No abstract

Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount of training data. To tackle this issue, we present a novel memory-augmented colorization model Memo-Painter that can produce high-quality colorization with limited data. In particular, our model is able to capture rare instances and successfully colorize them. We also propose a novel threshold triplet loss that enables unsupervised training of memory networks without the need of class labels. Experiments show that our model has superior quality in both few-shot and one-shot colorization tasks.

No abstract

Experiments are performed to understand the effects of surface orientation on the pool boiling characteristics of a highly wetting fluid from a flush-mounted, micro-porous-enhanced square heater. Micro-porous enhancement was achieved by applying copper and aluminum particle coatings to the heater surfaces. Effects of heater orientation on CHF and nucleate boiling heat transfer for uncoated and coated surfaces are compared. A correlation is developed to predict the heater orientation effect on CHF for those surfaces.

Apoptosis repressor with CARD (ARC) possesses the ability not only to block activation of caspase 8 but to modulate caspase-independent mitochondrial events associated with cell death. However, it is not known how ARC modulates both caspase-dependent and caspase-independent cell death.

This information is helpful for emergency vehicle drivers, safety practitioners, public safety agencies, and research communities to mitigate crash risks. It also offers ideas for researchers to advance technologies and strategies to further emergency vehicle safety on the road.

No abstract

Temporomandibular joint osteoarthritis (TMJ OA) is a degenerative condition of the TMJ led by a pathological tissue response of the joint under mechanical loading. It is characterized by the progressive destruction of the internal surfaces of the joint, which can result in debilitating pain and joint noise. Panoramic imaging can be used as a basic screening tool with thorough clinical examination in diagnosing TMJ OA. This paper proposes an algorithm that can extract the condylar region and determine its abnormality by using convolutional neural networks (CNNs) and Faster region-based CNNs (R-CNNs). Panoramic images are collected retrospectively and 1000 images are classified into three categories—normal, abnormal, and unreadable—by a dentist or orofacial pain specialist. Labels indicating whether the condyle is detected and its location enabled more clearly recognizable panoramic images. The uneven proportion of normal to abnormal data is adjusted by duplicating and rotating the images. An R-CNN model and a Visual Geometry Group-16 (VGG16) model are used for learning and condyle discrimination, respectively. To prevent overfitting, the images are rotated ±10° and shifted by 10%. The average precision of condyle detection using an R-CNN at intersection over union (IoU) >0.5 is 99.4% (right side) and 100% (left side). The sensitivity, specificity, and accuracy of the TMJ OA classification algorithm using a CNN are 0.54, 0.94, and 0.84, respectively. The findings demonstrate that classifying panoramic images through CNNs is possible. It is expected that artificial intelligence will be more actively applied to analyze panoramic X-ray images in the future.