Abstract: A deep-learning method and systems for analyzing optical coherence tomography (OCT) images based on a convolutional neural network are provided. The method includes extracting a feature from one or more three-dimensional OCT volumetric scan images and classifying the OCT images with respect to diabetic macular edema (DME) based on results the feature extracted. The step of extracting a feature from one or more three-dimensional OCT volumetric scan images is performed by a neural network such as a neural network based on a ResNet-34 architecture. The method can further include extracting a feature from one or more two-dimensional (2D) OCT B-scan images and classifying the OCT images with respect to DME based on results of the 2D feature extracted. The step of extracting a feature from one or more 2D OCT B-scan images is performed by a neural network such as a neural network based on a ResNet-18 architecture.

Abstract: An article of jewelry, e.g. necklace, bracelet or the like, comprising a flexible strand and one or more ornaments shaped, configured and dimensioned to be slidably retainable on the strand; one or more spaced apart magnetic components associated with (e.g. embedded) inside the strand; and a magnetic insert embedded in at least one of the ornament or ornaments which magnetic insert is attractable to the magnet components, whereby the one or more ornaments can be releasably retained on the strand by the force of magnetic attraction. Some examples of the strand can also have no ornaments and be stackable together using the force of magnetic attraction, and also a magnetic connector assembly comprising a first part coupled to a first end of the strand, and a second part coupled to a second end of the strand, each part having a magnetic component attractive to each other, the first and second parts being adapted to releasably connect together to form the strand into an endless loop.

Abstract: One embodiment of aggregate connecting cutting/serving board with glass holder (14) includes a generally planar cutting surface (16) that can rest on a countertop to enable cutting. In addition, the board is shaped like a jig-saw puzzle to allow more than two boards to connect to one another (10,14). One portion of the board is adapted to hold glasses (14) and able to rest on the countertop with the glass in place (18). In some versions, the boards are in various geometric shapes and sizes.

Abstract: A system including a first buffer module, a first encoder module, a control module, and a second buffer module. The first buffer module receives (i) a first block and (ii) a first logical block address (LBA) for the first block from a host, where the first block includes first data. The first encoder module generates a first checksum based on (i) the first data and (ii) the first LBA. The control module generates a second block, where the second block includes (i) the first data, (ii) the first LBA, and (iii) the first checksum. The second buffer module receives a third block from the first buffer module, where the third block includes a second LBA. The second buffer module determines whether the third block is different than the first block depending on whether the second LBA in the third block is different than the first LBA in the second block.