Abstract: Recurrent Neural Networks (RNNs) wherein a non-volatile memory (NVM) array provides a memory bank for the RNN. The RNN may include a Neural Turning Machine (NTM) and the memory bank may be an NTM matrix stored in the NVM array. In some examples, a data storage device (DSD) that controls the NVM array includes both a data storage controller and a separate NTM controller. The separate NTM controller accesses the NTM matrix of the NVM array directly while bypassing flash translation layer (FTL) components of the data storage controller. Additionally, various majority wins error detection and correction procedures are described, as well as various disparity count-based procedures.

Abstract: One example method includes defining an airgap control policy that specifies a threshold data value, generating a value for a set of data, determining whether the value that has been generated for the data meets or exceeds the threshold data value, and opening the air gap when the value that has been generated for the data meets or exceeds the threshold data value. The airgap is closed automatically when the value that has been generated for the data meets or exceeds the threshold data value.

Abstract: In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may execute a process; provide input data to the process as the process executes; receive output data from the process as the process executes; after executing the process, determine a neural network based at least on the input data to the process and the output data from the process; determine multiple binary neural networks from the neural network; determine a network of multiple logic gates based at least on the multiple binary neural networks of the neural network; and configure an integrated circuit based at least on the network of the multiple logic gates. For example, the integrated circuit may include at least one of a field programmable gate array, an application specific integrated circuit, a programmable array logic, and a complex logic device.

Abstract: Embodiments for automatically optimizing and persisting database views by receiving queries made to a database, wherein each query generates a respective database view, and generating a set of database maintained views generated by the queries. The system obtains, for each generated view, certain telemetry information about a respective view including latency, memory space utilization, and processor utilization, among other factors. It then scores each view of the generated views based on an base score modified by the obtained information to determine which one or more of the generated views to make persistent, and maintains the one or more persistent views to produce an optimized persistent set of database views. It further adapts later queries to use the optimized persistent views.

Abstract: A lens assembly of a viewing element for an endoscope has lenses and a barrel containing the lenses. The internal surface of the barrel is shaped in accordance with the relative position and size of the lenses and a lens holder encompassing at least a portion of the barrel. The barrel and/or the lens holder are injection-molded and can be variably positioned relative to each other. An optional adhesive layer that reduces or eliminates small particles from the viewing element is positioned on an inner surface of the barrel and/or lens holder and used to remove any internal particulate matter that may otherwise obstruct the field of view.

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: 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: 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: 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 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: 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: 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 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 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: 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 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: 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 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.