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Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...
Sampling has always been at the heart of signal processing providing a bridge between the analogue world and discrete representations of it, as our ability to process data in continuous space is quite limited. Furthermore, sampling plays a key part in unde ...
This paper reports an algorithm to automatically identify the chain of events leading to a disruption, evaluating the so-called reference warning time. This time separates the plasma current flat-top of each disrupted discharge into two parts: a non-disrup ...
Bioaerosols are emitted from various sources into the indoor environment and can positively and negatively impact human health. Humans are the major source of bioaerosol emissions indoors, specifically for bacteria. However, efficient sampling to guarantee ...
Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...
Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...
The present invention describes an imaging system that allows visualization of a wide range of samples both in terms of morphology and in terms of material (e.g. density distribution, varying chemical composition, or anything that induces a change of optic ...
2021
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In recent years, wearable epidermal sweat sensors have received extensive attention owing to their great potential to provide personalized information on the health status of individuals at the molecular level. For on‐demand medical analysis of sweat in se ...
2021
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In the current work we present two generalizations of the Parallel Tempering algorithm in the context of discrete-timeMarkov chainMonteCarlo methods for Bayesian inverse problems. These generalizations use state-dependent swapping rates, inspired by the so ...
As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often consid ...