11 accepted papers
Recent research has increasingly focused on delivering drug-carrying magnetic particles to diseased areas using electromagnetic actuation (EMA) systems. Particularly, in these systems, creating a field-free point (FFP) and using it to steer magnetic particles in the desired direction has attracted s…
Implicit neural representations (INRs) encode data as continuous coordinate-based functions parameterized by neural networks, shifting downstream tasks such as image recognition to operate on functional rather than discrete representations. Despite their increasing adoption, the adversarial robustne…
Recent research has increasingly focused on delivering drug-carrying magnetic particles to diseased areas using electromagnetic actuation (EMA) systems. Particularly, in these systems, creating a field-free point (FFP) and using it to steer magnetic particles in the desired direction has attracted s
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc. However, one of the challenges with deep Transformer models is the oversmoothing problem, where repre…
Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning. Various representation learning methods for tabular data have been proposed, ranging from encoder-decoder structures to Transformers. Among these…
Recently, active research has been conducted on the therapeutic functions of capsule endoscopes. Here, we propose an active capsule system that captures images of the interior of the gastrointestinal tract (GI) and actively delivers therapeutic patches. The active capsule system mainly comprises the…
With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios. Owing to the recent advances in generative modeling, fake data generated by tabular data synthesis models become sophisticated and realistic. However…
Electromagnetic actuation of micro-/milli-sized agents has traditionally relied on large electromagnets positioned at considerable distances from the agents. As a result, the electromagnets consume kilowatts of power to overcome the limited generation of magnetic field gradients. Miniaturized electr
Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due to the complicated nature of real-world tabular d…
The problem of processing very long time-series data (e.g., a length of more than 10,000) is a long-standing research problem in machine learning. Recently, one breakthrough, called neural rough differential equations (NRDEs), has been proposed and has shown that it is able to process such data. The…
This paper proposes a novel algorithm to control wheels of a rover in rough terrains with keeping traction and minimizing energy consumption on the basis of observing a change of a robot velocity from a 4WD Kinematic model. Proposed kinematic-based rough terrain control (KRTC) can satisfy a rover to…