Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

The forthcoming generation of wireless technology, 6G, promises a revolutionary leap
beyond traditional data-centric services. It aims to usher in an era of ubiquitous intelligent …

In this work, we consider the problem of sequence-to-sequence alignment for signals
containing outliers. Assuming the absence of outliers, the standard Dynamic Time War** …

Truly real-life data presents a strong, but exciting challenge for sentiment and emotion
research. The high variety of possible 'in-the-wild'properties makes large datasets such as …

Abstract Motivation Oxford Nanopore sequencing is a rapidly developed sequencing
technology in recent years. To keep pace with the explosion of the downstream data …

Many time-series classification problems involve develo** metrics that are invariant to
temporal misalignment. In human activity analysis, temporal misalignment arises due to …

Machine learning algorithms for the analysis of time-series often depend on the assumption
that utilised data are temporally aligned. Any temporal discrepancies arising in the data is …

Accumulative representations provide a method for representing variable-length videos with
constant length features. In this study, we present aligned temporal accumulative features …

We present AlignNet, a model that synchronizes videos with reference audios under non-
uniform and irregular misalignments. AlignNet learns the end-to-end dense correspondence …

While multi-modal deep learning is useful in distributed sensing tasks like human tracking,
activity recognition, and audio and video analysis, deploying state-of-the-art multi-modal …