Target selection is one of essential operation made available by interaction techniques in virtual reality (VR) environments. However, effectively positioning or selecting occluded objects is under-investigated in VR, especially in the context of high-density or a high-dimensional data visualization with VR. In this paper, we propose ClockRay, an occluded-object selection technique that can maximize the intrinsic human wrist rotation skills through the integration of emerging ray selection techniques in VR environments. We describe the design space of the ClockRay technique and then evaluate its performance in a series of user studies. Drawing on the experimental results, we discuss the benefits of ClockRay compared to two popular ray selection techniques - RayCursor and RayCasting. Our findings can inform the design of VR-based interactive visualization systems for high-density data.

Pulmonary arterial hypertension (PAH) prognosis prediction on 3D non-contrast CT images is one of the most important tasks for PAH treatment. It will help clinicians stratify patients into different groups for early diagnosis and timely intervention via automatically extracting the potential biomarkers of PAH to predict mortality. However, it is still a task of great challenges due to the large volume and low-contrast regions of interest in 3D chest CT images. In this paper, we propose the first multi-task learning-based PAH prognosis prediction framework, P$^{2}$-Net, which effectively optimizes the model and powerfully represents task-dependent features via our Memory Drift (MD) and Prior Prompt Learning (PPL) strategies. 1) Our MD maintains a large memory bank to provide a dense sampling of the deep biomarkers' distribution. Therefore, although the batch size is very small caused by our large volume, a reliable (negative log partial) likelihood loss is still able to be calculated on a representative probability distribution for robust optimization. 2) Our PPL simultaneously learns an additional manual biomarkers prediction task to embed clinical prior knowledge into our deep prognosis prediction task in hidden and explicit ways. Therefore, it will prompt the prediction of deep biomarkers and improve the perception of task-dependent features in our low-contrast regions. Our P$^{2}$-Net achieves a high prognostic correlation of the prediction and great generalization with the highest 70.19% C-index and 2.14 HR. Extensive experiments with promising results on our PAH prognosis prediction reveal powerful prognosis performance and great clinical significance in PAH treatment. All of our code will be made publicly available online.

The traditional gradient descent based optimization algorithms for neural network are subjected too many vulnerabilities, such as slow convergent rate, gradient vanishing and falling into local minima. Therefore, the alternative non-gradient descent learning algorithm was proposed and prevalently applied in kinds of domains, such as pseudoinverse learning algorithm (PIL). However, when a special variant of the PIL, taking the random configuration of weight parameters, is adopted, the generalization ability needs further improvement although it has excellent training efficiency. Thus, on consideration of integrating the idea of ensemble learning, we proposes two methods to enhance basic PIL. One method is equivalent to an additive model, which can raise the network's performance by introducing boosting mechanism, and the other is to adopt a recursive way to rectify the hidden layer output of the neural network, then the relative better model is used in the subsequent prediction. Comprehensive evaluating experiments are conducted on several datasets, and the experimental results illustrate that the our proposed methods are effective on the classification accuracy.