Abstract: A method for preparing a solid form of Bisphenol F is disclosed. The method comprises feeding Bisphenol F at a temperature in the range of 130-170° C. into an extruder; processing the fed Bisphenol F in a first mixing zone of the extruder, the first mixing zone maintained at a temperature in the range of 110-130° C., and then in a second mixing zone of the extruder, the second mixing zone maintained at a temperature in the range of 100-110° C.; conveying the processed Bisphenol F to a discharge zone of the extruder, the discharge zone maintained at a temperature in the range of 75-100° C.; discharging the processed Bisphenol F; and obtaining Bisphenol F in the solid form.

Abstract: Polyol component(s) for a recyclable polyurethane system is disclosed. The recyclable polyurethane system comprises a polyol component having a structural Formula (I), a polyol component having a structural Formula (II) or a polyol component having a structural Formula III and an isocyanate curing agent. A process(es) for preparing the polyol component having the structural Formula (I), the polyol component having the structural Formula (II) and the polyol component having the structural Formula (III) is also disclosed.

Abstract: An epoxy resin component(s) for a recyclable epoxy resin system is disclosed. The recyclable epoxy resin system comprises an epoxy resin component having a structural Formula I or an epoxy resin component having a structural Formula II and a curing agent. A process(es) for preparing the epoxy resin component having the structural Formula I and the epoxy resin system having the structural Formula II is also disclosed.

Abstract: This disclosure relates generally to system and method for parameter compression of capsule networks using deep features. The conventional capsule networks have distinct capability of retaining spatial correlations between extracted features but that comes at a cost of intensive computational, cost, memory usage and bandwidth requirement. The embodiments herein disclose a system and method for employing a lightweight deep features based capsule network that is capable of compressing the parameters. In an embodiment, the system includes a deep feature based capsule network such that the capsule layer is preceded by feature blocks. Said feature blocks comprises convolutional operation with a kernel size 3, followed by convolutional operation with kernel of size 1, and a Batch Normalization layer, and hence are able to extract deep features.

Abstract: An epoxy resin component(s) for a recyclable epoxy resin system is disclosed. The recyclable epoxy resin system comprises an epoxy resin component having a structural Formula I or an epoxy resin component having a structural Formula II and a curing agent. A process(es) for preparing the epoxy resin component having the structural Formula I and the epoxy resin system having the structural Formula II is also disclosed.

Abstract: An epoxy resin component(s) for a recyclable epoxy resin system is disclosed. The recyclable epoxy resin system comprises an epoxy resin component having a structural Formula I or an epoxy resin component having a structural Formula II and a curing agent. A process(es) for preparing the epoxy resin component having the structural Formula I and the epoxy resin system having the structural Formula II is also disclosed.

Abstract: Object annotation is images is tedious time consuming task when large volume of data needs to annotated. Existing methods limit to semiautomatic approaches for annotation. The embodiments herein provide a method and system for a deep network based architecture for automatic object annotation. The deep network utilized is a two stage network with first stage as an annotation model comprising a Faster Region-based Fully Convolutional Networks (F-RCNN) and Region-based Fully Convolutional Networks (RFCN) providing for two class classification to generate annotated images from a set of single object test images. Further, the newly annotated test object images are then used to synthetically generate cluttered images and their corresponding annotations, which are used to train the second stage of the deep network comprising the multi-class object detection/classification model designed using the F-RCNN and the RFCN as base networks to automatically annotate input test image in real time.

Abstract: Object annotation is images is tedious time consuming task when large volume of data needs to annotated. Existing methods limit to semiautomatic approaches for annotation. The embodiments herein provide a method and system for a deep network based architecture for automatic object annotation. The deep network utilized is a two stage network with first stage as an annotation model comprising a Faster Region-based Fully Convolutional Networks (F-RCNN) and Region-based Fully Convolutional Networks (RFCN) providing for two class classification to generate annotated images from a set of single object test images. Further, the newly annotated test object images are then used to synthetically generate cluttered images and their corresponding annotations, which are used to train the second stage of the deep network comprising the multi-class object detection/classification model designed using the F-RCNN and the RFCN as base networks to automatically annotate input test image in real time.

Abstract: The present disclosure includes systems and a methods for ingestion and processing of data in large volumes and varied data models. The system consists of a data intake adapter, tagging service, relation service, query service, persistence service and physical storage medium. The data intake adapters are implemented to support required data formats and models. The invention includes a method enabling assignments of tags to any data element that can be referenced in the system, including in some embodiments tables, rows, columns, data points, nodes, vectors, lists or other types. The invention further includes a method of data representation for tags data using hash tree data structures. The disclosure also includes a relations mechanism and service that is capable of defining relations between data elements. The disclosed system includes also a query service that leverages the internal data structures to provide efficient lookup and retrieval methods supporting vast range of analytical use cases.