IMPORTANCE-DRIVEN TRANSFER FUNCTION DESIGN FOR VOLUME VISUALIZATION OF MEDICAL IMAGES

Ph.DDOCTOR OF PHILOSOPH

Flow and surface renewal of the viscous filaments in a high-speed disperser

High-speed rotating equipment can be used in the devolatilization of high-viscosity polymer fluids, where the surface renewal is regarded as an important factor on mass transfer. In this work, based on the verification of computational fluid dynamics simulation with the flow visualization experiment, the width, residence time, and velocity of the filament from a rotor were studied by the volume of fluid model, including the influence of rotational speed, fluid viscosity, and surface tension, and so forth. A surface renewal stretch model was built to acquire the surface renewal rate (S-p). The results show that S-p, along the stretching direction of the filament generally reaches a maximum value as soon as it is formed, while S-p decreases sharply in a relatively short distance. The Reynolds number and Weber number of the rotor together with the radial distance were used to describe S-p under various conditions for the evaluation of mass transfer performance of such high-speed dispersers

Flow and Surface Renewal of the Viscous Filaments in a High-Speed Disperser

High-speed rotating equipment can be used in the devolatilization of high-viscosity polymer fluids, where the surface renewal is regarded as an important factor on mass transfer. In this work, based on the verification of computational fluid dynamics simulation with the flow visualization experiment, the width, residence time, and velocity of the filament from a rotor were studied by the volume of fluid model, including the influence of rotational speed, fluid viscosity, and surface tension, and so forth. A surface renewal stretch model was built to acquire the surface renewal rate (S-p). The results show that S-p, along the stretching direction of the filament generally reaches a maximum value as soon as it is formed, while S-p decreases sharply in a relatively short distance. The Reynolds number and Weber number of the rotor together with the radial distance were used to describe S-p under various conditions for the evaluation of mass transfer performance of such high-speed dispersers

Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime

Semi-supervised learning (SSL) addresses the lack of labeled data by exploiting large unlabeled data through pseudolabeling. However, in the extremely low-label regime, pseudo labels could be incorrect, a.k.a. the confirmation bias, and the pseudo labels will in turn harm the network training. Recent studies combined finetuning (FT) from pretrained weights with SSL to mitigate the challenges and claimed superior results in the low-label regime. In this work, we first show that the better pretrained weights brought in by FT account for the state-of-the-art performance, and importantly that they are universally helpful to off-the-shelf semi-supervised learners. We further argue that direct finetuning from pretrained weights is suboptimal due to covariate shift and propose a contrastive target pretraining step to adapt model weights towards target dataset. We carried out extensive experiments on both classification and segmentation tasks by doing target pretraining then followed by semi-supervised finetuning. The promising results validate the efficacy of target pretraining for SSL, in particular in the low-label regime