Current guidelines recommend early discussion of the impact of therapy on potential fertility.
Additionally, irrespective of the choice for preservation, consultation with a fertility preservation (FP) team is associated with decreased patient and family regret and better quality of life. Thus, timely discussion of gonadal damage risk and fertility preservation options is important. Infertility is considered one of the most important late effects of childhood cancer treatment by patients and their families. Both COG and SIOP protocols aim to limit the use of gonadotoxic treatment, but unfortunately this cannot be avoided in all patients. The risk of infertility in Wilms tumor patients is low but increases with intensification of treatment including the use of alkylating agents, whole abdominal radiation or radiotherapy to the pelvis.
Chemotherapy and radiotherapy are established risk factors for gonadal damage and are used in both COG and SIOP Wilms tumor treatment protocols. The useage of advanced-QARC is quite same as QARC.The survival of childhood Wilms tumor is currently around 90%, with many survivors reaching reproductive age. Run the training process via 'python main.py' To run and test VQRL, one should follow these steps:Ĭopy the pretrain VQPN model to 'QARC/VQRL/model/', we recommend GRU model for higher performances.Ĭollect network traces and copy the traces to 'QARC/VQRL/train_sim_traces/', the network traces' format is described like this: 'timestamp\tbandwidth\n', and you can also use cooked_traces from Pensieve as network trace dataset. VQRL(Video Quality Reinforcement Learning) We provide several baselines for evaluating the performance of VQPN in 'baseline/' folder. We use early stopping method that automaticlly tear down the training process if overfitting occurs. Type 'cd gru/ & python main.py' for training GRU model or 'cd cnn/ & python main.py' for training CNN model.
There are two nerual network architecture candidates, GRU model (as shown in the paper) and CNN model (also reaches a high performance but a little bit lower than the GRU model performs), in different folders. To run and evaluate VQPN, you should follow these steps.Ĭopy the '*.h5' training dataset and test dataset from 'videodatasets/' to 'QARC/VQPN' The traditional QARC method is composed of two modules: VQPN and VQRL. Type 'process-vmaf.py' to generate h5py file for training.Run 'main.py' for generating video datasets including logs and video frames.Run 'trans.py' for transcoding 'mp4' video format to 'flv'.
AREN0321 QARC DOWNLOAD
AREN0321 QARC INSTALL
for computing vmaf score: git clone, then install it.
AREN0321 QARC HOW TO
In 'videodatasets/', we describe how to generate video datasets. Please install these package before using QARC. This work requires tensorflow, tflearn, scipy, matplotlib, opencv2 and tensorboard. To overcome the ``state explosion problem'', we design a neural network to predict future perceptual video quality as a vector for taking the place of the raw picture in the DRL's inputs. In detail, QARC uses deep reinforcement learning(DRL) algorithm to train a neural network to select future bitrates based on previously observed network status and past video frames. We then propose QARC (video Quality Aware Rate Control), a rate control algorithm that aims to obtain a higher perceptual video quality with possible lower sending rate and transmission latency. Nevertheless, we notice that there exists a trade-off between sending bitrate and video quality, which motivates us to focus on how to reach a balance between them. To tackle this problem, most proposed rate control methods work for providing high video bitrates instead of video qualities. Due to the fluctuation of throughput under various network conditions, how to choose a proper bitrate adaptively has become an upcoming and interesting issue. Real-time video streaming is now one of the main applications in all network environments. QARC (video Quality Aware Rate Control) Introduction
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