Abstract: A computer system de-identifies data by selecting one or more attributes of a dataset and determining a set of data de-identification techniques associated with each attribute. Each de-identification technique is evaluated with respect to an impact on data privacy and an impact on data utility based on a series of metrics, and a data de-identification technique is recommended for each attribute based on the evaluation. The dataset is de-identified by applying the de-identification technique that is recommended for each attribute. Embodiments of the present invention further include a method and program product for de-identifying data in substantially the same manner described above.

Abstract: Various embodiments predict drug-disease associations. In one embodiment, a plurality of disease similarity matrices and a plurality of disease similarity matrices are accessed. Each of the plurality of drug similarity matrices is associated with a different drug information source. Each of the plurality of disease similarity matrices is associated with a different disease information source. A known drug-disease association matrix is also accessed. The known drug-disease association matrix indicates if a given drug identified is known to treat a given disease. At least one drug-disease association prediction is generated based on the plurality of drug similarity matrices, the plurality of disease similarity matrices, and the known drug-disease association matrix. The at least one drug-disease association prediction identifies a previously unknown association between a given drug and a given disease, and a probability that the given disease is treatable by the given drug.

Abstract: Methods and systems for moving a dataset from a relational database to a non-relational database are disclosed. A method includes: copying, by a computing device, source datafiles from a relational database to each of a plurality of region servers associated with a non-relational database; generating, by the computing device, at least one table in the non-relational database from the copied source datafiles; and accessing, by the computing device, the at least one table in the non-relational database by reading raw data stored in a relational database format from the copied source datafiles on one of the plurality of region servers associated with the non-relational database.

Abstract: A computer-implemented method includes training a cognitive engine to recognize a set of tags in customer data, where the set of tags are associated with one or more early-access programs of one or more products. A first set of customer data associated with a first instance of a first early-access program is submitted to the trained cognitive engine, where the first instance is associated with a customer and a first product. A plurality of data packets associated with the tags are received from the trained cognitive engine, responsive to the first set of customer data. The first instance of the first early-access program is reconciled based at least in part on the plurality of data packets.

Abstract: Techniques regarding the synthesis of cyclic carbonate monomers are provided. For example, one or more embodiments described herein can comprise a method, which can include cyclizing a functionalized diol monomer with N,N?-carbonyldiimidazole, wherein the cyclizing produces a cyclic carbonate monomer and an imidazole carbamate. The method can also include activating the imidazole carbamate with an acid, wherein the activating promotes cyclization of the imidazole carbamate into the cyclic carbonate monomer.

Abstract: A method for protecting a machine learning model includes: generating a first adversarial example by modifying an original input using an attack tactic, wherein the model accurately classifies the original input but does not accurately classify at least the first adversarial example; training a defender to protect the model from the first adversarial example by updating a strategy of the defender based on predictive results from classifying the first adversarial example; updating the attack tactic based on the predictive results from classifying the first adversarial example; generating a second adversarial example by modifying the original input using the updated attack tactic, wherein the trained defender does not protect the model from the second adversarial example; and training the defender to protect the model from the second adversarial example by updating the at least one strategy of the defender based on results obtained from classifying the second adversarial example.

Abstract: A computer system de-identifies data by selecting one or more attributes of a dataset and determining a set of data de-identification techniques associated with each attribute. Each de-identification technique is evaluated with respect to an impact on data privacy and an impact on data utility based on a series of metrics, and a data de-identification technique is recommended for each attribute based on the evaluation. The dataset is de-identified by applying the de-identification technique that is recommended for each attribute. Embodiments of the present invention further include a method and program product for de-identifying data in substantially the same manner described above.

Abstract: A cooling system is provided which includes, for instance, a coolant supply manifold, a multifunction coolant manifold structure, and multiple cooling structures. The multifunction coolant manifold structure includes a coolant-commoning manifold and an auxiliary coolant reservoir above and in fluid communication with the coolant-commoning manifold. The multiple cooling structures are coupled in parallel fluid communication between the coolant supply and coolant-commoning manifolds to receive coolant from the supply, and exhaust coolant to the coolant-commoning manifold. The coolant-commoning manifold is sized to slow therein a flow rate of coolant exhausting from the multiple cooling structures to allow gas within the exhausting coolant to escape the coolant within the coolant-commoning manifold. The escaping gas rises to the auxiliary coolant reservoir and is replaced within the coolant-commoning manifold by coolant from the auxiliary coolant reservoir.

Abstract: A batched Cholesky decomposition method, system, and non-transitory computer readable medium for a Graphics Processing Unit (GPU), include mirroring a second problem matrix of a second problem to a first problem matrix of a first problem as paired matrices and shifting the second problem matrix by N+1 and combining the first problem matrix and the mirrored second problem matrix into one matrix of (N+1)×N, where the first problem shared memory comprises regular intervals, where the second problem shared memory is continuous, and where the GPU performs batched dense Cholesky decomposition with the one matrix from the combining to accelerate the Cholesky decomposition.

Abstract: A method of forming a complementary metal oxide semiconductor (CMOS) device is provided. The method includes forming a plurality of vertical fins on a substrate, and forming at least two dummy gates across the plurality of vertical fins. The method further includes forming a masking block on one of the at least two dummy gates, and removing the portions of the at least two dummy gates not covered by the masking block, wherein the portion of the one dummy gate covered by the masking block forms a dummy gate plug. The method further includes forming a gate dielectric layer on the exposed surfaces of the plurality of vertical fins and dummy gate plug, and forming a conductive gate layer on the gate dielectric layer, wherein the dummy gate plug physically separates two active gate structures.

Abstract: A method for analog-to-digital conversion with reconfigurable function mapping for acceleration of calculating an activation function of a neural network system includes determining, by a shared circuit, a set of voltage intervals using digital bits in a look-up table to define a shape of the activation function being mapped. The shared circuit determines a set of most significant bits (MSBs) for each voltage interval by storing additional bits in the look-up table corresponding to each voltage interval entry. Further, each of several per-neuron circuits determines whether its accumulated input voltage is in a received voltage interval, and if so, causing the set of MSBs to be stored. Each of the per-neuron circuits determines a set of least significant bits (LSBs) by performing a linear interpolation over the voltage interval. The set of MSBs and the set of LSBs are output as a result of the activation function with analog-to-digital conversion.

Abstract: A computer-implemented method according to one embodiment includes initiating a file system interface in a kernel space of a system cluster, capturing a data access request sent from a user space of the system cluster, utilizing the file system interface, where the data access request is sent utilizing one or more protocols of a first file system, and redirecting the data access request to a second file system of the system cluster, utilizing the file system interface.

Abstract: A tape cartridge for increasing tape drive head performance is provided. The tape cartridge comprises a cleaning tape, a tape guide arrangement that guides the cleaning tape within the cleaner cartridge, a cleaning fluid application arrangement that applies cleaning fluid to the cleaning tape within the cleaner cartridge, and a cartridge memory that stores cleaning count and motor taches count information. The tape guide arrangement is operable to bring the cleaning tape in contact with the cleaning fluid application arrangement to apply the cleaning fluid onto the cleaning tape when the cleaning tape is subject to a first tension level and avoid the contact with the cleaning fluid application arrangement when the cleaning tape is subject to a second tension level to prevent application of the cleaning fluid onto the cleaning tape.

Abstract: A short pointer mode application is loaded in an address space configured for use by a plurality of types of applications including the short pointer mode application and a long pointer mode application. The address space has a first portion addressable by short pointers of a defined size and a second portion addressable by long pointers of another defined size. The other defined size is different from the defined size. Based on executing the short pointer mode application, one or more short pointers of the short pointer mode application are converted to one or more long pointers; and the one or more long pointers are used to access memory within the first portion of the address space addressable by short pointers.

Abstract: An augmented-reality system interacts with a physical object that has been embossed or otherwise labeled with one or more bar-code identifiers. The system extracts data from each identifier and uses each element of extracted data to select a computer-generated texture or object. When displaying the augmented-reality environment to a user, the system either replaces the physical object with the computer-generated object or applies the computer-generated texture to a region of a rendered surface of the computer-generated object. The application's selection of computer-generated objects or textures may be modified at any time by a user command, a context-sensitive or environmental factor, an occurrence of a certain virtual event within the augmented-reality environment, or a configuration setting.

Abstract: Disclosed is an approach comprising a column partitioned into a plurality of partitions including an empty partition and a plurality of filled partitions each comprising data entries associated with a set of parameters having parameter values, the data entries compressed in accordance with a compression dictionary. The approach comprises receiving forecasted parameter values for an expected set of data entries to be stored in an empty partition; predicting a recurrence frequency of the data entries in the expected set using the forecasted parameter values by evaluating the respective compression dictionaries of the filled partitions with a machine learning algorithm; generating a predictive compression dictionary for the expected set of data entries based on the predicted recurrence frequency of the data entries in the expected set; receiving the expected set of data entries; and compressing at least part of the received expected set of data entries using the predictive compression dictionary.

Abstract: A mechanism is provided for determining distinct values of an attribute in a data table including the attribute and data records stored in a set of data blocks. Attribute value information is provided for each data block that comprises information indicating distinct values of the attribute in the data blocks. At least part of a current data block of the data table is scanned to determine current distinct values of the attribute. The current distinct values of the attribute of the current data block are added to a list of current distinct values of the data table. The scanning and adding are repeatedly performed for a further data block of the data table if the attribute value information of the further data block indicates at least one distinct value that is not present in the list. The list of current distinct values are then provided.

Abstract: A semiconductor device structure and method for fabricating the same. The semiconductor device structure includes a first vertical transport field effect transistor (VTFET) comprising a first semiconductor fin and a second VTFET stacked on the first VTFET. The second VTFET includes a second semiconductor fin that is separate and distinct from the first semiconductor fin. At least one insulating layer is disposed on a top surface of the first VTFET. The second VTFET is disposed on the at least one insulating layer. The method includes forming a first vertical VTFET on a first substrate and bonding a second substrate to and on top of the first VTFET. A second VTFET is formed on the second substrate.

Abstract: An approach is provided that receives a question at a question-answering (QA) system. The received question includes one or more terms, and the question pertains to a subject matter domain that is supported by the QA system. Analyzing a number of expressions included in a set of question-answer pairs (QA pairs), with the QA pairs being ground-truths established to in support of the subject matter domain. The analysis identifies whether a selected term from the question is a synonym for any of the expressions. The expressions that are identified as synonyms are then used in a QA pipeline that generates one or more candidate answers to the received question.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.