This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few target data samples need to be labeled. In this scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by reverting to point-wise surrogates of distribution distances and similarities provides an effective solution. In addition, the approach has a high "speed" of adaptation, which requires an extremely low number of labeled target training samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.

Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for general cardiologists. The current document builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. The document thus aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying seven sets of requirements that may reduce algorithmic errors and biases. Finally, the document summarizes a list of reporting items as an itemized checklist that highlight steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.

Human metrology provides an important soft biometric, which can be used in challenging situations such as human identification at a distance, when traditional biometric traits such as fingerprints or iris cannot be easily acquired. We study the problem of predictability and correlation in human metrology, using the tools of uncertainty and differential entropy. We show that while various metrological features are highly correlated with each other, there exists some correlation clusters in human metrology, whereby measurements in a cluster tend to be highly correlated with each other but not with the others. Based on these clusters, we propose a two-step approach for predicting unknown body measurements. Using the same framework, we study the problem of estimating other soft biometrics such as weight and gender.

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