In this study, a novel partially parallel acquisition (PPA) method is presented which can be
used to accelerate image acquisition using an RF coil array for spatial encoding. This …

Recent studies reveal that a deep neural network can learn transferable features which
generalize well to novel tasks for domain adaptation. However, as deep features eventually …

Adversarial learning has been embedded into deep networks to learn disentangled and
transferable representations for domain adaptation. Existing adversarial domain adaptation …

Transfer learning is established as an effective technology in computer vision for leveraging
rich labeled data in the source domain to build an accurate classifier for the target domain. …

Early T-cell precursor acute lymphoblastic leukaemia (ETP ALL) is an aggressive malignancy
of unknown genetic basis. We performed whole-genome sequencing of 12 ETP ALL cases …

Extending the forecasting time is a critical demand for real applications, such as extreme
weather early warning and long-term energy consumption planning. This paper studies the long…

Deep networks have been successfully applied to learn transferable features for adapting
models from a source domain to a different target domain. In this paper, we present joint …

Process mining techniques are able to extract knowledge from event logs commonly available
in today’s information systems. These techniques provide new means to discover, monitor…

The recent success of deep neural networks relies on massive amounts of labeled data. For
a target task where labeled data is unavailable, domain adaptation can transfer a learner …

Time series analysis is of immense importance in extensive applications, such as weather
forecasting, anomaly detection, and action recognition. This paper focuses on temporal …