Communication, stereotypes and dignity: the inadequacy of the liberal case against censorship

J. S. Mill’s case against censorship rests on a conception of relevant communications as truth apt. If the communication is true, everyone benefits from the opportunity to exchange error for truth. If it is false, we benefit from the livelier impression truth makes when it collides with error. This classical liberal model is not however adequate for today’s world. In particular, it is inadequate for dealing with the problem of stereotyping. Much contemporary communication is not truth apt. Advertising and journalism, film and fashion portray images that can be neither verified nor refuted. Moreover, where these images do bear some relation to reality, any truth they may possess is not necessarily beneficial. Cultural stereotypes, for example, can be harmful even when true, to the extent that they reflect a distorted reality (the realities of life under conditions of injustice and exploitation). Exposure to such stereotypes affects a community’s self-conception. The resulting harms may be direct or indirect. Indirect harm is done when a stereotype affects a community’s capacity for self-determination, perpetuating existing inequalities by restricting the options its members understand to be available to them. Direct harm is done when a stereotype induces a distorted self-conception. Pace Kant, human dignity is not purely a function of our capacity to be authors of a universal moral law. It also resides in our capacity to achieve an undistorted self-conception. Thus true communications that reflect a distorted historical reality may threaten our dignity, through their effects on our self-conception, independent of any consequences they may have for self-determining action

On the very idea of a recovery model for mental health

Both in the UK and internationally, the ‘recovery model’ has been promoted to guide mental healthcare in reaction against what is perceived to be an overly narrow traditional bio-medical model. It has also begun to have an influence in thinking more broadly about mental health both for individuals and for communities and in the latter case has been linked to policies to promote social inclusion. In this widening application, however, there is a risk that the model becomes too broad to count as a model and thus to compete with other models such as a bio-medical model of health or illness. In this short paper we sketch some of the competing views of illness and health in order to locate and articulate a possible recovery model for mental health. We suggest that a distinct recovery model could be based on a view that places values at the centre of an analysis of mental health. Our aim, however, is to clarify the options rather than defend the model that emerges. We do, however, caution against one possible version of a recovery model. Thus if a recovery model were to be defended along the line we sketch we think that it would be better to construe the values involved on eudaimonic rather than hedonic lines

Stationary Phase Method in Discrete Wigner Functions and Classical Simulation of Quantum Circuits

One of the lowest-order corrections to Gaussian quantum mechanics in infinite-dimensional Hilbert spaces are Airy functions: a uniformization of the stationary phase method applied in the path integral perspective. We introduce a ``periodized stationary phase method'' to discrete Wigner functions of systems with odd prime dimension and show that the $\frac{\pi}{8}$ gate is the discrete analog of the Airy function. We then establish a relationship between the stabilizer rank of states and the number of quadratic Gauss sums necessary in the periodized stationary phase method. This allows us to develop a classical strong simulation of a single qutrit marginal on $t$ qutrit $\frac{\pi}{8}$ gates that are followed by Clifford evolution, and show that this only requires $3^{\frac{t}{2}+1}$ quadratic Gauss sums. This outperforms the best alternative qutrit algorithm (based on Wigner negativity and scaling as $\sim\hspace{-3pt} 3^{0.8 t}$ for $10^{-2}$ precision) for any number of $\frac{\pi}{8}$ gates to full precision

Discrete Wigner Function Derivation of the Aaronson-Gottesman Tableau Algorithm

The Gottesman-Knill theorem established that stabilizer states and operations can be efficiently simulated classically. For qudits with dimension three and greater, stabilizer states and Clifford operations have been found to correspond to positive discrete Wigner functions and dynamics. We present a discrete Wigner function-based simulation algorithm for odd-$d$ qudits that has the same time and space complexity as the Aaronson-Gottesman algorithm. We show that the efficiency of both algorithms is due to the harmonic evolution in the symplectic structure of discrete phase space. The differences between the Wigner function algorithm and Aaronson-Gottesman are likely due only to the fact that the Weyl-Heisenberg group is not in $SU(d)$ for $d=2$ and that qubits have state-independent contextuality. This may provide a guide for extending the discrete Wigner function approach to qubits

LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes

Deep neural network (DNN) architectures have been shown to outperform traditional pipelines for object segmentation and pose estimation using RGBD data, but the performance of these DNN pipelines is directly tied to how representative the training data is of the true data. Hence a key requirement for employing these methods in practice is to have a large set of labeled data for your specific robotic manipulation task, a requirement that is not generally satisfied by existing datasets. In this paper we develop a pipeline to rapidly generate high quality RGBD data with pixelwise labels and object poses. We use an RGBD camera to collect video of a scene from multiple viewpoints and leverage existing reconstruction techniques to produce a 3D dense reconstruction. We label the 3D reconstruction using a human assisted ICP-fitting of object meshes. By reprojecting the results of labeling the 3D scene we can produce labels for each RGBD image of the scene. This pipeline enabled us to collect over 1,000,000 labeled object instances in just a few days. We use this dataset to answer questions related to how much training data is required, and of what quality the data must be, to achieve high performance from a DNN architecture