Abstract: Various embodiments of systems and methods for providing virtualized (e.g., serverless) broker clusters for a data streaming service are disclosed. A data streaming service uses a front-end proxy layer and a back-end broker layer to provide virtualized broker clusters, for example in a Kafka-based streaming service. Resources included in a virtualized broker cluster are monitored and automatically scaled-up, scaled-down, or re-balanced in a way that is transparent to data producing and/or data consuming clients of the data streaming service.

Abstract: Methods, systems, and computer-readable media for tracing service interactions without global transaction identifiers are disclosed. A service monitoring system receives an event message from a first service in a service-oriented system. The event message comprises one or more elements of data from a body of a service request from an upstream service. The first service initiates a sub-task associated with the service request. The service monitoring system receives one or more additional event messages from one or more additional services. The additional event message(s) comprise one or more additional elements of data from one or more additional service requests associated with one or more additional sub-tasks. The service monitoring system determines, based (at least in part) on the element(s) of data in the event message and the additional element(s) of data in the additional event message(s), that the sub-task and the additional sub-task(s) are associated with a higher-level task.

Abstract: Systems and methods for configuring a virtual machine provided by a remote computing system based on the availability of one or more remote computing resources and respective corresponding prices of the one or more remote computing resources are disclosed. Users are presented with an interface that allows for selection of individual remote computing resources to be included in a custom-configured virtual machine. Also, a customized corresponding price is determined for the custom-configured virtual machine based on user selections and current availability of the selected remote computing resources to be included in the custom-configured virtual machine.

Abstract: Apparatus and methods are disclosed herein for remote, direct memory access (RDMA) technology that enables direct memory access from one host computer memory to another host computer memory over a physical or virtual computer network according to a number of different RDMA protocols. In one example, a method includes receiving remote direct memory access (RDMA) packets via a network adapter, deriving a protocol index identifying an RDMA protocol used to encode data for an RDMA transaction associated with the RDMA packets, applying the protocol index to a generate RDMA commands from header information in at least one of the received RDMA packets, and performing an RDMA operation using the RDMA commands.

Abstract: Learning iterations, individual ones of which include a respective bucket group selection phase and a class boundary refinement phase, are performed using a source data set whose records are divided into buckets. In the bucket group selection phase of an iteration, a bucket is selected for annotation based on output obtained from a classification model trained in the class boundary refinement phase of an earlier iteration. In the class boundary refinement phase, records of buckets annotated as positive-match buckets for a target class in the bucket group selection phase are selected for inclusion in a training set for a new version of the model using a model enhancement criterion. The trained version of the model is stored.

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: 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: An autonomous mobile device (AMD) moves around a physical space while performing tasks. The AMD may have sensors with fields of view (FOVs) that are forward-facing. As the AMD moves forward, a safe region is determined based on data from those forward-facing sensors. The safe region describes a geographical area clear of obstacles during recent travel. Before moving outside of the current FOV, the AMD determines whether a move outside of the current FOV keeps the AMD within the safe region. For example, if a path that is outside the current FOV would result in the AMD moving outside the safe region, the AMD modifies the path until poses associated with the path result in the AMD staying within the safe region. The resulting safe path may then be used by the AMD to safely move outside the current FOV.

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: 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: 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: When a first search query including an image of an item is received to search for items associated with similar images, a second search query that includes text based on the image is generated. The text may be based on previous queries associated with the depicted item, visual features of the image, or text that is present in the image. The results from the first search query are scored based on their correspondence with the image of the item. Results having a score greater than a threshold are presented first in the output, followed by a selected number of results from the second search query. Results from the first search query that are associated with a score less than the threshold may be presented after the results from the second search query. This presentation increases the likelihood that items presented earlier in the output are relevant to the initial query.

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: A skid-steer delivery autonomous ground vehicle has a drive train and suspension that aids in maneuverability. The AGV has six wheels, each of which is powered by its own motor. The AGV has features that diminish the dragging effect on the wheels, either by choice of wheel features or by taking weight off the front wheels during turning.

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

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: 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: 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.