Abstract: Thermal Cut-Off (TCO) devices suitable for surface mount reflow processes are disclosed. The TCO devices are modeled after existing lead attached TCO device structures, but are improved with compact and miniaturized structures suitable for surface mount reflow operations. The arm and base terminals include pads for surface mount reflow. The base molds are designed for receiving the PTC device, bimetal device, and arm terminal feature and may operate using either a single base terminal or a multi-part base terminal. Multiple cover designs are disclosed to lower the heat capacity of the upper plate of the TCO device relative to the base terminal. A TCO device featuring an integrated arm and bimetal device terminal is also disclosed, with an updated base portion to support the integrated terminal.

Abstract: Some aspects of the present publication relate to a cable modem, i.e. CM, comprising a memory unit, in which instructions are saved, and a processor. The processor is configured to execute the instructions saved in said memory unit so that said electronic device executes the following operations: Receiving special service flow configuration information; said special service flow configuration information comprises information, which is configured by users and used for enabling the establishment of special service flows with one or a plurality of client stations connected with the CM; and establishing said special service flows with the cable modem terminal system, i.e. the CMTS, using the dynamic service flow technology based on said special service flow configuration information; said special service flows are only used for the communication with said one or plurality of client stations.

Abstract: Described herein are systems, methods, and apparatuses for implementing self-supervised domain-adaptive pre-training via a transformer for use with medical image classification in the context of medical image analysis.

Abstract: Described herein are means for learning semantics-enriched representations via self-discovery, self-classification, and self-restoration in the context of medical imaging. Embodiments include the training of deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a collection of semantics-enriched pre-trained models, called Semantic Genesis. Other related embodiments are disclosed.

Abstract: A Discriminative, Restorative, and Adversarial (DiRA) learning framework for self-supervised medical image analysis is described. For instance, a pre-trained DiRA framework may be applied to diagnosis and detection of new medical images which form no part of the training data. The exemplary DiRA framework includes means for receiving training data having medical images therein and applying discriminative learning, restorative learning, and adversarial learning via the DiRA framework by cropping patches from the medical images; inputting the cropped patches to the discriminative and restorative learning branches to generate discriminative latent features and synthesized images from each; and applying adversarial learning by executing an adversarial discriminator to perform a min-max function for distinguishing the synthesized restorative image from real medical images. The pre-trained model of the DiRA framework is then provided as output for use in generating predictions of disease within medical images.

Abstract: The vertical counterforce loading device includes a concrete support member, four transfer components, four connection components, a vertical force transmission component and a load test soil layer. The concrete support member is formed by pouring and concreting below the load test soil layer. The four transfer components are divided into two groups to be symmetrically and parallelly anchored in the concrete support member. The vertical force transmission component includes a load plate, a jack, a primary beam and a secondary beam arranged in sequence from bottom to top. The load plate is installed on the load test soil layer. Two secondary beams are connected crosswise to both ends of the primary beam, where end portions of the secondary beams are respectively connected to second ends of the connection components through reinforcement components. The device can improve work efficiency, reduce construction costs and improve safety.

Abstract: A self-supervised learning framework for empowering instance discrimination in medical imaging using Context-Aware instance Discrimination (CAiD), in which the trained deep models are then utilized for the processing of medical imaging. An exemplary system receives a plurality of medical images; trains a self-supervised learning framework to increasing instance discrimination for medical imaging using a Context-Aware instance Discrimination (CAiD) model using the received plurality of medical images; generates multiple cropped image samples and augments samples using image distortion; applies instance discrimination learning a mapping back to a corresponding original image; reconstructs the cropped image samples and applies an auxiliary context-aware learning loss operation; and generates as output, a pre-trained CAiD model based on the application of both (i) the instance discrimination learning and (ii) the auxiliary context-aware learning loss operation.

Abstract: An electronic device for processing real-time-streaming-protocol (RTSP) packets in a network to provide enhanced video-on-demand (VOD) services is provided. The electronic device includes a user interface, a non-transitory memory configured to store instructions including an application layer gateway (ALG) module and network service software, and a hardware processor. The hardware processor is configured to execute the instructions to store a list of mac-bridge clients that includes a MAC address and a MAC mask corresponding to each mac-bridge client, and determine whether at least one of the source and destination MAC address of a received RTSP packet is on the list. When at least one of the source and destination MAC address is on the list, the RTSP packet is processed using the network service software and a next RTSP packet is received; otherwise, the received RTSP packet is processed using the ALG module.

Abstract: Described herein are means for implementing systematic benchmarking analysis to improve transfer learning for medical image analysis.

Abstract: Described herein are means for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism, in the context of processing medical imaging. According to a particular embodiment, there is a system specially configured for diagnosing a Pulmonary Embolism (PE) within new medical images which form no part of the dataset upon which the AI model was trained.

Abstract: Described herein are means for generating pre-trained models for nnU-Net through the use of improved transfer learning techniques, in which the pre-trained models are then utilized for the processing of medical imaging.

Abstract: The present disclosure relates to an access point, a method, a medium, and a computer program product for the access point.

Abstract: Described herein are means for implementing annotation-efficient deep learning models utilizing sparsely-annotated or annotation-free training, in which trained models are then utilized for the processing of medical imaging. An exemplary system includes at least a processor and a memory to execute instructions for learning anatomical embeddings by forcing embeddings learned from multiple modalities; initiating a training sequence of an AI model by learning dense anatomical embeddings from unlabeled date, then deriving application-specific models to diagnose diseases with a small number of examples; executing collaborative learning to generate pretrained multimodal models; training the AI model using zero-shot or few-shot learning; embedding physiological and anatomical knowledge; embedding known physical principles refining the AI model; and outputting a trained AI model for use in diagnosing diseases and abnormal conditions in medical imaging. Other related embodiments are disclosed.

Abstract: Embodiments described herein include systems for implementing annotation-efficient deep learning in computer-aided diagnosis.

Abstract: Described herein are means for the generation of Transferable Visual Word (TransVW) models through self-supervised learning in the absence of manual labeling, in which the trained TransVW models are then utilized for the processing of medical imaging.

Abstract: Described herein are systems, methods, and apparatuses for actively and continually fine-tuning convolutional neural networks to reduce annotation requirements, in which the trained networks are then utilized in the context of medical imaging. The success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, it is tedious, laborious, and time consuming to create large annotated datasets, and demands costly, specialty-oriented skills. A novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework is presented to dramatically reduce annotation cost, starting with a pre-trained CNN to seek “worthy” samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning.

Abstract: Not only is annotating medical images tedious and time consuming, but it also demands costly, specialty-oriented expertise, which is not easily accessible. To address this challenge, a new self-supervised framework is introduced: TransVW (transferable visual words), exploiting the prowess of transfer learning with convolutional neural networks and the unsupervised nature of visual word extraction with bags of visual words, resulting in an annotation-efficient solution to medical image analysis. TransVW was evaluated using NIH ChestX-ray14 to demonstrate its annotation efficiency. When compared with training from scratch and ImageNet-based transfer learning, TransVW reduces the annotation efforts by 75% and 12%, respectively, in addition to significantly accelerating the convergence speed. More importantly, TransVW sets new records: achieving the best average AUC on all 14 diseases, the best individual AUC scores on 10 diseases, and the second best individual AUC scores on 3 diseases.

Abstract: Described herein are means for implementing medical image segmentation using interactive refinement, in which the trained deep models are then utilized for the processing of medical imaging.

Abstract: Described herein are means for generating source models for transfer learning to application specific models used in the processing of medical imaging.

Abstract: Methods, systems, and media for segmenting images are provided. In some embodiments, the method comprises: generating an aggregate U-Net comprised of a plurality of U-Nets, wherein each U-Net in the plurality of U-Nets has a different depth, wherein each U-Net is comprised of a plurality of nodes Xi,j, wherein i indicates a down-sampling layer the U-Net, and wherein j indicates a convolution layer of the U-Net; training the aggregate U-Net by: for each training sample in a group of training samples, calculating, for each node in the plurality of nodes Xi,j, a feature map xi,j, wherein xi,j is based on a convolution operation performed on a down-sampling of an output from Xi?1,j when j=0, and wherein xi,j is based on a convolution operation performed on an up-sampling operation of an output from Xi+1,j?1 when j>0; and predicting a segmentation of a test image using the trained aggregate U-Net.