Abstract: Techniques for application boundary detection are described. A request to identify application boundaries within customer software executed in a cloud environment is received. Metadata associated with the customer software is obtained, the metadata including an identification of application units, features associated with each identified application unit, and connectivity between application units. The application units are clustered into initial application unit clusters based at least in part on the features associated with each application unit and the connectivity between application units. The initial application unit clusters are iteratively combined into at least some increasingly larger application unit clusters to generate final application unit clusters. A result including an identification of the application units in the final application unit clusters is output.

Abstract: A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task, a quantum algorithm, or a quantum circuit using an intermediate representation. The quantum computing service is then configured to automatically translate the quantum task, quantum algorithm, or quantum circuit into a specific representation specific to a particular quantum computing technology selected by the customer to be used to execute the customer's quantum task, quantum algorithm, or quantum circuit.

Abstract: A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task, a quantum algorithm, or a quantum circuit using an intermediate representation. The quantum computing service is then configured to automatically translate the quantum task, quantum algorithm, or quantum circuit into a specific representation specific to a particular quantum computing technology selected by the customer to be used to execute the customer's quantum task, quantum algorithm, or quantum circuit.

Abstract: Disclosed are techniques for anomaly detection in time series data using an ML model. An untrained time series forecasting machine learning (ML) model may be provided as part of a class that includes an anomaly detection function, a features module, and a target transform module. In response to the class being invoked, an instance of the time series forecasting ML model may be trained using training time series data specified in the invocation of the class. The trained instance of the forecasting ML model may be persisted in an anomaly detection object along with instances of the anomaly detection function, the features module, and the target transform module. In response to receiving a call to the anomaly detection object, performing anomaly detection on time series data specified in the call using at least the trained instance of the forecasting ML model and the instance of the anomaly detection function.

Abstract: Disclosed is a method of detecting anomalies in time series data. The method includes computing a first bound for a first window of the time series a second bound for a second window of the time series, wherein the second window includes more samples of the time series data. The method also includes generating a first outlier status that indicates whether a current value of the time series data exceeds the first bound, and generating a second outlier status that indicates whether the current value of the time series data exceeds the second bound. The method also includes determining, by a processing device, whether an anomaly is detected in the time series data based on values of the first outlier status and the second outlier status. The method also includes generating an alert in response to determining that the anomaly is detected and sending the alert to a notification system.

Abstract: The subject technology receives first data. The subject technology transforms first data to a wide format, the wide format comprising a second table. The subject technology splits a set of rows into a set of shards of rows. The subject technology, for each shard from the set of shards, sends a particular user defined table function (UDTF), including a particular shard of rows, to a different execution node to perform a set of operations for determining a rolling correlation over a window size. The subject technology provides a set of output values of each particular UDTF corresponding to each shard from the set of shards in a second UDTF. The subject technology sends the second UDTF to a particular execution node to perform an aggregate operation. The subject technology receives a value of the rolling correlation from the particular execution node based on the aggregate operation.

Abstract: The subject technology receives first data. The subject technology transforms first data to a wide format, the wide format comprising a second table. The subject technology splits a set of rows into a set of shards of rows. The subject technology, for each shard from the set of shards, sends a particular user defined table function (UDTF), including a particular shard of rows, to a different execution node to perform a set of operations for determining a rolling correlation over a window size. The subject technology provides a set of output values of each particular UDTF corresponding to each shard from the set of shards in a second UDTF. The subject technology sends the second UDTF to a particular execution node to perform an aggregate operation. The subject technology receives a value of the rolling correlation from the particular execution node based on the aggregate operation.

Abstract: A system described herein may provide a technique for generating one or more predictive models of device availability, which may be used to predict whether a given device will be able to be reached via one or more networks to receive information, such as Over-the-Air (“OTA”) updates. The predictive models may be based on, for example, radio frequency (“RF”) metrics, device availability metrics, and timing offsets between times associated with such RF metrics and availability metrics. For a given device, based on RF metrics associated with the device and further based on a candidate time, the predictive model may be used to determine whether the device will be available at the candidate time.

Abstract: Disclosed is a fast and accurate time series forecasting algorithm that eliminates the need for hyperparameter tuning. Time series data may be analyzed using a quadratic function to determine a quadratic trend prediction, which is removed from the time series data to generate first detrended time series data. A moving median of the time series data is determined and the moving median is removed from the time series data to generate second detrended time series data. An amplitude scaling factor is determined based on the second detrended time series data and the first detrended time series data is descaled using the amplitude scaling factor to generate descaled time series data. The descaled time series data is analyzed to determine a seasonal prediction and a time series forecast is generated based on the seasonal prediction, the quadratic trend prediction, and the amplitude scaling factor.

Abstract: Using an attributes model of a time series forecasting model, determine a set of features based on time series data, the set of features including periodic components. The time series data may be divided into a set of segments. For each segment of the set of segments, a weight may be assigned using an age of the segment, resulting in a set of weighted segments of time series data. Using a trend detection model of the time series forecasting model, trend data from the set of weighted segments of time series data may be determined. A time series forecast may be generated by combining the set of features and the trend data.

Abstract: A network infrastructure modernization service described herein may allow a customer to migrate the underlying network infrastructure topology and configurations of the on-premises environment onto the cloud environment. By collecting the network infrastructure configurations and/or runtime metrics, generating a network model based on the configurations and/or runtime metrics, and applying modernization rules to the network model to generate a network infrastructure template, the network infrastructure modernization service described in the present application enables the customers to be migrate their on-premises applications as well as the underlying network infrastructure onto the cloud environment.

Abstract: Using an attributes model of a time series forecasting model, determine a set of features based on time series data, the set of features including periodic components. The time series data may be divided into a set of segments. For each segment of the set of segments, a weight may be assigned using an age of the segment, resulting in a set of weighted segments of time series data. Using a trend detection model of the time series forecasting model, trend data from the set of weighted segments of time series data may be determined. A time series forecast may be generated by combining the set of features and the trend data.

Abstract: Techniques are described for enabling a software modernization application to automatically generate and execute test cases as part of a containerization workflow used to modernize various types of legacy software applications. A software modernization application enables a user to identify a legacy application to convert into a containerized application. Once identified, the software modernization application automatically packages application artifacts and identified dependencies into container images and creates a deployment pipeline used to deploy the containerized application into testing and production environments of a service provider network, among other processes. The software modernization application also instruments the legacy application to generate log data reflecting requests and responses received and processed by the application during operation.

Abstract: The disclosure provides a laser surface treatment method and a laser surface treatment station. The laser surface treatment method is applied to a surface to be adhesively bonded of a casting of a vehicle body-in-white part, wherein the laser surface treatment method comprises the following steps: S1: providing the casting of the vehicle body-in-white part, the casting having been demolded and machined; and S2: performing laser surface treatment on the surface to be adhesively bonded of the casting, the surface to be adhesively bonded being used for a subsequent adhesive application process for connection of the casting, wherein the laser surface treatment is carried out by pulse laser, and the laser surface treatment employs the following parameters: an average laser power greater than 500 W; a laser frequency ranging from 5 kHz to 10 kHz; and laser single-point energy greater than 50 mJ.

Abstract: A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task, a quantum algorithm, or a quantum circuit using an intermediate representation. The quantum computing service is then configured to automatically translate the quantum task, quantum algorithm, or quantum circuit into a specific representation specific to a particular quantum computing technology selected by the customer to be used to execute the customer’s quantum task, quantum algorithm, or quantum circuit.

Abstract: A system described herein may generate one or more models based on relationship data associated with multiple objects. The models may include one or more thresholds (e.g., which may indicate potential collisions between objects). The system may compare relationship data, associated with a particular vehicle and a particular object, with the one or more thresholds associated with the one or more models, and determine whether the relationship data associated with the particular vehicle and the particular object is rare data with respect to the one or more models. When the relationship data associated with the particular vehicle and the particular object is not rare data, the system may refraining from causing the particular vehicle to perform a collision prevention measure, and when the relationship data associated with the particular vehicle and the particular object is rare data, the system may cause the particular vehicle to perform the collision prevention measure.

Abstract: A quantum computing service includes connections to multiple quantum hardware providers that are configured to execute quantum circuits using quantum computers based on different quantum technologies. The quantum computing service enables a customer to define a quantum algorithm/circuit in an intermediate representation and select from any of a plurality of supported quantum computing technologies to be used to execute the quantum algorithm/quantum circuit.

Abstract: A quantum computing service includes connections to multiple quantum hardware providers that are configured to execute quantum circuits using quantum computers based on multiple quantum technologies. The quantum computing service also includes at least two edge computing devices at the quantum hardware provider facilities. The edge computing devices are configured to be controlled by the quantum computing service to cause quantum jobs to be performed on the quantum computers at the quantum hardware provider facilities.

Abstract: This disclosure describes techniques implemented partly by a service provider network for containerizing applications. In an example, the techniques may include receiving annotated source code of an application to be containerized, analyzing one or more application component annotations included in the annotated source code to determine an application component that is to be included in a container associated with a containerized version of the application, and analyzing one or more method annotations included in the annotated source code to determine one or more methods of the application component to be included in an application programming interface (API) for the container. The container including the application component may then be built and the API for the container may be generated based at least in part on the one or more methods of the application component.

Abstract: An apparatus and method for providing a filtered air environment and streams of filtered air both in front of a wearer’s face at an angle to prevent contamination by pathogens and contaminants also supply filtered air to the nostrils and mouth, which also dilute any remaining particles. The device includes a blower element configured to direct a filtered airflow across a wearer’s face at an angle to push particles away and another stream of air to lead toward the nostrils and mouth to supply filtered air. The apparatus includes a fan or multiple fan elements to develop an airflow. A filter removes contaminants from the airflow and local environment of the wearer. Variations of a vent system have an intake conduit positioned close to the exhaust conduit, with or without an air deflector, to create a “microcirculation” that helps to capture the wearer’s exhaled particles and increases the filtration process.