MICROFLUIDIC CHIPS FOR PARTICLE PURIFICATION AND FRACTIONATION

    公开(公告)号:WO2019142087A1

    公开(公告)日:2019-07-25

    申请号:PCT/IB2019/050272

    申请日:2019-01-14

    Abstract: Microfluid chips that comprise one or more microscale and/or mesoscale condenser arrays, which can facilitate particle purification and/or fractionation, are described herein. In one embodiment, an apparatus can comprise a layer of a microfluidic chip. The layer can comprise an inlet that can receive fluid,an outlet that can output a purified version of the fluid, and a condenser array coupled between and in fluid communication with the inlet and the outlet. The condenser array can comprise a plurality of pillars arranged in a plurality of columns. Also, a pillar gap sized to facilitate a throughput rate of the fluid of greater than or equal to about 1.0 nanoliter per hour can be located between a first pillar of the plurality of pillars in a first column of the plurality of columns and a second pillar of the plurality of pillars in the first column.

    DATA OBJECT REWRITE TO SEQUENTIAL STORAGE MEDIA

    公开(公告)号:WO2019123068A1

    公开(公告)日:2019-06-27

    申请号:PCT/IB2018/059607

    申请日:2018-12-04

    Abstract: A method includes writing a data set to a sequential access medium. The method also includes reading the data set immediately after being written to the sequential access medium in a read-while-write process to identify one or more faulty encoded data blocks, each of the one or more faulty encoded data blocks including at least one faulty codeword having symbols at least 10 bits in size. Moreover, the method includes rewriting a first of the one or more faulty encoded data blocks within a first encoded data block set to a particular logical track in the rewrite area of the sequential access medium selected from a predetermined subset of logical tracks. The predetermined subset of logical tracks includes D1+D2+1 logical tracks. Only one faulty encoded data block from a particular sub data set is rewritten in a single encoded data block set in the rewrite area.

    DATA DE-IDENTIFICATION BASED ON DETECTION OF ALLOWABLE CONFIGURATIONS FOR DATA DE-IDENTIFICATION PROCESSES

    公开(公告)号:WO2019116137A1

    公开(公告)日:2019-06-20

    申请号:PCT/IB2018/059453

    申请日:2018-11-29

    CPC classification number: G06F21/6254 G06F21/577 G06F2221/033

    Abstract: A system for de-identifying data determines one or more identifiers that identify an entity of a dataset. One or more data de-identification processes are identified and associated with the determined one or more identifiers. Each data de-identification process is associated with one or more sets of configuration options indicating information to preserve in the dataset. The identified data de-identification processes are executed on the dataset in accordance with the associated sets of configuration options to generate datasets with varying preserved information. The generated datasets are evaluated for privacy vulnerabilities and a data de-identification process and an associated set of configuration options are selected based on the evaluation. The selected data de-identification process is executed on the dataset according to the associated set of configuration options to produce a resulting de-identified data set. Embodiments include a method and computer program product for de-identifying data in substantially the same manner described above.

    ROBUST GRADIENT WEIGHT COMPRESSION SCHEMES FOR DEEP LEARNING APPLICATIONS

    公开(公告)号:WO2019111118A1

    公开(公告)日:2019-06-13

    申请号:PCT/IB2018/059516

    申请日:2018-11-30

    Abstract: Embodiments of the present invention provide a computer-implemented method for adaptive residual gradient compression for training of a deep learning neural network (DNN). The method includes obtaining, by a first learner, a current gradient vector for a neural network layer of the DNN, in which the current gradient vector includes gradient weights of parameters of the neural network layer that are calculated from a mini-batch of training data. A current residue vector is generated that includes residual gradient weights for the mini-batch. A compressed current residue vector is generated based on dividing the residual gradient weights of the current residue vector into a plurality of bins of a uniform size and quantizing a subset of the residual gradient weights of one or more bins of the plurality of bins. The compressed current residue vector is then transmitted to a second learner of the plurality of learners or to a parameter server.

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