Turbulent Black Holes

We show that rapidly-spinning black holes can display turbulent gravitational behavior which is mediated by a new type of parametric instability. This instability transfers energy from higher temporal and azimuthal spatial frequencies to lower frequencies--- a phenomenon reminiscent of the inverse energy cascade displayed by 2+1-dimensional turbulent fluids. Our finding reveals a path towards gravitational turbulence for perturbations of rapidly-spinning black holes, and provides the first evidence for gravitational turbulence in an asymptotically flat spacetime. Interestingly, this finding predicts observable gravitational wave signatures from such phenomena in black hole binaries with high spins and gives a gravitational description of turbulence relevant to the fluid-gravity duality.Comment: 5+3 pages, 2 figures, corrected an error in the treatment of the driving mode; example and figures changed, discussion adde

Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of "Unremarkable Computing", that by augmenting the users' routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience

On combining information from multiple gravitational wave sources

In the coming years, advanced gravitational wave detectors will observe signals from a large number of compact binary coalescences. The majority of these signals will be relatively weak, making the precision measurement of subtle effects, such as deviations from general relativity, challenging in the individual events. However, many weak observations can be combined into precise inferences, if information from the individual signals is combined in an appropriate way. In this study we revisit common methods for combining multiple gravitational wave observations to test general relativity, namely (i) multiplying the individual likelihoods of beyond-general-relativity parameters and (ii) multiplying the Bayes Factor in favor of general relativity from each event. We discuss both methods and show that they make stringent assumptions about the modified theory of gravity they test. In particular, the former assumes that all events share the same beyond-general-relativity parameter, while the latter assumes that the theory of gravity has a new unrelated parameter for each detection. We show that each method can fail to detect deviations from general relativity when the modified theory being tested violates these assumptions. We argue that these two methods are the extreme limits of a more generic framework of hierarchical inference on hyperparameters that characterize the underlying distribution of single-event parameters. We illustrate our conclusions first using a simple model of Gaussian likelihoods, and also by applying parameter estimation techniques to a simulated dataset of gravitational waveforms in a model where the graviton is massive. We argue that combining information from multiple sources requires explicit assumptions that make the results inherently model-dependent.Comment: 9 pages, 3 figure

State estimation filtering algorithms for vehicle attitude determination using a dual-arc accelerometer array and 3-axis rate gyroscopes

Sensor measurements are corrupted by biases, noise and drift effects and, in order to provide accurate measurements, these errors need to be estimated and, thus, eliminated. The current model used an Extended Kalman filter for the estimation of rate gyroscope measurement errors. This work improves upon that filter by applying a more robust, more accurate and more reliable Unscented Kalman filter. In addition, an algorithm for estimating the accelerometer measurement errors is developed using control theory. Using the attitude estimate from the Unscented Kalman filter, an error signal is formed between that attitude and the attitude estimates from the accelerometer array(s). This error signal is then reduced by implementation of an innovative method using PID controllers to estimate, and reduce the effects of, accelerometer measurement errors. While this thesis uses a previously developed device and equations, it is a departure from the previous works as it considers parameters and variables that were ignored in those studies

The Simultaneity of Beginning Teachers’ Practical Intentions

Teachers use their practical intentions – their in-the-moment goals and concerns – to craft their spontaneous classroom decisions. This research study explored the content of (and relationship between) beginning teachers’ practical intentions by asking six student teachers in mathematics to participate in a stimulated recall interview of their teaching. These interviews were analyzed for the different practical intentions that teachers articulated as having experienced as they taught. Four prominent categories of practical intentions were found: the desire to maintain lesson momentum; the desire to cover content; the desire to support student needs; and the desire to foster independent student thinking. Furthermore, it was found that different practical intentions often occurred simultaneously, as teachers often expressed the desire to achieve multiple instructional goals within a given moment of instruction. Implications for teacher education are discussed

The End of College: Creating the Future of Learning and the University of Everywhere

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