Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision model…
The various topologies, traffic patterns and cost targets of optical networks have prevented the deployment of end-to-end solutions across multi-domains, and the optimization of the network as a wh…
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and…
Established process steps for design science research have not addressed the usability of technology artifacts beyond the original research setting. Building on what is already known about generali…
Despite the efficiency and scalability of machine learning systems, recent studies have demonstrated that many classification methods, especially Deep Neural Networks (DNNs), are vulnerable to adve…
Industry sentiment links income and wealth to private-label demand. The intuition is that decreasing income and wealth increases the demand for (cheaper) private labels. Whereas plausible causality…
Kubernetes is the de facto industry standard for orchestrating containerized applications in clouds. Performance analyses of Kubernetes are often conducted in resource-rich environments on the ente…
We present a photonically-excited antenna array at E-band for scanning by beam switching in wireless links. First, we discuss the proposed technique applied to photonic-enabled (sub)millimeter-wave…
Photonics die or integrated photonics modules co-packaged with compute engines have the potential to deliver significant improvements in power, bandwidth and reach needed to meet the computing and …