Results for random-measure

qrng.anu.edu.au offers true random numbers to anyone on the internet. The random numbers are generated in real-time in our lab by measuring the quantum fluctuations of the vacuum. The vacuum is described very differently in the quantum mechanical context than in the classical context. Traditionally, a vacuum is considered as a space that is empty of matter or photons. Quantum mechanically, however, that same space resembles a sea of virtual particles appearing and disappearing all the time. This result is due to the fact that the vacuum still possesses a zero-point energy. Consequently, the electromagnetic field of the vacuum exhibits random fluctuations in phase and amplitude at all frequencies. By carefully measuring these fluctuations, we are able to generate ultra-high bandwidth random numbers.

Noisy sensor data, approximations in the equations that describe the system evolution, and external factors that are not accounted for all place limits on how well it is possible to determine the system's state. The Kalman filter deals effectively with the uncertainty due to noisy sensor data and to some extent also with random external factors. The Kalman filter produces an estimate of the state of the system as an average of the system's predicted state and of the new measurement using a weighted average. The purpose of the weights is that values with better (i.e., smaller) estimated uncertainty are "trusted" more. The weights are calculated from the covariance, a measure of the estimated uncertainty of the prediction of the system's state. The result of the weighted average is a new state estimate that lies between the predicted and measured state, and has a better estimated uncertainty than either alone. This process is repeated at every time step, with the new estimate and its covariance informing the prediction used in the following iteration. This means that the Kalman filter works recursively and requires only the last "best guess", rather than the entire history, of a system's state to calculate a new state.

The middleware makes sure any request to specified paths would have been preflighted if it was sent by a browser. We don't want random websites to be able to execute actual GraphQL operations from a user's browser unless our CORS policy supports it. It's not good enough just to ensure that the browser can't read the response from the operation; we also want to prevent CSRF, where the attacker can cause side effects with an operation or can measure the timing of a read operation. Our goal is to ensure that we don't run the context function or execute the GraphQL operation until the browser has evaluated the CORS policy, which means we want all operations to be pre-flighted. We can do that by only processing operations that have at least one header set that appears to be manually set by the JS code rather than by the browser automatically. POST requests generally have a content-type `application/json`, which is sufficient to trigger preflighting. So we take extra care with requests that specify no content-type or that specify one of the three non-preflighted content types. For those operations, we require one of a set of specific headers to be set. By ensuring that every operation either has a custom content-type or sets one of these headers, we know we won't execute operations at the request of origins who our CORS policy will block.

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