Location estimation or localization is one of the key components in IoT applications such as
remote health monitoring and smart homes. Amongst device-free localization technologies …

The generalization of deep learning has helped us, in the past, address challenges such as
malware identification and anomaly detection in the network security domain. However, as …

Classical molecular dynamics simulations are based on solving Newton's equations of
motion. Using a small timestep, numerical integrators such as Verlet generate trajectories of …

Ensuring power grid resiliency, forecasting climate conditions, and optimization of
transportation infrastructure are some of the many application areas where data is collected …

We review previous approaches to nowcasting earthquakes and introduce new approaches
based on deep learning using three distinct models based on recurrent neural networks and …

In this paper, we have proposed a new pipeline for landscape analysis of time-series
machine learning datasets that enables us to better understand a benchmarking problem …

Optimizing the time-series forecasting performance is a multi-objective problem which
enables the comparison of general applicability of methods across multiple use cases such …

Blurred small objects produced by crop**, war**, or intrinsically so, are challenging to
detect and classify. Therefore, much recent research is focused on feature extraction built on …

To facilitate the co-design of next generation hardware architectures, it is critical to
characterize the workloads of deep learning (DL) applications and assess their …

Classical molecular dynamics simulations are based on Newton's equations of motion and
rely on numerical integrators to solve them. Using a small timestep to avoid discretization …