Deep learning is the state-of-the-art learning algorithm for many machine learning tasks. Yet, training a deep learning model on a large data set is often time-consuming, taking several days or even months. During model training, it is desirable to offer a non-trivial progress indicator that can continuously project the remaining model training time and the fraction of model training work completed. This makes the training process more user-friendly. In addition, we can use the information given by the progress indicator to assist in workload management. In this paper, we present the first set of techniques to support non-trivial progress indicators for deep learning model training when early stopping is allowed. We report an implementation of these techniques in TensorFlow and our evaluation results for both convolutional and recurrent neural networks. Our experiments show that our progress indicator can offer useful information even if the run-time system load varies over time. In addition, the progress indicator can self-correct its initial estimation errors, if any, over time.

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Circular RNAs (CircRNAs) are a class of noncoding RNAs formed by backsplicing during cotranscriptional and posttranscriptional processes, and they widely exist in various organisms. CircRNAs have multiple biological functions and are associated with the occurrence and development of many diseases. While the biogenesis and biological function of circRNAs have been extensively studied, there are few studies on circRNA degradation and only a few pathways for specific circRNA degradation have been identified. Here we outline basic information about circRNAs, summarize the research on the circRNA degradation mechanisms and discusses where this field might head, hoping to provide some inspiration and guidance for scholars who aim to study the degradation of circRNAs.

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