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Recollecting Bones
(2016)
In the same “guarded, roundabout and reticent way” which Lindsay Barrett invokes for Australian conversations about imperial injustice, Germans, too, must begin to more systematically explore, in Paul Gilroy’s words, “the connections and the differences between anti-semitism and anti-black and other racisms and asses[s] the issues that arise when it can no longer be denied that they interacted over a long time in what might be seen as Fascism’s intellectual, ethical and scientific pre-history” (Gilroy 1996: 26). In the meantime, we need to care for the dead. We need to return them, first, from the status of scientific objects to the status of ancestral human beings, and then progressively, and proactively, as close as possible to the care of those communities from whom they were stolen.
Reflections of Lusáni Cissé
(2016)
Postcolonial Piracy
(2016)
Media piracy is a contested term in the academic as much as the public debate. It is used by the corporate industries as a synonym for the theft of protected media content with disastrous economic consequences. It is celebrated by technophile elites as an expression of freedom that ensures creativity as much as free market competition. Marxist critics and activists promote flapiracy as a subversive practice that undermines the capitalist world system and its structural injustices. Artists and entrepreneurs across the globe curse it as a threat to their existence, while many use pirate infrastructures and networks fundamentally for the production and dissemination of their art. For large sections of the population across the global South, piracy is simply the only means of accessing the medial flows of a progressively globalising planet.
Using an algorithm based on a retrospective rejection sampling scheme, we propose an exact simulation of a Brownian diffusion whose drift admits several jumps. We treat explicitly and extensively the case of two jumps, providing numerical simulations. Our main contribution is to manage the technical difficulty due to the presence of two jumps thanks to a new explicit expression of the transition density of the skew Brownian motion with two semipermeable barriers and a constant drift.
We consider a statistical inverse learning problem, where we observe the image of a function f through a linear operator A at i.i.d. random design points X_i, superposed with an additional noise. The distribution of the design points is unknown and can be very general. We analyze simultaneously the direct (estimation of Af) and the inverse (estimation of f) learning problems. In this general framework, we obtain strong and weak minimax optimal rates of convergence (as the number of observations n grows large) for a large class of spectral regularization methods over regularity classes defined through appropriate source conditions. This improves on or completes previous results obtained in related settings. The optimality of the obtained rates is shown not only in the exponent in n but also in the explicit dependence of the constant factor in the variance of the noise and the radius of the source condition set.
We prove statistical rates of convergence for kernel-based least squares regression from i.i.d. data using a conjugate gradient algorithm, where regularization against overfitting is obtained by early stopping. This method is related to Kernel Partial Least Squares, a regression method that combines supervised dimensionality reduction with least squares projection. Following the setting introduced in earlier related literature, we study so-called "fast convergence rates" depending on the regularity of the target regression function (measured by a source condition in terms of the kernel integral operator) and on the effective dimensionality of the data mapped into the kernel space. We obtain upper bounds, essentially matching known minimax lower bounds, for the L^2 (prediction) norm as well as for the stronger Hilbert norm, if the true
regression function belongs to the reproducing kernel Hilbert space. If the latter assumption is not fulfilled, we obtain similar convergence rates for appropriate norms, provided additional unlabeled data are available.
We elaborate a boundary Fourier method for studying an analogue of the Hilbert problem for analytic functions within the framework of generalised Cauchy-Riemann equations. The boundary value problem need not satisfy the Shapiro-Lopatinskij condition and so it fails to be Fredholm in Sobolev spaces. We show a solvability condition of the Hilbert problem, which looks like those for ill-posed
problems, and construct an explicit formula for approximate solutions.