Systems and methods provide a learned difference metric that operates in a wide artifact space. An example method includes initializing a committee of deep neural networks with labeled distortion pairs, iteratively actively learning a difference metric using the committee and psychophysics tasks for informative distortion pairs, and using the difference metric as an objective function in a machine-learned digital file processing task. Iteratively actively learning the difference metric can include providing an unlabeled distortion pair as input to each of the deep neural networks in the committee, a distortion pair being a base image and a distorted image resulting from application of an artifact applied to the base image, obtaining a plurality of difference metric scores for the unlabeled distortion pair from the deep neural networks, and identifying the unlabeled distortion pair as an informative distortion pair when the difference metric scores satisfy a diversity metric.

A sequencing method, system and kit of low molecular weight heparin (LMWH) oligosaccharides are provided. The sequencing method includes: a sample preparation step: isolating or preparing a group of LMWH oligosaccharide mixture samples; a sample treatment step: performing complete enzymatic digestion and nitrous acid degradation on the LMWH oligosaccharide mixture samples to obtain an enzymatically digested eight-common-heparin-disaccharide array, a 3-O-sulfate group array, a 1,6-anhydro structure array, a nitrous acid degradation array, respectively; a data processing step: obtaining a disaccharide isomeric unit array according to the enzymatically digested eight-common-heparin-disaccharide array and the nitrous acid degradation array; a sequence database building step: building a sequence database according to the degree of polymerization of the oligosaccharide mixture, the disaccharide isomeric unit array, the 3-O-sulfate group array, and the 1,6-anhydro structure array; and a specific result output step: screening the sequence database according to input qualification information and then outputting a specific result file.

Disclosed herein is a system for enabling a default label to be configured for a network location created to store files. The default label can be assigned at a time when the files are uploaded to the network location. An owner of the network location can define the default label to be assigned to the files. Whenever an unlabeled file is uploaded to the network location, the unlabeled file automatically inherits the default label. Furthermore, the system is configured to consider an order of label priority when determining whether to assign a default label to a previously labeled file to be uploaded to the network location. The system is configured to upgrade a file with a preassigned label of lower priority to the default label, while permitting another file to be stored without a label change if the preassigned label is of higher priority compared to the default label.

Methods, non-transitory machine readable media, and computing devices that ensure correctness of file system analytics are disclosed. With this technology, a first generation number for a volume is incremented in response to a modification of a rule set that defines properties of objects of a file system associated with the volume. A determination is made when a second generation number in a first inode for a first one of the objects matches the first generation number. The first inode is identified based on a traversal of a directory tree associated with the file system. The modified rule set is applied to the properties for the first one of the objects to obtain values, when the second generation number fails to match the first generation number. Analytics data is output after the traversal has completed. The analytics data is generated in response to a query and is based on the values.

Disclosed herein are an apparatus and method for managing in-memory container storage. The apparatus includes one or more processors, executable memory for storing at least one program executed by the one or more processors, and a container file system for storing a container, which provides application virtualization. Here, the container file system includes a merged access layer, a container layer, and an image layer, and the at least one program provides an application with link information of files in the container layer and the image layer, thereby allowing the application to access the files.

Computer implemented methods for selective data deduplication in a multitenant environment are disclosed. Data deduplication of blocks written to a storage area associated with a tenant and redundant copies of the blocks written to other storage areas of other tenants is permitted or prevented based on tagging the first storage area associated with the tenant with a particular type of parameter. Responsive to detecting a write operation directed to the storage area tagged with a parameter indicating that deduplication is not permitted, a block to be written to the storage area is modified prior to hashing the block. Responsive to detecting a write operation directed to the storage area tagged with a parameter indicating that deduplication is permitted, a block to be written to the storage area is prevented from being modified prior to hashing the block.

A computer-implemented method for dynamic storage pricing in a multitenant environment is disclosed. The computer-implemented method includes dynamically modifying a storage cost for one or more tenants pointing to a block written to a storage area of the multitenant environment based, at least in part, on detecting a change in a number of tenants pointing to the block.

Mechanisms are provided for adjusting a configuration of data stored in a storage system. According to various embodiments, a storage module may be configured to store a configuration of data. A processor may be configured to identify an estimated performance level for the storage system based on a configuration of data stored on the storage system. The processor may also be configured to transmit an instruction to adjust the configuration of data on the storage system to meet the service level objective when the estimated performance level fails to meet a service level objective for the storage system.

Provided is a computer system to present information useful for achieving purposes related to an object by utilizing AI prediction. The computer system manages a prediction model for predicting an object event based on evaluation data and feature profiling database that defines a change rule of each of the plurality of feature values included in the evaluation data, generates change policy data by changing the plurality of feature values included in the evaluation data based on the feature profiling database, calculates an evaluation value indicating effectiveness of the change policy data, and generates display data for presenting the change policy data and the evaluation value as information useful for achieving purposes related to the object.

A distributed storage system may synchronously apply an Information Lifecycle Management (ILM) policy to objects at ingest. In one embodiment of synchronous ILM, three options are available for a user: balanced, strict, and dual commit. Dual commit refers to the behavior where one will always create two replicated copies in the same site and then apply ILM asynchronously. Strict refers to the behavior where the storage system attempts to apply the ILM policy synchronously on ingest, and if the storage system cannot the ingest of the object will fail. This ensures that the storage system can guarantee that ILM has been applied to recently ingested objects. Balanced refers to the behavior where the storage system attempts to apply ILM synchronously, but if the storage system cannot the storage system may fall-back to dual-commit.