Abstract: An acoustic event detection system may employ one or more recurrent neural networks (RNNs) to extract features from audio data, and use the extracted features to determine the presence of an acoustic event. The system may use self-attention to emphasize features extracted from portions of audio data that may include features more useful for detecting acoustic events. The system may perform self-attention in an iterative manner to reduce the amount of memory used to store hidden states of the RNN while processing successive portions of the audio data. The system may process the portions of the audio data using the RNN to generate a hidden state for each portion. The system may calculate an interim embedding for each hidden state. An interim embedding calculated for the last hidden state may be normalized to determine a final embedding representing features extracted from the input data by the RNN.

Abstract: A system and method for providing quantum entanglement using a hybrid space-fiber quantum network are described. The hybrid space-fiber quantum network includes a communications hub located proximate to an optical ground station and also includes an aerial entangled particle source, such as an entangled photon source attached to a satellite, drone, aircraft, etc. An atmospheric or free-space channel is used to distribute quantum entanglement between optical ground stations that are separated by geographic distances, via the aerial entangled particle source. Also, fiber optic links are connected to the communications hub located proximate to the optical ground station. The communications hub includes optical switches that enable any of the fiber optic links connected to the communications hub to receive or send distributed quantum entanglement to a remotely located recipient endpoint via the atmospheric or free-space channel.

Abstract: A first configurable address decoder can be coupled between a source node and a first interconnect fabric, and a second address decoder can be coupled between the first interconnect fabric and a second interconnect fabric. The first address decoder can be configured with a first address mapping table that can map a first set of address ranges to a first set of target nodes connected to the first interconnect fabric. The second address decoder can be configured with a second address mapping table that can map a second set of address ranges to a second set of target nodes connected to the second interconnect fabric. The second address decoder can be part of the first set of target nodes. The first address decoder and the second address decoder can be configured or re-configured to determine different routes for a transaction from the source node to a target node in the second set of target nodes via the first and second interconnect fabrics.

Abstract: This disclosure describes systems and methods for using a primary device, communicatively coupled to a remote system, to configure or re-configure a secondary device in the same environment as the primary device. In some instances, the primary device may communicatively couple to the secondary device via a short-range wireless connection and to the remote system via a wireless area network (WAN), a wired connection, or the like. Thus, the primary device may act as an intermediary between the secondary device and the remote system for configuring the secondary device.

Abstract: Systems and methods are described for merging customer profiles, such as may be implemented by a computer-implemented contact center service. In some aspects, a subset of profiles may be determined that satisfy merging criteria, where individual profiles include a plurality of data fields. At least one value in a first data field that conflicts between at least two profiles may be identified. Next a merged value may be selected for the first data field based on data deduplication criteria, where the data deduplication criteria includes at least one indicator of accuracy of values of the plurality of data fields. As a result of a determination that at least the subset of profiles of the group of profiles meet the merging criteria, at least the subset of profiles may be combined into a combined profile using the merged value.

Abstract: Systems and methods are provided to eliminate multiplication operations with zero padding data for convolution computations. A multiplication matrix is generated from an input feature map matrix with padding by adjusting coordinates and dimensions of the input feature map matrix to exclude padding data. The multiplication matrix is used to perform matrix multiplications with respective weight values which results in fewer computations as compared to matrix multiplications which include the zero padding data.

Abstract: A materialization configuration request is received via a programmatic interface from a client of a journal-based multi-data-store database. The request indicates a partitioning rule to be used to select, for respective writes indicated in committed transaction entries of a journal, the materialization node at which the writes are to be stored. A control plane component of the database verifies that a set of materialization nodes corresponding to the partitioning rule has been established, and initiates the propagation of writes from the journal to the materialization nodes by respective write appliers.

Abstract: Techniques for reducing the latency of content retrieval from a content delivery network include receiving a request from a client device for media content, parsing the request for attributes associated with the request and the client device, and providing the attributes to a machine learning model to perform server-side prediction of an estimated retrieval time of the media content. A quality level for the media content is determined based on the estimated retrieval time, and the requested media content is provided to the client device at the determined quality level.

Abstract: Devices, systems, and methods are provided for enhanced geographical caching of estimated arrival times. A method may include receiving respective user inputs indicative of respective users being in transit to a destination location from within a geographic region; determining, for the first user and the second user, a first estimated time of arrival from a first geographical area to the destination location, the first geographical area including the first location and the second location; identifying a third location of the first device at a third time, wherein the third location is within the first geographical area; determining that a time-to-live (TTL) of the first estimated time of arrival has not expired at the third time; and refraining from recalculating the first estimated time of arrival.

Abstract: Techniques for training a machine-learning model are described. In an example, a computer generates a first pseudo-label indicating a first mask associated with a first object detected by a first machine-learning model in a first training image. A transformed image of the first training image can be generated using a transformation. Based on the transformation, a second pseudo-label indicating a second mask detected in the transformed image and corresponding to the first mask can be determined. A second machine-learning model can be trained using the second pseudo-label. The trained, second machine-learning model can detect a third mask associated with a second object based on a second image.

Abstract: Implementations detailed herein include description of a computer-implemented method.

Abstract: Disclosed herein are techniques for classifying data with a data processing circuit. In one embodiment, the data processing circuit includes a probabilistic circuit configurable to generate a decision at a pre-determined probability, and an output generation circuit including an output node and configured to receive input data and a weight, and generate output data at the output node for approximating a product of the input data and the weight. The generation of the output data includes propagating the weight to the output node according a first decision of the probabilistic circuit. The probabilistic circuit is configured to generate the first decision at a probability determined based on the input data.

Abstract: Disclosed are various embodiments for implementing passenger profiles for autonomous vehicles. A passenger of the autonomous vehicle is identified. A passenger profile corresponding to the passenger and comprising a passenger preference is identified. The passenger preference is identified. A configuration setting of the autonomous vehicle corresponding to autonomous operation of the autonomous vehicle is then adjusted based at least in part on the passenger preference.

Abstract: Described is a multiple-camera system and process for detecting, tracking, and re-verifying agents within a materials handling facility. In one implementation, a plurality of feature vectors may be generated for an agent and maintained as an agent model representative of the agent. When the object being tracked as the agent is to be re-verified, feature vectors representative of the object are generated and stored as a probe agent model. Feature vectors of the probe agent model are compared with corresponding feature vectors of candidate agent models for agents located in the materials handling facility. Based on the similarity scores, the agent may be re-verified, it may be determined that identifiers used for objects tracked as representative of the agents have been flipped, and/or to determine that tracking of the object representing the agent has been dropped.

Abstract: Systems, devices, and methods are provided for processing images using machine learning. Features may be obtained from an image using a residual network, such as ResNet-101. Features may be analyzed using a classification model such as K-nearest neighbors (K-NN). Features and metadata extracted from images may be used to generate other images. Templates may be used to generate various types of images. For example, assets from two images may be combined to create a third image.

Abstract: Server-specified subscription filters for long-lived client requests to fetch data in response to events. In one aspect, the techniques encompass a method performed by a set of one or more computing devices. The method includes the step of receiving a long-lived request to fetch data in response to events sent by a client computing device. The method further includes receiving a server-specified subscription filter for the long-lived request and executing the long-lived request. Executing the long-lived request includes creating a persistent function that uses the server-specified subscription filter to map a source event stream to a response event stream. The response event stream is provided to the client computing device. The server-specified subscription filter facilitates filtering of events fetched for the long-lived request in a way that may not be possible or impractical if the subscription client were required to specify the filter in the long-lived request.

Abstract: A database management system receives a command defining a view of the database. The view definition is accepted without determining whether references to schema elements within the view definition are resolvable to existing elements of the database schema. A query of the view is received. In response to the query of the view, the database management system resolves references to schema elements in the view definition by determining whether the references correspond to data available for processing the query.

Abstract: Systems and methods to measure and affect focus, engagement, and presence of users may include measuring a variety of aspects of users engaged in particular activities. Individual user characteristics or preferences and attributes of activities may be taken into account to determine levels of focus for particular users and activities. A variety of sensors may detect aspects of users engaged in activities to measure levels of focus. In addition, a variety of output devices may initiate actions to affect levels of focus of users engaged in activities. Further, a variety of processing algorithms, including machine learning models, may be trained to identify desired levels of focus, to calculate current levels of focus, and to select actions to change or boost levels of focus. In this manner, activities undertaken by users, as well as interactions between multiple users, may be made more engaging, efficient, and productive.

Abstract: A system for database restoration across service regions. The system includes data storage and backup data storage in the first region. The system includes a frontend for the database service configured to receive, from a client, a request to restore a database to the first region from backups stored in another backup data storage in a second region and to receive an authentication token for the request from the client. The system also includes a backup restore manager service for the first region configured to send, to another backup restore manager service implemented in the second region, a credential request for a second region credential authorizing retrieval of the one or more other backups from the second region. The backup restore manager service sends a backup restore request to retrieve the backups from the other backup data storage and loads the backups to restore the database in the first region.

Abstract: Methods, systems, and computer-readable media for automated management of machine images are disclosed. A machine image management system determines that a trigger for a machine image build process has occurred. The machine image management system performs the machine image build process responsive to the trigger. The machine image build process generates a machine image, and the machine image comprises a plurality of operating system components associated with an application. The machine image is validated by the machine image management system for compliance with one or more policies. The machine image management system provides the machine image to one or more recipients. One or more compute resources are launched using the machine image, and the application is executed on the compute resource(s) launched using the machine image.