Radio Access Networks (RANs) for telecommunications represent large agglomerations of
interconnected hardware consisting of hundreds of thousands of transmitting devices (cells) …

Multivariate time-series data are gaining popularity in various urban applications, such as
emergency management, public health, etc. Segmentation algorithms mostly focus on …

One problem in real-world applications of reinforcement learning is the high dimensionality
of the action search spaces, which comes from the combination of actions over time. To …

We consider the problem of detecting and localizing the time-frequency span of unmanned
aerial vehicles (UAVs) which transmit Wi-Fi-type [1] signals in the ISM band. Interference is …

Being an effective long-distance mass transit, the railway will continue to flourish for its
limited carbon footprint in the environment. Ensuring the equipment's reliability and …

We investigate the use of unsupervised deep learning to create a general purpose
automated fault detection system for manufacturing equipment. Unexpected equipment …

Many software engineering tasks, such as testing, debugging, and anomaly detection can
benefit from the ability to infer a behavioral model of the software. Most existing inference …

There is a huge interest today to move towards highly autonomous vehicles. To establish a
framework that ensures robustness and reach the highly set qualifications set by authorities …

Validation of autonomous driving systems remains one of the biggest challenges that car
manufacturers must tackle in order to provide safe driverless cars. The complexity of this task …

This paper considers the use of recently proposed optimal transport-based multivariate
goodness-of-fit (GoF) test statistics, namely rank energy and its variant the soft rank energy …