Abstract: A metal oxide varistor (MOV) includes an MOV body, a first electrode, a second electrode, and a thermal cut-off insulation shell. The MOV body is a crystalline microstructure with zinc oxide mixed with one or more other metal oxides. The first electrode is adjacent one side of the MOV body and is connected to a first radial lead. The second electrode is adjacent a second side of the MOV body and is connected to a second radial lead having a curved portion. The thermal cut-off insulation shell is adjacent the second electrode and has a protrusion, with the curved portion of the second radial lead being adjacent the protrusion.

Abstract: Techniques for providing increased support for deduplication and compression of encrypted storage volumes. The techniques include receiving, at a storage virtual machine (VM), a data encryption key (DEK) associated with encrypted volume data, in which the DEK is wrapped in a key encryption key (KEK). The techniques include receiving, at the storage VM from a client virtual machine (VM), a write request specifying the encrypted volume data. The techniques include obtaining, by the storage VM, the KEK from a key management system (KMS) embedded on the storage VM. The techniques include unwrapping, by the storage VM, the DEK using the KEK, and decrypting, by an IO decryptor hosted by the storage VM, the encrypted volume data using the DEK. The techniques include performing, by the storage VM, data reduction operations on the decrypted volume data, and storing, by the storage VM, the data-reduced volume data on a storage array.

Abstract: One embodiment of a method for controlling a robot includes generating a representation of spatial occupancy within an environment based on a plurality of red, green, blue (RGB) images of the environment, determining one or more actions for the robot based on the representation of spatial occupancy and a goal, and causing the robot to perform at least a portion of a movement based on the one or more actions.

Abstract: An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

Abstract: An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

Abstract: An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

Abstract: An image processing method is provided that automatically calculates Body Surface Area (BSA) score using machine learning techniques. A Felzenszwalb image segmentation algorithm is used to define proposed regions in each of a plurality of training set images. The training set images are oversegmented, and then each of the proposed regions in each of the plurality of oversegmented training set images are manually classified as being a lesion or a non-lesion. A Convolutional Neural Network (CNN) is then trained using the manually classified proposed regions in each of the plurality of training set images. The trained CNN is then used on test images to calculate BSA scores.

Abstract: The present invention relates to xanthone analogs. Such compounds may be used in the treatment of bacterial infections.

Abstract: An electronic system includes a control unit configured to generate a firmware register and store a system health information to the firmware register; and a communication unit, coupled to the control unit, configured to retrieve at least a portion of the system health information and communicate the portion of the system health information in response to a singular read request independent of an operational status of a host device.

Abstract: An electronic system includes a control unit configured to generate a firmware register and store a system health information to the firmware register; and a communication unit, coupled to the control unit, configured to retrieve at least a portion of the system health information and communicate the portion of the system health information in response to a singular read request independent of an operational status of a host device.

Abstract: The present invention relates to xanthone analogs. Such compounds may be used in the treatment of bacterial infections.

Abstract: An apparatus and method for packing a data bus by moving ports that are associated with assigned time slots from a first set of time slots to a second set of time slots on the data bus. After all the time slots that can be moved are moved, the remaining time slots are shifted on the data bus resulting in larger contiguous groupings of time slots.