functionality. A resource groups service allows customers and/or other users of the service provider network to view and access collections of computing resources that share common resource tags and/or other attributes. Resource groups can also be evaluated in order to identify computing resources in a service provider network for which certain types of actions or other functionality is to be performed. Membership of resource groups can also be evaluated over time and inferences can be drawn from the membership and from operational information associated with the member computing resources.

Abstract: Techniques for low latency updating of an electrophoretic panel are described. According to such techniques, pixels may be continuously scanned out instead of buffering and updating at fixed scanning slots. In an example, a controller of a device loads, before receiving update data associated with a pixel of an electrophoretic panel, a LUT into a memory accessible to a LUT hardware module. After the LUT is loaded into the memory, the controller receives the update data indicating a color change to the pixel and associates the update data with the LUT. Based at least in part on the update data and the LUT, the controller determines waveform data corresponding to the color change and causes an output on the electrophoretic panel based at least in part on the waveform data.

Abstract: A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.

Abstract: Techniques for processing input data for a detected user are described. Received image data is processed to identify an indicated user. Based on the user a machine learning model is implemented. The machine learning model is then used to process input data for a user input. An action is performed using the resulting output data.

Abstract: A media marker mechanism may be used to send, to a cloud service, up-to-date context regarding playback of a media content stream on a user device. The cloud service may insert a media content item into a media content stream and/or combine media content items into a media content stream. The cloud service may implement the media marker mechanism to tag a content item with metadata that can be read by the user device. The user device can play the streaming media content and, when the tagged content item plays, read the metadata and send it to the cloud service. The cloud service can use the metadata to enrich the media content delivery by, for example, providing a companion image that the user device can display while paying the tagged content item, providing a link to make the companion image clickable, handling requests referring to the media content, etc.

Abstract: A quantum computing service may store, in a cache, one or more compiled files of respective quantum functions included in one or more quantum computing programs received one or more customers. When the quantum computing service receives another quantum computing program, from the same or a different customer, the quantum computing service may determine whether the quantum computing program may include one or more of the quantum functions corresponding to the compiled files in the cache. If so, the quantum computing service may use the compiled files in the cache to compile the quantum computing program.

Abstract: A speech-processing system receives input data representing text. One or more encoders trained to predict audio properties corresponding to the text process the text to predict those properties. A speech decoder processes phoneme embeddings as well as the predicted properties to create data representing synthesized speech.

Abstract: A fiber optic alignment assembly can include first and second receivers positioned on a base. The first receiver can include a channel for receiving a collet of a first fiber optic ribbon such that the ferrule of the first fiber optic ribbon is positioned for engagement. The second receiver can include a channel for receiving a collet of a second fiber optic ribbon such that a ferrule of the second fiber optic ribbon is positioned for engagement with the ferrule of the first fiber optic ribbon.

Abstract: Dialog acts (e.g., questions) are selected for voice browsing by a model trained to identify a dialog act that is most likely to lead to a desired outcome. Upon receiving an invocation to begin a conversation, a score indicative of a level of confidence that the conversation will have a successful outcome is determined, and a dialog act is selected based on the score. Subsequently, at each turn of the conversation, the score is updated or a new score is calculated, and a dialog act is selected based on the updated or new score. Confidence scores are calculated based on input features that are determined based on the user who uttered the invocation or responses to dialog acts, as well as a context of the conversation, and provided to a linear model or a machine learning model as inputs.

Abstract: Systems and methods are provided for presenting selectable page navigation options representing topics or search queries determined to be relevant to an item that a user is viewing on an initial page. When a user selects to begin entering a search query in a search filed on the initial page, a system may determine two or more page destinations to recommend to the user based on the first item presented on the initial page, where the page destinations may each represent a different topic or search query. These recommended destinations may then be presented over at least a portion of the initial page near the search field as selectable options for navigating to a corresponding recommended page destination. The selectable options may include, for each recommended page destination, an image representing the page destination and text identifying the page destination.

Abstract: Devices and techniques are generally described for learning personalized concepts for natural language processing. In various examples, a first natural language input may be received. In some examples, a determination may be made that the first natural language input comprises non-actionable slot data. A dialog session may be initiated with the user. In some examples, first slot data that is indicated by the user during the dialog session may be determined. In various examples, data representing the first slot data may be stored in a database in association with the first natural language input.

Abstract: A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.

Abstract: A method includes extracting, using a backbone of a machine learning model, a plurality of features from an image of a foot and predicting, using a first portion of the machine learning model and based on one or more features of the plurality of features, a first aspect of the foot. The method also includes predicting, using a second portion of the machine learning model and based on one or more features of the plurality of features, a second aspect of the foot different from the first aspect, generating, using at least the first aspect and the second aspect, a two-dimensional model of a shoe, and superimposing the two-dimensional model of the shoe onto the image of the foot.

Abstract: A system, method and processor that mitigates security vulnerabilities using context-dependent address space hiding. In some embodiments, a hardware mechanism allows a more-privileged software component managing multiple less-privileged software components to blind itself against “out-of-context” less-privileged software components. The hardware mechanism can allow the more-privileged software component to dynamically hide a portion of the more-privileged address space related to the “out-of-context” less-privileged software components, based on knowledge of the “in-context” less-privileged software component. A context register is set with a value from which an address range, within the address space of the more-privileged software component, can be determined, where the address range is associated with a first less-privileged software component can be determined.

Abstract: Methods, systems, and computer-readable media for automatic archiving of data store log data are disclosed. One or more operation records in a log are selected for archival. The one or more operation records comprise data indicative of operations performed on one or more data objects of a data store. The one or more operation records are selected for archival prior to deletion from the log. The one or more operation records are replicated from the log to an archive. Based at least in part on the replicating, the one or more operation records in the log are marked as archived. Based at least in part on the marking as archived, the deletion of the one or more operation records from the log is permitted.

Abstract: A new type system may be added to a type registry for a data processing service. A request to add the new type system may be received that describes the new type system for a data store. The new type system may be used to perform a data processing job that accesses the data store to obtain or store data as a source or target data store.

Abstract: A computer system and associated methods are disclosed for mitigating side-channel attacks using a shared cache. The computer system includes a host having a main memory and a shared cache. The host executes a virtual machine manager (VMM) that determines respective security keys for a plurality of co-located virtual machines (VMs). A cache controller for the shared cache includes a scrambling function that scrambles addresses of memory accesses performed by threads of the VMs according to the respective security keys. Different cache tiers may implement different scrambling functions optimized to the architecture of each cache tier. Security keys may be periodically updated to further reduce predictability of shared cache to memory address mappings.

Abstract: Techniques for using suppression filters for presenting content of a network documents are described. In an example, a computer system receives, from a device associated with an account identifier, a request for an online network document. The computer system determines that a first category identifier of a first item category is associated with a suppression filter. The computer system determines that the account identifier is associated with an online item interaction having an interaction time associated with an item that belongs to the first item category. The computer system determines, based on a comparison of the interaction time and the suppression time, that a presentation of the first content in the online network document is prohibited. The computer system sends, to the device in response to the request, network document data that indicates second content about a third item that belongs to a second item category.

Abstract: Systems, methods, and computer-readable media are disclosed for using machine learning to generate automated custom media experiences. Example methods may include determining a first user identifier of a first user present in an environment, determining a first set of media preferences of the first user using media presented in association with the first user identifier, and determining a second user identifier of a second user present in the environment. Methods may include determining a second set of media preferences of the second user using media presented in association with the second user identifier, generating, using a first machine learning model, a first playlist of media using the first set of media preferences and the second set of media preferences for presentation to the first user and the second user, the first playlist comprising first media and second media, and causing audible presentation of the first media.