Abstract: Disk drive reallocation or replacement is disclosed. When performing a data protection operation, a health score is determined for each of the disk drives associated with the data protection operation. Replacement is recommended for each of the disk drives with an unfavorable health score. The recommendation may also identify a drive class based in part on the write or wear pattern. This allows unhealthy drives to be replaced prior to performing the data protection operation.

Abstract: A method includes searching a group of PITs, identifying one of the PITs as having an indicator of an occurrence of an event involving data associated with the identified PIT, restoring the data, running a production system copy using the data, and while the production system copy is running, taking increasingly granular backups of the data until the event is located. The event may be a corruption of the data, or other problem.

Abstract: One method includes receiving a request to restore data to a particular point in time, scanning a snapshot that corresponds to the point in time, based on the scanning, identifying any invalid data segments pointed to by the snapshot, for each of the invalid data segments, identifying a most recent, valid, version of that segment, and based on the request, restoring a set of valid data segments. In this method, no invalid segments are restored.

Abstract: A method includes accessing a group that comprises a group of PITs, replaying the PITs according to respective times at which the snapshots were taken, analyzing the PITs as they are being replayed, and based on the analyzing, identifying an event that has occurred within a time frame spanned collectively by the PITs. Replaying the PITs includes presenting the PITs, in order from oldest to newest, as a continuous stream of events.

Abstract: One example method includes receiving, at a remote site from a production site, copies of production site assets, storing, at the remote site, the copies of the production site assets, using, at the remote site, the copies of the production site assets to restore a temporary production site, running the temporary production site at the remote site, and restoring, from the remote site to the production site, the copies of the production site assets.

Abstract: Opportunistically transmitting backups through a time-limited air gap. A data protection system may predict rates of changes for one or more applications. The predicted rate of change allows a size of corresponding backups to be estimated. If there is time during which an air gap is available (closed), at least of the backups is selected and opportunistically transmitted to a vault through the air gap.

Abstract: A system can fit an artificial intelligence risk model to data based on labeled training data to produce a fitted model, wherein the labeled training data comprises respective features of users and products, and corresponding labels of respective maintenance costs applicable to the products, and wherein the fitted model comprises a tree model that is configured to differentiate between groups of the data with differing maintenance cost distributions. The system can, in response to applying a first input to the fitted model, produce an output that indicates a predicted maintenance cost distribution, wherein the first input comprises a feature of a user of the users and a product of the products.

Abstract: The present disclosure encompasses solid state forms of Belumosudil, in embodiments crystalline polymorphs of Belumosudil or salts thereof, processes for preparation thereof, and pharmaceutical compositions thereof.

Abstract: A forensic kit with a granular infected backup. A forensic engine may evaluate a production system that is infected with malware or other corruption and generate a forensic kit. The forensic kit may include copies of components of the production system that are infected or that are sufficiently related to infected components. The forensic kit may be provided to investigators.

Abstract: A system can determine a first output from an explainable artificial intelligence risk model based on a first input, wherein the first input indicates a first computing configuration, and wherein the first output indicates a first predicted maintenance cost of the first computing configuration during a time period. The system can determine a second output from the explainable artificial intelligence risk model based on a second input, wherein the second input indicates a second computing configuration that differs from the first computing configuration, and wherein the second output indicates a second predicted maintenance cost of the second computing configuration during the time period. The system can, in response to determining that the second predicted maintenance cost is less than the first predicted maintenance cost, saving an indication of a difference between the second predicted maintenance cost and the first predicted maintenance cost.

Abstract: Redistributing disks based on disk wear patterns is disclosed. The wear patterns of disk drives in a storage system are learned or determined. When a restore operation is performed, the volumes to disk drive mappings are changed to balance the overall wear pattern of the storage system. This insures that, after the restore operation, disks that had comparatively lower wear levels are used more heavily while disks that had comparatively higher wear levels are used less heavily.

Abstract: A system can train an artificial intelligence risk model to produce a trained model, wherein labeled training data for the training comprises respective features of user accounts and products, and corresponding labels of respective support costs applicable to supporting the products. The system can perform reconstructive self-supervised learning on a group of features of a user account to produce a complete group of features that are specified for the user account. The system can, in response to applying an input to the trained model, wherein the input comprises the complete group of features and a product of the products, produce an output that indicates a predicted cost that corresponds to the input.

Abstract: Methods and systems for anomaly detection in a distributed system are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using an inference model. The inference model may be an autoencoder trained to reconstruct data that is intended to match input data to an extent considered acceptable by the system. To accurately perform anomaly detection, the inference model may require re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.

Abstract: The present disclosure relates to a signal generator including: a plurality of interpolators, each interpolator being configured to: receive a first input signal having a first phase, and a second input signal having a second phase; generate a plurality of interpolated signals based on a plurality of interpolations of the input signals, each interpolated signal having a respective phase based on the respective interpolation, and combine the interpolated signals to provide an output signal; the plurality of interpolators including: a first plurality of interpolators, each interpolator being configured to receive as input signals a first reference signal and a second reference signal; and a second plurality of interpolators, each interpolator being configured to receive as first input signal an output signal from an interpolator of the first plurality of interpolators and as second input signal another output signal from another interpolator of the first plurality of interpolators.

Abstract: A self-expandable medical device for advancement through vasculature to an expansion site in a blood vessel is disclosed. The device comprises a working portion and a connection portion that can be connected to a pusher. The working portion provides an outward radial force at every diameter between and including the diameter of a compressed and an expanded configurations, and comprises a plurality of crowns of cells comprising an open area bordered by two proximal cell struts, two distal cell struts, and two middle cell struts. The proximal cell struts each extend distally from a common proximal end to a proximal end of a respective one of the two middle cell struts. The distal cell struts each extend proximally from a common distal end to a distal end of a respective one of the two middle cell struts. Each middle cell strut is more flexible than the distal cell strut and proximal cell strut to which it extends.

Abstract: An expandable clot mobilizer device for extraction of an occlusion from a blood vessel is provided, which comprises a working portion including a plurality of crowns of cells. Each cell comprises an open area bordered by two proximal cell struts, two distal cell struts, and two middle cell struts. The proximal cell struts each extending distally from a common proximal end to a proximal end of a respective one of the two middle cell struts, the distal cell struts each extending proximally from a common distal end to a distal end of a respective one of the two middle cell struts, each middle cell strut in each crown bordering two adjacent cells in that crown and each middle cell strut being adapted and configured to be more flexible than the distal cell strut and the proximal cell strut to which it extends. The clot mobilizer also comprises a tapered portion.

Abstract: Methods and systems for anomaly detection in a distributed environment are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data and classify the anomalies based on magnitudes of anomalies using an inference model. Different magnitudes of anomalies may be keyed to different action sets in response to the presence of anomalies in data. To perform anomaly detection, the inference model may require re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.

Abstract: A system can comprise a communications antenna comprising a material that is configured to change shape in response to being stimulated with an external stimulus. The system can comprise an antenna performance detector that is configured to detect a measure of performance of the communications antenna. The system can comprise a distortion correction component that is configured to receive an indication of the measure of performance, determine an amount of distortion of the shape of the communications antenna based on the indication of the measure of performance, based on the amount of distortion, determine an amount of the external stimulus with which to stimulate the communications antenna, and selectively apply the amount of the external stimulus to the communications antenna to change the shape of the communications antenna.

Abstract: Methods and systems for detecting data drift while performing anomaly detection in a distributed environment are disclosed. To perform anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using a continuous inference model. To detect data drifts in data from the one or more data collectors, the anomaly detector may also detect anomalies in data obtained from one or more data detectors using a quantized inference model. The output of the continuous inference model may be compared to the output of the quantized inference model to determine whether the continuous inference model has adapted to data drift over time through re-training. Following anomaly detection and/or data drift detection, the data may be discarded to remove the data from the anomaly detector.

Abstract: Methods and systems for anomaly detection in a distributed environment are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using an inference model. To perform anomaly detection, the inference model may require periodic re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.