Abstract: Autonomous ground vehicles that are outfitted with onboard cameras and configured to travel within an area or region are programmed with a previously generated visual map of the area or region. The visual map includes a plurality of map points from which images were previously captured and processed to determine visual features of such images. In the event of a loss of one or more position signals, such as GPS signals, the autonomous ground vehicles capture images using one or more of the cameras, and process the images to determine visual features from such images. The visual features determined from such images are compared to the visual map. Positions of one or more of the map points are identified as most likely corresponding to a position of the autonomous ground vehicle where visual features associated with such map points are similar to the visual features determined from such images.

Abstract: Systems and methods are disclosed for a shuttle track system having a calibration shuttle for determining portions of the track having non-conforming geometry for optimum use of the shuttle track system. For example, the shuttle track system may include a track having an electromagnetic propulsion system (e.g., linear synchronous motor) embedded between two sides of the track. The calibration shuttle may move to various locations on the track and may use imaging systems for generating image data of a portion of the track. A trained model may process the image data to determine edges and surfaces of the components of the track and may determine distances between reference points on the track. The distances may be compared to threshold values to determine if they are in conformity with desired track geometry.

Abstract: A group masked autoencoder may be implemented for anomaly detection. An autoencoder network model may be trained without supervision and applied to output an estimated joint probability distribution of normality for a group of frames of time series data. The estimated joint probability distribution may be used to determine an anomaly score for the time series data. An anomaly may be detected according to the anomaly score and a result that indicates a detected anomaly may be provided.

Abstract: An aerial vehicle is configured to calculate ranges to objects around the aerial vehicle when operating within indoor spaces, using a LIDAR sensor or another range sensor. The aerial vehicle calculates ranges within a plurality of sectors around the aerial vehicle and identifies a minimum distance measurement for each of the sectors. Sets of adjacent sectors having distance measurements above a threshold are identified, and bearings and minimum distance measurements of the sets of adjacent sectors are determined. When the aerial vehicle detects an object within a flight path, the aerial vehicle selects one of the sets of adjacent sectors based on the minimum distance measurements and executes a braking maneuver in a direction of the selected one of the sets of adjacent sectors.

Abstract: Techniques for selecting a skill component to process a natural language input are described. When a natural language input is received, natural language understanding (NLU) output data representing the natural language input is generated, and skill components (capable of processing the NLU output data) are determined. Thereafter, rules (for preventing the invocation of skill components) are implemented in a tiered manner, resulting in the determination of a subset of the skill components. The subset of skill components is ranked using a machine learning model(s), and the top-ranked skill component is called to process the NLU output data.

Abstract: Methods and apparatus are described for high-resolution video encoding using spatial lapping to reduce visual discontinuities within video frames. A plurality of frame regions may be encoded to form a higher-resolution video frame. For example, a 7680×4320 video frame may be constructed from four 4096×2160 frame regions arranged into a 2×2 grid. The encoding of the frame regions may be done in parallel. The encoded frame regions may be arranged such that the frame regions lap one another at one or more overlapping regions. Encoding of border regions adjacent to the overlapping regions may be done based on an encoding parameter (e.g., quantization parameter) of pixels of an overlapping region to determine an encoding parameter of pixels of the border regions to reduce visual artifacts at seams between frame regions and overlapping regions.

Abstract: The present disclosure describes techniques for storing encrypted files in a secure file repository and transferring those encrypted files to one or more recipients. A user selects a file to upload to a secure file repository. A secure collaboration app on the user's device generates a first encryption key that is used to encrypt the file. The encrypted file is then uploaded to the secure file repository, which provides the secure collaboration app with a random file name and a location of the encrypted file. The secure collaboration app updates locally stored metadata of the first encrypted file. To securely transfer the file, the user generates a second encryption key, encrypts the metadata with the second encryption key, and transmits the encrypted metadata to one or more receivers. The one or more receivers decrypt the encrypted metadata and use the decrypted metadata to retrieve the file and decrypt it.

Abstract: Disclosed are systems and methods to compare two or more machine learning models to determine the comparative performance of those models. Markers may be assigned to data items and data item marker scores generated for those data items, independent of the machine learning models. Each of the machine learning models to be compared may then process the data items and generate respective model scores for those data items. A sub-set of the data items may then be generated for each machine learning model based on the model scores assigned to the data items by the respective model. A model marker score may then be computed for each machine learning model based on the marker scores assigned to each of the data items of the sub-set of data items determined for each model. Finally, the model marker scores may be compared to determine which machine learning model has the highest performance.

Abstract: Techniques for conversational-based searching are described. A system may receive a first spoken user input, and may determine that the first spoken user input corresponds to a request for information associated with an entity. The system may retrieve item results corresponding to the entity. The system may determine a suggested user input based on the retrieved item results, and may determine output data corresponding to the suggested user input. The system may send output data to a user device, where the output data includes the item results and the suggested user input. The system may receive a second spoken user input, and may determine that the second spoken user input corresponds to the suggested user input. In response, the system may send the previously determined output data to the device.

Abstract: An apparatus includes a hub, a carrier structure and a first plurality of connective elements. The carrier structure surrounds the hub and holds a plurality of rollers that rotate within the carrier structure about an axis that is orthogonal to an axis of rotation of the hub. The first plurality of connective elements are coupled to the hub and the carrier structure. A connective element of the first plurality of connective elements elastically deforms to absorb an impact experienced by the carrier structure such that the rollers can rotate within the carrier structure while the carrier structure experiences the force.

Abstract: A quantum computing service includes a quantum circuit compilation module that uses received information about quantum processing units (QPUs) along with information about a quantum circuit to be compiled in order to generate an optimized ordered list of compilation passes to be performed to compile the quantum circuit for execution on a given QPU.

Abstract: Disclosed are various embodiments for on-demand application-driven network slicing. In one embodiment, first data is transmitted to or from a first application in a particular computing device using a first network slice in a communications network. A request is sent from the particular computing device to configure a second network slice in the communications network based at least in part on a requirement of a second application. Second data is transmitted to or from the second application using the second network slice.

Abstract: A system configured to perform content recognition using fingerprinting to recognize known media content. A device determines fingerprints based on decoded content data to be sent using a media interface component to an output component. Metadata related to the content/device/fingerprint may also be created. The fingerprints and metadata are sent by the device to a supporting system for orchestration and matching of the fingerprints to known media content.

Abstract: Techniques for processing speculative data using data history information are described. A system may receive, at a first component, first data and second data for use in a first operation. The component may use metadata associated with each of the first data and the second data to determine that the first data and the second data correspond to different versions of data of the same data type. Based on such a determination, the component may cease processing corresponding to the first operation with respect to the first data and the second data.

Abstract: Techniques are described for pre-processing frames to apply a temporal filter. A P-frame may be temporally filtered based on neighboring reference frames. The temporal filter may be applied to blocks of the P-frame to improve alignment with dependent frames that are encoded based on blocks of the P-frame.

Abstract: Techniques are described for enabling users to create and configure request-based policies used by a data storage system to generate custom responses to particular types of requests for users' stored data objects. The request-based policies include rules used to identify types of requests for data objects and corresponding types of responses to be generated responsive to detecting such requests. The types of responses that can be specified in such policies include responses other than simply returning a requested data object or denying a request. For example, a rule for certain types of responses can instruct the data storage system to return a dynamically generated data object, another rule can instruct the system to return a different data object than the one requested, and so forth.

Abstract: This disclosure describes, in part, techniques for identifying interactions and events associated with inventory locations. For instance, system(s) may receive image data representing a user interacting with an inventory location. The system(s) may then generate heatmap data indicating a first portion of the image data that represents the inventory location and feature data indicating a second portion of the image data that represents the user. Next, the system(s) may analyze the heatmap data with respect to the feature data to determine that the second portion of the image data corresponds to the first portion of the image data. As such, the system(s) may determine that the user is interacting with the inventory location. Based on the determination, the system(s) may analyze the first portion of the image data to identify an event that occurs at the inventory location, such as the user removing an item.

Abstract: Results of applying a set of voting rules to a target corpus of documents are used to obtain a set of derived probabilistic labels indicating the probabilities of the presence of a particular attribute within the documents' constituent objects. A machine learning model is trained to identify a candidate portion of a document from which a value of the attribute is to be extracted. The training data for the model includes learned representations obtained from paths of constituent objects, and the corresponding derived labels. A proposed value for the attribute, obtained based on an assigned attribute value presence probability score for an individual constituent object from a selected candidate portion of a document, is provided.

Abstract: A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes.

Abstract: Systems and methods are provided for generating a flat garment pattern and/or 3D mesh representation of a garment from one or more images depicting the garment laid flat or hung up. A system may obtain both a front image depicting a front view of a garment and a back image depicting a back view of the garment. A front and back silhouette of the garment may then be generated, which may include segmenting the garment depiction from background image content. A parametric representation of the garment may then be generated based on the front and back silhouettes, which may be implemented by iteratively optimizing, using differentiable rendering techniques, a garment representation within a parametric garment space previously learned for the particular garment type. A 3D mesh garment representation may then be generated based on the parametric representation, from which a flat sewing pattern may subsequently be generated if desired.