RESUMO
We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.
RESUMO
Older, multimorbid patients carry a high risk of suffering from a thromboembolic disease. The incidence of deep vein thrombosis in the outpatient setting is, depending on age, between 71 and 132 cases per 100,000 inhabitants and year. The rates increase significantly in the hospital setting and with increasing age. Today, pharmacological thrombolytic prophylaxis is the gold standard in patients needing surgical and internal medical care and is described in international evidence-based guidelines. However, the percentage of patients, who despite the guidelines do not receive pharmacological thrombolytic prophylaxis, is still very high. This is especially true for the group of multimorbid, very old patients. In the present work, the pathophysiological and clinical background for the increased susceptibility of thromboembolism in geriatric patients is summarized and the drugs currently available for pharmacological thromboembolic prophylaxis are presented. Hereby, special emphasis is placed on aspects of application in the elderly.
