ion process for radiology fellowships is a new approach to evaluate the potential to enhance the applicant's experience during this process. This technology also allows for the evaluation of candidates without the need for in-person interaction. Further studies could streamline these methods to minimize work redundancy with traditional text assessments or even evaluate the acceptance of using only audiovisual content on smartphones.[This corrects the article DOI 10.2196/24475.]. Tricuspid regurgitation (TR) has a poor prognosis and limited treatment options and is frequently accompanied by right ventricular (RV) dysfunction. Transcatheter tricuspid valve interventions (TTVI) to reduce TR have been shown to be safe and feasible with encouraging early results. Patient selection for TTVI remains challenging, with the role of right ventricular (RV) function being unknown. The aims of this study were 1) to investigate survival in a TTVI-treated patient population and a conservatively treated TR population, and 2) to evaluate the outcome of TTVI as compared to conservative treatment stratified according to the degree of RV function. We studied 684 patients from the multicentre TriValve cohort (TTVI cohort) and compared them to 914 conservatively treated patients from two tertiary care centres. Propensity matching identified 213 pairs of patients with severe TR. As we observed a non-linear relationship of RV function and TTVI outcome, we stratified patients according to tricuspid annular plane systolic excursion (TAPSE) to preserved (TAPSE >17 mm), mid-range (TAPSE 13-17 mm) and reduced (TAPSE <13 mm) RV function. The primary outcome was one-year all-cause mortality. TTVI was associated with a survival benefit in patients with severe TR when compared to matched controls (one-year mortality rate 13.1% vs 25.8%; p=0.031). Of the three RV subgroups, only in patients with mid-range RV function was TTVI associated with an improved survival (p log-rank 0.004). In these patients, procedural success was associated with a reduced hazard ratio for all-cause mortality (HR 0.22; 95% CI 0.09, 0.57). TTVI is associated with reduced mortality compared to conservative therapy and might exert its highest treatment effect in patients with mid-range reduced RV function. TTVI is associated with reduced mortality compared to conservative therapy and might exert its highest treatment effect in patients with mid-range reduced RV function.The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants. Developing computer-based methods for assessing infant movements in videos is pivotal for improved cerebral palsy screening. Most existing methods use appearance-based features and are thus sensitive to strong but irrelevant signals caused by background clutter or a moving camera. Moreover, these features are computed over the whole frame, thus they measure gross whole body movements rather than specific joint/limb motion. Addressing these challenges, we develop and validate a new method for fidgety movement assessment from consumer-grade videos using human pose extracted from short clips. Human pose capture only relevant motion profiles of joints and limbs and are thus free from irrelevant appearance artifacts. The dynamics and coordination between joints are modeled using spatio-temporal graph convolutional networks. Frames and body parts that contain discriminative information about fidgety movements are selected through a spatio-temporal attention mechanism. We validate the proposed model on the cerebral palsy screening task using a real-life consumer-grade video dataset collected at an Australian hospital through the Cerebral Palsy Alliance, Australia. Our experiments show that the proposed method achieves the ROC-AUC score of 81.87%, significantly outperforming existing competing methods with better interpretability.Learning with label proportions (LLP) deals with the problem that the training data are provided as bags, where the label proportions of training bags rather than the labels of individual training instances are accessible. https://www.selleckchem.com/products/azd1656.html Existing LLP studies assume that the label proportions of all training bags are accessible. However, in many applications, it is time-consuming to mark all training bags with label proportions, which leads to the problem of learning with both marked and unmarked bags, namely, semisupervised LLP (SLLP). In this work, we propose semisupervised proportional support vector machine (SS-∝SVM), which extends the proportional SVM (∝SVM) model to its semisupervised version. To the best of our knowledge, SS-∝SVM is the first attempt to cope with the SLLP problem. Two realizations, alter-SS-∝SVM and conv-SS-∝SVM, which are based on alternating optimization and convex relaxation, respectively, are developed to solve the proposed SS-∝SVM model. Moreover, we design a cutting plane (CP) method to optimize conv-SS-∝SVM with a guaranteed convergence rate and present a fast accelerated proximal gradient method to solve the multiple kernel learning subproblem in conv-SS-∝SVM efficiently. Empirical experiments not only justify the superiority of SS-∝SVM over its supervised counterpart in classification accuracy but also demonstrate the high competitive computational efficiency of the CP optimization of conv-SS-∝SVM.We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees, such as stability and optimality at system level. Existing approximate/adaptive dynamic programming (ADP) results that consider system performance theoretically are not readily providing practically useful learning control algorithms for this problem, and reinforcement learning (RL) algorithms that address the issue of data efficiency usually do not have performance guarantees for the controlled system. This study fills these important voids by introducing innovative features to the policy iteration algorithm. We introduce flexible policy iteration (FPI), which can flexibly and organically integrate experience replay and supplemental values from prior experience into the RL controller. We show system-level performances, including convergence of the approximate value function, (sub)optimality of the solution, and stability of the system. We demonstrate the effectiveness of the FPI via realistic simulations of the human-robot system.