This year's World Cup had half the planet watching. Yet few people know how well the video and audio streams at such events are protected behind the scenes. How hard would it be, say, for an attacker to tamper with the media at the production facility? It turns out: easier than it should be. For decades, broadcast production relied on dedicated point-to-point cabling between the capture devices and the control room, whose physical isolation was, in effect, its security guarantee: tampering with a stream required touching a cable.

In this talk, Professor Daniel L. Gardner will discuss his recent book, Thresholds of Digital Gameplay (MIT Press), and current ongoing research. The book presents an analytical lens calibrated by peripheral-to-gameplay interfaces, interactions, infrastructures, and inequities.

Data-driven machine learning methods are making advances in many long-standing problems in robotics, including grasping, legged locomotion, perception, and more. There are, however, robotics applications for which data-driven methods are less effective. Data acquisition can be expensive, time consuming, or dangerous -- to the surrounding workspace, humans in the workspace, or the robot itself.

This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/YxfR9eZFd2oD7C886. Sign-up is possible from July 30 midday (12pm) until August 3 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by August 5 midday.

This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

Longitudinal data has become a major part of the landscape for clinical and epidemiological research. While variance is typically understood as nuisance – the “noise” in “signal-to-noise” – there is increasing evidence that underlying variability in subject-level measures over time may also be important in predicting future health outcomes of interest.