Abstract: A monolithic silicon Josephson junction can be fabricated from a silicon wafer using semiconductor technologies such as gas immersion laser doping and smart cut technology. Rather than using a superconducting metal for the junction electrodes, silicon that has been highly doped with boron can serve as the superconducting material for the electrodes of the Josephson junction. A region of the silicon wafer that has not been doped serves as the tunnel barrier. Fabrication of the Josephson junction does not require a metallization process since the electrodes and tunnel barrier are formed from highly doped silicon and non-doped silicon, respectively. The single-crystal structure of the resulting Josephson junction yields low two-level system (TLS) noise.

Abstract: According to one embodiment, a computer-implemented method for file handling in a hierarchical storage environment includes performing a file access notification process for determining files related to the first file based on enhanced metadata and a priority list defining a likelihood of possible access, in response to receiving a file access notification corresponding to access of a first file. The related files are placed in a highest level storage tier, and the priority list is updated.

Abstract: A computer-implemented method according to one embodiment includes receiving a plurality of backup requests at a data storage system having a data storage library. At least some of the backup requests have unique service-metadata associated therewith. Each backup request is placed in a backup queue, where an ordering of the backup requests within the backup queue is based at least in part on the service-metadata. Backup operations are performed on the data storage system in accordance with the ordering of the backup requests within the backup queue. A computer program product for determining resource allocation in a backup environment according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: Methods, computer program products, devices, and/or systems are provided that perform the following operations: receiving data from an incoming data stream; separating the data into data chunks and storing the data chunks in accordance with a selection configuration matrix in a set of different matrix buffers until the matrix buffers are filled up; processing the data chunks by multiple processing units, each processing unit associated to a matrix buffer of the matrix buffers via an I/O block; providing the processed data chunks as inputs to a neural network component; and classifying the data and filtering the data based on a classification of the data by the neural network component.

Abstract: Methods, computer program products, devices, and/or systems are provided that perform the following operations: receiving data from an incoming data stream; separating the data into data chunks and storing the data chunks in accordance with a selection configuration matrix in a set of different matrix buffers until the matrix buffers are filled up; processing the data chunks by multiple processing units, each processing unit associated to a matrix buffer of the matrix buffers via an I/O block; providing the processed data chunks as inputs to a neural network component; and classifying the data and filtering the data based on a classification of the data by the neural network component.

Abstract: The present disclosure relates to a method for classifying a non-monetary donation. The method comprises: training a machine learning model comprising input neurons arranged in an input layer, each input neuron being operable for inputting an input value (Xn) of a parameter of the donation, and at least one hidden layer comprising multiple hidden neurons, each hidden neuron being operable for calculating a hidden layer value based on at least one input value and weights (w(Xn, Ym)) associated with the respective input neurons. The number of input parameters may be reduced based on ranking the weights (w(Xn, Ym)) related to the individual input parameters. The number of neurons of the hidden layer may be reduced based on ranking the weights related to the individual neurons of the hidden layer. The trained model, the reduced input parameters, and reduced hidden neurons may be provided for enabling the classification using the trained model.

Abstract: A computer-implemented method for performing an analytic workflow process is provided. The method includes storing data from multiple input sources into a data storage pool, performing at least one analytic workflow executed by computing nodes, identifying data, by an analyzer module, expected to be required for a downstream analytic step. Thereby, the identified data have not yet been received in the in-memory database. The method also includes triggering a push module to move the identified data from the data storage pool to the in-memory database. Additionally, the method includes monitoring, by the analyzer module, output data of an analytic step and identifying pointers to additional data expected to be required for a downstream analytic step of a parallel analytic workflow process different to the analytic step of the monitored output data, and moving, by a push module, the data referred to by the pointers to the in-memory database.

Abstract: The invention relates to a tiered storage system comprising tiers of data storage. The tiered storage system further comprises a processor; and a memory coupled to the processor. The memory comprises instructions which, when executed by the processor, cause the processor to: receive usage data descriptive of usage of memory extents stored by the tiered storage system; identify periodic usage patterns of the memory extents at least partially by calculating a correlation coefficient between the usage data and a predetermined list of conditions; determine a projected data usage for each of the memory extents using the periodic usage patterns, wherein the projected data usage is temporally dependent; sort the memory extents into usage bins according to the projected data usage; and control the tiers of data storage to migrate the at memory extents between the tiers of data storage using temporal changes of the sorting into the usage bins.

Abstract: A shared networked storage may be separated from a key vault system. A storage request with data to be stored and the storage request with a confidentiality rating may be received. The confidentiality rating may indicate a level of confidentiality the data is associated with. The storage request with the data and the confidentiality rating may be received via a shared networked storage access interface by a security layer. The data to be stored by the key vault system and the confidentiality rating may be encrypted on request of the security layer and into a data container. The shared networked storage may be categorized into Cloud zones. Each Cloud zone may be assigned a trust level. The data container may be stored in one of the Cloud zones of the shared networked storage. The trust level of the one of the Cloud zones may correspond to the confidentiality rating.

Abstract: A computer-implemented method for performing an analytic workflow process is provided. The method includes storing data from multiple input sources into a data storage pool, performing at least one analytic workflow executed by computing nodes, identifying data, by an analyzer module, expected to be required for a downstream analytic step. Thereby, the identified data have not yet been received in the in-memory database. The method also includes triggering a push module to move the identified data from the data storage pool to the in-memory database. Additionally, the method includes monitoring, by the analyzer module, output data of an analytic step and identifying pointers to additional data expected to be required for a downstream analytic step of a parallel analytic workflow process different to the analytic step of the monitored output data, and moving, by a push module, the data referred to by the pointers to the in-memory database.

Abstract: A method, computer system, and a computer program product for integrated failure indication is provided. The present invention may include receiving a plurality of sensor data from a plurality of sensors mounted within the tape system. The present invention may then include determining that the received plurality of sensor data is abnormal. The present invention may then include generating an alert based on determining that the received plurality of sensor data is abnormal. The present invention may lastly include sending the generated alert to a program manager.

Abstract: A method for performance analysis and use management of a battery module is disclosed, wherein the battery module includes a multitude of interconnected battery cells and a battery management system with a plurality of dedicated analysis/control units (ACUs) that analyze performance of the battery module, the ACUs being assigned to individual battery cells and/or battery blocks of battery module. The method includes measuring current and voltage of one or more of an individual battery cell and a battery block; calculating a charge removal from the one or more of the individual battery cell and the battery block; calculating a loading charge of the one or more of the individual battery cell and the battery block; determining the remaining charge of the one or more of the individual battery cell and the battery block; and failure monitoring of the one or more of the individual battery cell and the battery block.

Abstract: Data may be handled based on compressibility (i.e., whether the data may be further compressed or is not further compressible). A supervised learning model may be trained using a set of known further compressible data and a set of known non-compressible data. Using these data sets, the model may generate weighting factors and bias for the particular data sets. The trained model may then be used to evaluate a set of unclassified data.

Abstract: A cognitive hierarchical storage-management system receives feedback describing users' satisfaction with the way that one or more prior data-access requests were serviced. The system uses this feedback to associate each previously requested data element's metadata and storage tier with a level of user satisfaction, and to optimize user satisfaction when the system is trained. As feedback continues to be received, the system uses machine-learning methods to identify how closely specific metadata patterns correlate with certain levels of user satisfaction and with certain storage tiers. The system then uses the resulting associations when determining whether to migrate data associated with a particular metadata pattern to a different tier. Data elements may be migrated between different tiers when two metadata sets share metadata values. A user's degree of satisfaction may be encoded as a metadata element that may be used to train a neural network of a machine-learning module.

Abstract: A RAID (redundant array of independent storage devices) storage system where a file is stored at a physical protection level corresponding to a RAID protection level defined by metadata included in and/or with the file to be stored.

Abstract: A computer-implemented method according to one embodiment includes receiving a plurality of backup requests at a data storage system having a data storage library. At least some of the backup requests have unique service-metadata associated therewith. Each backup request is placed in a backup queue, where an ordering of the backup requests within the backup queue is based at least in part on the service-metadata. Backup operations are performed on the data storage system in accordance with the ordering of the backup requests within the backup queue. A computer program product for determining resource allocation in a backup environment according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: Data may be handled based on compressibility (i.e., whether the data may be further compressed or is not further compressible). A supervised learning model may be trained using a set of known further compressible data and a set of known non-compressible data. Using these data sets, the model may generate weighting factors and bias for the particular data sets. The trained model may then be used to evaluate a set of unclassified data.

Abstract: A method and system for tracking a course of a cable using electromagnetic waves. A first distance between a first transceiver and a second transceiver is determined by determining a total transmission time for a first wireless signal traveling in a linear line from the first transceiver to the second transceiver and back to the first transceiver in the linear line, wherein the first transceiver and the second transceiver each include a receiver and a transmitter. A second distance between the first transceiver and the second transceiver is determined by comparing a phase difference between the first signal received by the second transmitter and a second signal received by the second transmitter, wherein the second signal was transmitted from the first transceiver into the cable and received by the second transceiver wirelessly from the cable, and wherein the first signal and the second signal are phase aligned.

Abstract: Embodiments relate to a sputter chamber with a configurable surface in communication with a target material. A control system is in communication with the chamber and functions to prepare an alloy film by changing a composition of the configurable surface. As ingress gas is introduced to the chamber to interact with the changed composition, the interaction causes a reaction that produces an alloy film.

Abstract: Data may be handled based on compressibility (i.e., whether the data may be further compressed or is not further compressible). A supervised learning model may be trained using a set of known further compressible data and a set of known non-compressible data. Using these data sets, the model may generate weighting factors and bias for the particular data sets. The trained model may then be used to evaluate a set of unclassified data.