Abstract: Systems and methods are disclosed including a memory and a processing device operatively coupled to the memory. The processing device can perform operations including receiving log data related to a first order of a set of error-handling operations performed on data residing in a segment of a memory device; applying an optimization model to the log data, wherein the optimization model is based on probability data of error recovery and latency data of the set of error-handling operations; and responsive to applying the optimization model to the log data, obtaining, as an output of the optimization model, a second order of the set of error-handling operations, wherein the second order adjusts an order of one or more error-handling operations of the set of error-handling operations in the first order.

Abstract: Systems and methods are disclosed including a memory device and a processing device operatively coupled to the memory device. The processing device can perform operations including applying an ordered set of error-handling operations to be performed on data residing in a segment of the memory device as an input to a trained machine learning model, wherein the trained machine learning model is based on latency data for previously-performed error-handling operations; and obtaining an output of the trained machine learning model, the output comprising a reordered set of error-handling operations to be performed on the data residing in the segment of the memory device, and wherein the reordered set adjusts an order of one or more error-handling operations of the ordered set of error-handling operations.

Abstract: An improved customizable shoe system that enables a wearer to personalize their shoes. The system includes a shoe including a sole and an upper extending about edges of the sole, and one or more transparent enclosures disposed about the shoe. The one or more enclosures each include an opening. A sheet containing one or more inserts corresponding in size and shape to the one or more enclosures, such that a user can remove the one or more inserts to be housed within the one or more enclosures.

Abstract: Synthetic, persistent RNA vectors for controlled expression of one or more heterologous polynucleotide sequences, each of the one or more heterologous polynucleotide sequences encoding for a reprogramming factor, are described. The vectors comprise a mechanism for silencing (off) and initiation or resumption (on) control of expression of the one or more reprogramming factors in the cell, tissue, or organ. Methods of using the vectors are also described, for example, to treat the age-related disease or condition, where the methods provide for treatment of the disease or condition, and in some embodiments, with retention of cellular identity.

Abstract: Methods for treating a cell, tissue, or organ and for treating an age-related disease or condition are provided, where the cell, tissue or organ is contacted with a synthetic, persistent RNA vector comprising one or more heterologous polynucleotide sequences, each of the one or more heterologous polynucleotide sequences encoding for a reprogramming factor. Contacting achieves expression of the one or more reprogramming factors in the cell, tissue, or organ to treat the age-related disease or condition. In an embodiment, the method is used to obtain a rejuvenated cell, tissue, or organ with retention of cellular identity.

Abstract: The subject matter described herein relates to compositions and methods for cellular rejuvenation, tissue engineering, and regenerative medicine by transient exposure of cells or tissues to synthetic, non-integrative mRNAs encoding reprogramming factors. Reprogramming factor encoding polynucleotides and corresponding polypeptides that trigger less immune response, are more stable, and/or exhibit altered activity than wild-type reprogramming factors are provided. RNA vectors expressing one or more of the improved reprogramming factor polynucleotide sequences are also provided.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Systems and methods for solid-state storage drive-level failure prediction and health metric are described. A plurality of host-write commands are received at a solid-state storage device. A number of drive-writes per day based on the on the plurality of host-write commands is determined. An aggregated amount of degradation to one or more internal non-volatile memory components based on the number of drive-writes per day is determined. Using a machine-learned model, a probability of failure value based on a set of parameter data and the aggregated amount of degradation to the non-volatile memory component is generated. An alert is generated, based on the probability of failure value or degradation threshold.

Abstract: A method of error management includes, in response to a read request for first data from a first storage device of a plurality of storage devices under one or more common data protection schemes, receiving a read uncorrectable indication regarding the first data, obtaining uncorrected data and metadata of an LBA associated with the first data, and obtaining the same LBA from one or more other storage devices of the plurality. The method further includes comparing the uncorrected data with the data and metadata from the other storage devices, speculatively modifying the uncorrected data based, at least in part, on the other data to create a set of reconstructed first data codewords, and, in response to a determination that one of the reconstructed first data codewords has recovered the first data, issuing a write_raw command to rewrite the modified data and associated metadata to the first storage device.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: A method of error management includes, in response to a read request for first data from a first storage device of a plurality of storage devices under one or more common data protection schemes, receiving a read uncorrectable indication regarding the first data, obtaining uncorrected data and metadata of an LBA associated with the first data, and obtaining the same LBA from one or more other storage devices of the plurality. The method further includes comparing the uncorrected data with the data and metadata from the other storage devices, speculatively modifying the uncorrected data based, at least in part, on the other data to create a set of reconstructed first data codewords, and, in response to a determination that one of the reconstructed first data codewords has recovered the first data, issuing a write_raw command to rewrite the modified data and associated metadata to the first storage device.

Abstract: Systems and methods for solid-state storage drive-level failure prediction and health metric are described. A plurality of host-write commands are received at a solid-state storage device. A number of drive-writes per day based on the on the plurality of host-write commands is determined. An aggregated amount of degradation to one or more internal non-volatile memory components based on the number of drive-writes per day is determined. Using a machine-learned model, a probability of failure value based on a set of parameter data and the aggregated amount of degradation to the non-volatile memory component is generated. An alert is generated, based on the probability of failure value or degradation threshold.

Abstract: Systems and methods for solid-state storage drive-level failure prediction and health metric are described. A plurality of host-write commands are received at a solid-state storage device. A number of drive-writes per day based on the on the plurality of host-write commands is determined. An aggregated amount of degradation to one or more internal non-volatile memory components based on the number of drive-writes per day is determined. Using a machine-learned model, a probability of failure value based on a set of parameter data and the aggregated amount of degradation to the non-volatile memory component is generated. An alert is generated, based on the probability of failure value or degradation threshold.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: Systems and methods for solid-state storage drive-level failure prediction and health metric are described. A plurality of host-write commands are received at a solid-state storage device. A number of drive-writes per day based on the on the plurality of host-write commands is determined. An aggregated amount of degradation to one or more internal non-volatile memory components based on the number of drive-writes per day is determined. Using a machine-learned model, a probability of failure value based on a set of parameter data and the aggregated amount of degradation to the non-volatile memory component is generated. An alert is generated, based on the probability of failure value or degradation threshold.

Abstract: This disclosure provides systems, methods and apparatus for packaging a device, for example an electromechanical systems (EMS) device, with a seal. In one aspect, the EMS device includes a primary seal positioned around a perimeter of the EMS device and in contact with a substrate and a cover plate, and a secondary seal positioned around portions of an outer periphery of the primary seal and in contact with the substrate and the cover plate. The primary seal can have low outgassing and low permeability of water vapor, and the secondary seal can have high mechanical strength that does not degrade when adhered to glass.