Microsoft

In various examples, an image search is performed based on a query image where the result includes images with semantic information that matches the query image. For example, image encoders are used to extract information from images that are stored in an index. Continuing this example, when a query is obtained the index is searched to determine potential matches, and a comparison is performed between the query image and the potential matches to determine images with semantic information matching the query image.

Examples solutions for evaluating network connectivity between computing resources within a computing environment include: generating a network packet for a proxy ping at a first host server that provides a first virtual machine (VM), the network packet being originally generated by the first host server and not generated by the first VM, the network packet being created to include a proxy ping flag within a header of the network packet; capturing a sending timestamp; receiving a response network packet generated by a second host server in response to interception of the network packet without the network packet being sent through to the second VM, the network packet having been identified for interception by the second host server based on identifying the proxy ping flag within the header of the network packet; capturing a response timestamp; and computing a latency value based on a difference between the sending and response timestamps.

Methods, systems, and computer program products are provided that implement vector-related requests using query operators. For example, a system includes a database, a parser, a converter, an optimizer, and an execution engine. The database is associated with query operators that are non-vector-specific. The database stores a table with vector embeddings. The parser is configured to parse a request indicating a vector operation associated with the vector embeddings. The converter is configured to convert the vector operation into a logical operator tree comprising a representation of the request as a logical flow of the query operators, enabling the vector operation without vector-specific executable code or operators. The optimizer is configured to convert the logical operator tree into an executable plan. The execution engine is configured to execute the executable plan against the table with vector embeddings.

Methods, devices, systems, and computer program products are provided for adaptive determination of stylus tilt by selectively using gyroscopic and sensor data for accuracy of tilt determinations as signal conditions change. A device comprises an orientation detector that determines stylus tilt using a first tilt detector based on a first set of conditions detected at a first time. The device detects a second set of conditions at a second time. The device adapts tilt determination from the first tilt detector to a second tilt detector for determination of tilt at the second time. The device determines tilt at the second time using the second tilt detector. Adaptive tilt detection can vary tilt calculations by using different equations, using the same equation with different weights, blending the results of calculations using multiple equations, etc., to maintain the accuracy of tilt determinations as signal conditions change.

An isolated platform for responsible Al is described. In various examples, a method is performed by a computing device. Output is generated by executing at least part of an application in an isolated virtual machine with an input-output virtualized accelerator, where the application is an artificial intelligence application. The isolated virtual machine is used to check the output. In response to the check being successful, the output is returned. In response to the check being unsuccessful, an error message is returned.

Systems, methods, and computer program products are disclosed for thermal and power aware virtual machine (VM) placement in a data center including a set of rows separated by a set of aisles. When deploying a set of VMs in a data center for workload execution, a subset of candidate aisles that have sufficient estimated airflow capacity to host the set of VMs and a subset of candidate rows that have sufficient estimated power capacity to host the VMs are determined. A set of servers is selected in the subset of candidate aisles and in the subset of candidate rows based at least on a workload type associated with the workload, a distribution of workloads of the workload type in the subset of candidate aisles, and a distribution of workloads of the workload type in the subset of candidate rows. The set of VMs are deployed to the set of servers.

Techniques are described herein that are capable of performing role-based approval of a resource lifecycle event with regard to a cloud-based resource. A request to perform a resource lifecycle event with regard to a cloud-based resource is received. A designated approver is identified. The designated approver has authority to grant approval to perform the resource lifecycle event with regard to the cloud-based resource as a result of the designated approver having an approver role, which is authorized to grant the approval. An explicit approval is obtained from the designated approver. The explicit approval grants approval to perform the resource lifecycle event with regard to the cloud-based resource. As a result of an approval criterion being satisfied, performance of the resource lifecycle event with regard to the cloud-based resource is triggered. The approval criterion includes obtainment of the explicit approval.

An anomaly detection model is tuned by training an anomaly detection condition tuning model using features of training input data provided to the anomaly detection model, training anomaly detection conditions used by the anomaly detection model to identify a detected anomaly in the training input data, and training data feedback identifying the detected anomaly as true or false. A first tuned training data condition is extracted for each feature by executing the anomaly detection condition tuning model on the features. A condition regression model is trained using second training data including the features of the training input data and each first tuned training data condition. Second tuned anomaly detection conditions are generated by executing the condition regression model on the new input data. The anomaly detection model is executable on the new input data using the second tuned anomaly detection conditions to identify an anomaly in the new input data.

Methods, systems, apparatuses, and computer program products are provided for protecting data in a memory of an integrated circuit (IC). A process token is obtained in a special purpose IC from a host that is external to and communicatively connected to the special purpose IC. The process token is stored in a first memory portion of the special purpose IC. In response to receiving a processing request from the host, the processing request is processed, and data generated by processing the processing request is written in a second memory portion of the special purpose IC. When a read request is received to read the data in the second memory portion, a determination is made whether the read request includes a read token that matches the previously stored process token. If the read token matches the process token, the data in the second memory portion may be returned to the host.

Described are examples for detecting emerging patterns in data. A detection system for detecting patterns outside of supervised machine learning models is provided for determining similarity scores between transactions to detect the emerging patterns. Transactions in the pattern can be reviewed to determine whether to render decisions on the transactions or similar subsequently occurring transactions. A self-correcting detection system is also provided for using machine learning models to correct for emerging patterns in the transaction data.