In their paper, the researchers introduce a nanowire-based device to create high-electron-mobility tri-gate transistors for power-conversion applications. Based on nanoscale structures, the novel transistor design significantly reduces heat loss during the energy conversion process.
Contrary to expectations, the experiment revealed that the respondents held on to their views firmly, regardless of the celebrity inputs or their esteem in the eyes of the respondents. It was also clear that respondents liked to hear an opinion identical to their own even if it came from a disliked celebrity. Conversely, a dissenting opinion by a celebrity or expert reduced the respondent’s empathy for that person.
Two EPFL students have developed PowerSGD, an algorithm that allows compression of the needed bandwidth without compromising the accuracy of the training.
CrowdNotifier, a new protocol developed in part at EPFL. It alerts people who attended an event where there was a risk of COVID-19 infection.
We are investigating new stochastic theories and analytics including statistical learning techniques, machine training and inference systems and their applications to IoT, social media and big data platforms.
We are investigating technologies to maximize efficiency with in-memory data services, in-situ query processing on streamed data, and on-demand query engine customization using multi-objective compiler optimization.
We are investigating a three-pronged approach to holistic data platforms and datacenter efficiency: vertical integration to minimize data-movement; specialization to optimize work per service; and approximation to tailor work for output quality.
We are investigating technologies spanning from decentralized trust and cryptography to robustness and resiliency of natural processes to strengthen security, privacy and trust in data platforms and systems.