Smart objects are increasingly widespread and their ecosystem, also known as the Internet
of Things (IoT), is relevant in many application scenarios. The huge amount of temporally …

It has long been established that predictive models can be transformed into lossless
compressors and vice versa. Incidentally, in recent years, the machine learning community …

Deep-learning-based compressor has received interests recently due to much improved
compression ratio. However, modern approaches suffer from long execution time. To ease …

Over the preceding decade, there has been a discernible surge in the prominence of
artificial intelligence, marked by the development of various methodologies, among which …

The sharply growing volume of time series data due to recent sensing technology
advancement poses emerging challenges to the data transfer speed and storage as well as …

In this work, we conceptualize the learning process as information compression. We seek to
equip generative pre-trained models with human-like learning capabilities that enable data …

Sensor-generated time-series data now constitutes a significant and growing portion of the
world's data due to the rapid proliferation of the Internet of Things (IoT). The transmission …

Large amounts of time series data are required to support data analysis at the edge in the
end-edge-cloud Industrial Internet of Things (IIoT) architecture. Reducing the storage cost is …

The storage of multi-media data can benefit from the advancements in general-purpose
lossless compression. The explosive growth of multi-media data volume in data centers …

Modern wind turbines are monitored by sensors that generate massive amounts of high
frequency time series that are ingested on the edge and then transferred to the cloud where …