Graph topology inference in networks with co-evolving and interacting time-series is crucial
for network studies. Vector autoregressive models (VAR) are popular approaches for …

Landmark detection is critical in medical imaging for accurate diagnosis and treatment of
diseases. While there are many automated methods for landmark detection, the potential of …

Emerging memcapacitive nanoscale devices have the potential to perform computations in
new ways. In this article, we systematically study, to the best of our knowledge for the first …

Recent studies have shown that networks of memcapacitive devices provide an ideal
computing platform of low power consumption for reservoir computing systems. Random …

Unveiling causal relationships among time-series in multivariate observational data is a
challenging research topic. Such data may be represented by graphs, where nodes …

The quest for novel computing architectures is currently driven by (1) machine learning
applications and (2) the need to reduce power consumption. To address both needs, we …

The literature suggests that schizophrenia is associated with alterations in brain network
connectivity. We investigate whether large-scale Augmented Granger Causality (lsAGC) can …

Deep learning models can be applied successfully in real-work problems; however, training
most of these models requires massive data. Recent methods use language and vision, but …

Schizophrenia is associated with alternations in brain network connectivity. We investigate
whether large-scale Granger Causality (lsGC) can capture such alterations using resting …

In this work, we utilize a Transformer-based network for precise anatomical landmark
detection in chest X-ray images. By combining the strengths of Transformers and UNet …