In this paper, we introduce DetailCLIP: A Detail-Oriented CLIP to address the limitations of
contrastive learning-based vision-language models, particularly CLIP, in handling detail …

Time series forecasting is crucial and challenging in the real world. The recent surge in
interest regarding time series foundation models, which cater to a diverse array of …

Time series forecasting is a critical and challenging task in practical application. Recent
advancements in pre-trained foundation models for time series forecasting have gained …

Autoencoder model architectures are attractive approaches for implementing intelligent
mobile/IoT sensing applications. This is attributed to their capability of offering efficient …

We present a novel frequency-based Self-Supervised Learning (SSL) approach that
significantly enhances its efficacy for pre-training. Prior work in this direction masks out pre …

Modern wearable devices can conveniently and continuously record various biosignals in
the many different environments of daily living, ultimately enabling a rich view of individual …

Many healthcare applications are inherently multimodal, involving several physiological
signals. As sensors for these signals become more common, improving machine learning …

Automatic radar signal recognition (RSR) plays a pivotal role in electronic warfare (EW), as
accurately classifying radar signals is critical for informing decision-making processes …

Classification models are expected to perform equally well for different classes, yet in
practice, there are often large gaps in their performance. This issue of class bias is widely …

Over the past decades, the rapid expansion of the wearable market has led to significant
advances in integrated devices and technologies, particularly in smartwatches. These …