Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.

Abstract: At an artificial intelligence system, training iterations of a first machine learning model are implemented. In a particular iteration, a group of data items are selected from an item collection using active learning, and respective labels selected from a set of tags are obtained for at least some of the items of the group. Using feature processing elements of a different machine learning model, a respective feature set corresponding to individual labeled items is generated in the iteration, and the feature sets are included in a training set used to train the first machine learning model. A trained version of the first machine learning model is stored after a training completion criterion is met.

Abstract: Systems and methods are provided for dynamic segmentation of users during an experiment based on changes to application data collected during the experiment. Data regarding application interactions and associated application metadata may be collected from users during application experiments that involve testing different variants of a feature or otherwise different user experiences. The data regarding application interactions and associated application metadata may be evaluated to discover segments of users and/or usage patterns (e.g., “cohorts”). During the experiment, the users may be dynamically re-segmented into new/different cohorts based on new application data being collected.

Abstract: A candidate attribute combination of a first data set is identified, such that the candidate attribute combination meets a data type similarity criterion with respect to a collection of data types of sensitive information for which the first data set is to be analyzed. A collection of input features is generated for a machine learning model from the candidate attribute combination, including at least one feature indicative of a statistical relationship between the values of the candidate attribute combination and a second data set. An indication of a predicted probability of a presence of sensitive information in the first data set is obtained using the machine learning model.

Abstract: A system configured to improve a voice quality during a communication session by performing bandwidth extension on a narrowband speech signal to generate a wideband speech signal with higher audio quality. For example, a system can extend a speech bandwidth from a narrowband signal having a first bandwidth (e.g., 4 kHz) to a wideband signal having a second bandwidth (e.g., 8 kHz or higher). To perform bandwidth extension, the system may include cascaded neural networks, such as two or more sub-pixel convolutional neural networks (CNNs) connected in series. In some examples, a first sub-pixel CNN may extend the speech bandwidth from 4 kHz to 6 kHz and a second sub-pixel CNN may extend the speech bandwidth from 6 kHz to 8 kHz. Alternatively, the system may use three or more cascaded neural networks and/or may extend the speech bandwidth above 8 kHz without departing from the disclosure.

Abstract: Hop counts can be determined in a variety of network segments using packet headers collected at boundaries of the segments. Agents executing on network devices are used to transmit packet header data, including a TTL budget, to a collector server computer. The collector server computer can discern signal (production flows) from noise (traceroutes and probing traffic), detecting packets that are at risk of being dropped due to TTL expiration. Alerts can be generated for packet flows with a dangerously low remaining TTL budget, which are at high risk of expiring due to operational events resulting in traffic temporarily traversing slightly longer paths. Source addresses can be identified for such packet flows and network devices through which the packet flows are traversing can be identified. Hop counts can then be computed for different network segments, such as an Internet segment, a backbone segment and a data center segment.

Abstract: Disclosed are systems and methods that determine a likelihood that an item page represents a risky item. For example, a machine learning model may be trained to determine a probability score that an item page corresponds to a topic of a plurality of topics based on item textual descriptions determined from the item page and search queries corresponding to the item page. Still further, one or more of a topic risk score, item risk score, cumulative item risk score, and/or a total item risk score may be determined. Each of the risk scores may be indicative of a likelihood that the item page represents a risky item.

Abstract: To achieve impact-less re-routing of traffic towards non best-paths in a network, two locator routes are originated for each network fabric: a primary locator and a secondary locator. In a steady state, the forwarding state on each router is created by recursion of endpoints identifiers through their originating location's primary locator-route. Secondary locator routes are not used in the steady state. At a desired point, a steering route is injected into the network device to redirect routing from the primary locator route to the secondary locator route. The secondary locator route has a different resultant port from the primary locator route to redirect incoming packets on a different network path.

Abstract: A first security rule set for traffic of a compute instance run at a virtualization server is stored in response to a programmatic request. A second security rule set for traffic of a network function accelerator of the virtualization server is stored in response to another programmatic request. Prior to delivery of network messages to the compute instance or from the accelerator, compliance with the applicable security rule set is verified.

Abstract: A detection mechanism is disclosed for identifying peers transmitting network traffic towards a cloud provider using routing prefixes that were not advertised for that peer. Once detected, an alarm condition can occur and the peer notified to remove any static route configurations. In one embodiment, a collector server computer can be used to acquire routing prefixes advertised to a peer. A second server computer can be used to obtain network traffic received from the peer. A comparison can then be made between the advertised prefixes and the network traffic being received. If network traffic is being received for unadvertised prefixes, then an alarm condition can occur. The detection mechanism can regularly inspect active traffic flows against BGP route advertisements at fixed intervals. For peers that are identified that are not in compliance with route advertisements, the alarm condition can occur for follow-up remediation.

Abstract: Movement of an autonomously motile device may be controlled by a user device. The user device may display image data captured by a camera of the autonomously motile device; a user may provide input, such as a touch gesture on a display screen, indicating a command for the autonomously motile device to move to a location indicated by the input. The autonomously motile device determines a coordinate of the input and a time of the touch input; the autonomously motile device then determines a direction and distance of a corresponding movement.

Abstract: A hybrid Bacon-Shor surface code is implemented using a fault tolerant quantum computer comprising hybrid acoustic-electric qubits. A control circuit includes an asymmetrically threaded superconducting quantum interference devices (ATS) that excites phonons in a mechanical resonator by driving a storage mode of the mechanical resonator and dissipates phonons from the mechanical resonator via an open transmission line coupled to the control circuit. The hybrid Bacon-Shor surface code only couples four phononic modes per given ATS, reducing cross-talk as compared to other systems that couple more phononic modes per ATS. Also, measurements are performed such that three parity measurements are taken between a phononic readout mode and a transmon qubit in a given syndrome measurement cycle.

Abstract: Disclosed are various embodiments for automated testing of systems and applications. A first computing device may be in data communication with a second computing device. The first computing device can receive a fault instruction specifying receive a fault instruction specifying a fault to inject into a virtual compute instance, parameters for the fault, a duration of the fault, and an identifier of the virtual compute instance. The first computing device can then send a command to a second computing device that hosts the virtual compute instance, wherein the command specifies at least the fault, the parameters for the fault, and the virtual compute instance. In turn, the second computing device can receive the command from the first computing device, save a pre-fault state of the virtual compute instance, and then introduce the fault into the virtual compute instance.

Abstract: Systems, methods, and computer-readable media are disclosed for modeled advertisement conversion attributions. An example method may include receiving first input data comprising first advertisement impression data and first advertisement conversion data, wherein the first input data includes one or more user identifiers associated with both the advertisement impression data and advertisement conversion data. The example method may also include training one or more machine learning models using the first input data. The example method may also include receiving second input data comprising second advertisement impression data, wherein user identifiers are unavailable for the second input data. The example method may also include determining, using the one or more machine learning models, second predicted conversion data associated with the second input data.

Abstract: Techniques to reduce overhead in a direct memory access (DMA) engine can include processing descriptors from a descriptor queue to obtain a striding configuration to generate tensorized memory descriptors. The striding configuration can include, for each striding dimension, a stride and a repetition number indicating a number of times to repeat striding in the corresponding striding dimension. One or more sets of tensorized memory descriptors can be generated based on the striding configuration. Data transfers are then performed based on the generated tensorized memory descriptors.

Abstract: Descriptors of machine learning tasks to be used to respond to analysis requests, indicating acceptable categories of runtime environments for the tasks and metrics to be collected from the tasks, are received via programmatic interfaces. In response to an analysis request, an orchestrator receives results from individual tasks as they become available, provides the results to other tasks, and causes a response to the request to be prepared using results from at least a subset of the tasks. Metrics collected from the tasks, and a visual representation of the tasks indicating their runtime environments are presented.

Abstract: For a given cross-data-store transaction request at a storage service, a coordinator transmits respective voting transition requests to a plurality of log-based transaction managers (LTMs) configured for the respective data stores to which writes are directed in the transaction. The LTMs transmit responses to the coordinator based on data-store-specific conflict detection performed using contents of the voting transition requests and respective data-store-specific state transition logs. The coordinator determines a termination status of the cross-data-store transaction based on the LTMs' responses, and provides an indication of the termination status to the LTMs.

Abstract: A service implemented at a first isolated virtual network of a provider network is added to a database of privately-accessible services. Configuration changes that enable network packets to flow between the first isolated virtual network and a second isolated virtual network without utilizing a network address accessible from the public Internet are implemented. Service requests originating at the second isolated virtual network are transmitted to the first isolated virtual network via private pathways of the provider network. Metrics corresponding to service requests directed from the second isolated network to the service are collected and provided to the respective owners of one or both isolated virtual networks.

Abstract: Methods, systems, and computer-readable media for read-only data store replication to edge locations are disclosed. A read-only data store is replicated to an edge server. A request for content is received at the edge server from a client device. The content is sought from a content cache at the edge server or from an origin server coupled to the edge server. Processing of the request is initiated, comprising encountering an event. The event is associated with a function specified by a customer, and the function specifies the read-only data store as input. The function associated with the event is executed at the edge server, including retrieval of data from the data store. The content is generated based at least in part on execution of the function. The content is sent from the edge server to the client device.

Abstract: Representations of sets of descriptors of reference objects are stored in a repository, with individual descriptors including information about entities identified in the reference objects. In response to a request to extract content from a particular data object, a reference object which satisfies a similarity criterion with respect to the particular data object is identified from the repository using the descriptors. A structural comparison of the particular data object and the reference object is performed to determine an entity related to another entity identified in the particular data object.