Abstract: A method for predicting errors in prescription claim data is performed by a claim analysis device. The method includes extracting historical claim features from successfully processed historical claims received from the data warehouse system. The method includes extracting pending claim features from a pending claim. The method includes applying a binarization process on the extracted historical claim features to obtain a binarized training feature set. The method includes applying the binarization process on the extracted pending claim features to obtain a binarized pending feature set. The method includes calculating an aggregate distance between the binarized pending feature set and the binarized training feature set. The method includes identifying the historical claim associated with the least aggregate distance as a predictive historical claim.

Abstract: In one example in accordance with the present disclosure, a device is described, which includes a first retainer to connect to a first section of a cable and a second retainer to connect to a second section of the cable. The first retainer and second retainer are connected together with a breakaway mechanism. The breakaway mechanism separates when tension is applied between the first section of the cable and the second section of the cable.

Abstract: A medical system comprises processing circuitry configured to: receive a first trained model, wherein the trained model has been trained using a first data set acquired in a first cohort; receive a second data set acquired in a second cohort; input data included in the second data set and data representative of the first trained model into a second trained model; and receive from the second trained model an affinity-relating value which represents an affinity between the data included in the second data set and the first trained model.

Abstract: An apparatus comprises processing circuitry configured to receive a plurality of training image data sets and a plurality of predetermined displacements. The processing circuitry is further configured to use the training image data sets and predetermined displacements to train a transformation regressor in combination with a discriminator in an adversarial fashion by repeatedly alternating a transformation regressor training process in which the transformation regressor is trained to predict displacements, and a discriminator training process in which the discriminator is trained to distinguish between predetermined displacements and displacements predicted by the transformation regressor.

Abstract: A medical image processing apparatus includes processing circuitry configured to apply a first trained model to input image data to obtain a first output based on the input data, where the input data includes clinical data. The processing circuitry is further configured to apply a second trained model to the input data to obtain a second output based on the input data, where the first trained model and the second trained model have been trained in dependence on a hierarchical relationship between the first output and the second output. The hierarchical relationship includes at least one of: a spatial hierarchy, a temporal hierarchy, an anatomical hierarchy, and a hierarchy of clinical conditions.

Abstract: A medical image processing apparatus includes processing circuitry configured to apply a first trained model to input image data to obtain a first output based on the input data, where the input data includes clinical data. The processing circuitry is further configured to apply a second trained model to the input data to obtain a second output based on the input data, where the first trained model and the second trained model have been trained in dependence on a hierarchical relationship between the first output and the second output. The hierarchical relationship includes at least one of: a spatial hierarchy, a temporal hierarchy, an anatomical hierarchy, and a hierarchy of clinical conditions.

Abstract: A medical system comprises processing circuitry configured to: receive a first trained model, wherein the trained model has been trained using a first data set acquired in a first cohort; receive a second data set acquired in a second cohort; input data included in the second data set and data representative of the first trained model into a second trained model; and receive from the second trained model an affinity-relating value which represents an affinity between the data included in the second data set and the first trained model.

Abstract: An apparatus comprises processing circuitry configured to receive a plurality of training image data sets and a plurality of predetermined displacements. The processing circuitry is further configured to use the training image data sets and predetermined displacements to train a transformation regressor in combination with a discriminator in an adversarial fashion by repeatedly alternating a transformation regressor training process in which the transformation regressor is trained to predict displacements, and a discriminator training process in which the discriminator is trained to distinguish between predetermined displacements and displacements predicted by the transformation regressor.

Abstract: An apparatus comprises processing circuitry configured to receive first image data; receive second medical image data; and apply a transformation regressor to perform a registration process to obtain a predicted displacement that is representative of a transformation between the first image data and the second image data; wherein the transformation regressor is trained in combination with a discriminator in an adversarial fashion by repeatedly alternating a transformation regressor training process in which the transformation regressor is trained to predict displacements, and a discriminator training process in which the discriminator is trained to distinguish between predetermined displacements and displacements predicted by the transformation regressor.

Abstract: An apparatus comprises processing circuitry configured to receive first image data; receive second medical image data; and apply a transformation regressor to perform a registration process to obtain a predicted displacement that is representative of a transformation between the first image data and the second image data; wherein the transformation regressor is trained in combination with a discriminator in an adversarial fashion by repeatedly alternating a transformation regressor training process in which the transformation regressor is trained to predict displacements, and a discriminator training process in which the discriminator is trained to distinguish between predetermined displacements and displacements predicted by the transformation regressor.

Abstract: Ubershaders may be used in a graphics development environment as an efficiency tool because many options and properties may be captured in a single shader program. Each selectable option of property in the shader code may be tagged with an attribute to indicate the presence of the selection. The single shader program embodying the many selectable options and properties may be compiled to an intermediate version that also embodies the many options and properties, along with at least remnants of the tagging attributes. Upon a request for executable code including indications of the desired selectable options or properties, generation of the executable code may proceed such that it includes only the desire selectable options and properties and not other selectable options and properties embodied in the source code.

Abstract: Ubershaders may be used in a graphics development environment as an efficiency tool because many options and properties may be captured in a single shader program. Each selectable option of property in the shader code may be tagged with an attribute to indicate the presence of the selection. The single shader program embodying the many selectable options and properties may be compiled to an intermediate version that also embodies the many options and properties, along with at least remnants of the tagging attributes. Upon a request for executable code including indications of the desired selectable options or properties, generation of the executable code may proceed such that it includes only the desire selectable options and properties and not other selectable options and properties embodied in the source code.

Abstract: The present invention concerns peptides comprising at least one motif having the amino acid sequence B1-X3-10-B2, wherein B1 and B2 are identical or different and each is a basic amino acid and X3-10 is a sequence of 3 to 10 identical or different non-acidic amino acids, and wherein the N-terminus of the peptide comprises a D-amino acid and/or includes a protecting group, collagen or hyaluronic acid conjugates comprising the same peptides and a therapeutic or diagnostic agent, and compositions and uses thereof. It also concerns peptides comprising at least one motif having the amino acid sequence B1-X3-10-B2, wherein B1 and B2 are identical or different and each is a basic amino acid and X3-10 is a sequence of 3 to 10 identical or different non-acidic amino acids, for use in the treatment or prevention of ocular diseases or conditions.

Abstract: Devices and methods of providing hardware support for dynamic type checking are provided. In some embodiments, a processor includes a type check register and support for one or more checked load instructions. In some embodiments, normal load instructions are replaced by a compiler with the checked load instructions. In some embodiments, to perform a checked load, an error handler instruction location is stored in the type check register, and a type tag operand is compared to a type tag stored in the loaded memory location. If the comparison succeeds, execution may proceed normally. If the comparison fails, execution may be transferred to the error handler instruction. In some embodiments, type prediction is performed to determine whether a checked load instruction is likely to fail.

Abstract: Devices and methods of providing hardware support for dynamic type checking are provided. In some embodiments, a processor includes a type check register and support for one or more checked load instructions. In some embodiments, normal load instructions are replaced by a compiler with the checked load instructions. In some embodiments, to perform a checked load, an error handler instruction location is stored in the type check register, and a type tag operand is compared to a type tag stored in the loaded memory location. If the comparison succeeds, execution may proceed normally. If the comparison fails, execution may be transferred to the error handler instruction. In some embodiments, type prediction is performed to determine whether a checked load instruction is likely to fail.

Abstract: Addressed is a system and method for remote data caching and replication by local copy maintenance of remote data within a SAN file system. Distributed Storage Tank (DST), an extension to a SAN file system, provides for transparent SAN client access of local copies by importing, exporting, and storing data using network file access protocols as well as by providing assurance of metadata and file content validity. A Remote Access Agent (RAA) handles protocol implementation and conversion necessary for communication with remote data sources. Controlled by a consistency policy, consistency is maintained by RAA fetching and updating local copies if modifications have occurred to a file since it was first stored as a local copy in local storage. Additionally, RAA returns metadata pertaining to the requested data. A SAN client obtains metadata corresponding to the requested data and utilizes it to directly access locally stored copies of remote data.

Abstract: A system provides referencing from one file system server to another through the use of a file system location database improving movement and replication of file systems. When a file system is moved from a first file system server a data object that references the file system remains in the first server and contains information used to find the current location of the file system. The actual location of the file system is stored in the separate file system location database which contains the locations of file systems on a number of file system servers. This allows the data in a file system to be replicated or moved without requiring updates to the data in any redirecting or referencing servers.

Abstract: Improved techniques are disclosed for accessing content in file systems, allowing file system clients to realize advantages of file system referrals even though a file access protocol used by the client is not specifically adapted for referral objects. (For example, the client may have a legacy file system protocol or a proprietary file system protocol which does not support referrals.) These advantages include a uniform name space view of content in a network file system, and an ability to locate content in a (nearly) seamless and transparent manner, even though the content may be dynamically moved from one location to another or replicated in different locations. A file system server returns a symbolic link in place of a referral, and an automated file mounting process on the client is leveraged to access the content using the link. Built-in crash recovery techniques of the file system client are leveraged to access moved content.

Abstract: A new and distinct Chrysanthemum plant named ‘95-157-6’ is provided.