Metropolia

Niniejszy artykuł stanowi fragment czwartego rozdziału ostatniej książki Michaela Hardta i Antonia Negriego Commonwealth, w znacznie większej mierze niż ich poprzednie prace zainteresowanej wątkami miejskimi. Zawarta w nim propozycja to próba ujęcia współczesnego miasta w kategoriach biopolitycznych, co odróżniałoby je od wcześniejszych form organizacji przestrzennej, np. miasta przemysłowego i pozwalało na przejście od formy miejskiej do formy metropolitalnej. Główna teza tekstu głosi, że z uwagi na zachodzące współcześnie zmiany na gruncie produkcji i pracy, metropolia zajmuje miejsce zarezerwowane wcześniej dla fabryki („metropolia jest tym dla wielości, czym fabryka dla klasy robotników przemysłowych”). Staje się zarazem nieograniczonym ścianami obszarem produkcji tego, co wspólne, jak i obiektem kontestacji ogniskującej się w obliczu władzy imperialnej i kapitalistycznego wyzysku. Autorzy analizują w tym miejscu również dwie kolejne jakości de&niujące metropolię: kwestie nieprzewidywalnych spotkań oraz organizacji oporu (w formie miejskich rebelii zwanych żakeriami). Gdy ująć te cechy wspólnie, przekonują Hardt i Negri, należy zgodzić się z tezą, że metropolia jest miejscem, w którym wielość znajduje swój dom

Learning Mixtures of Gaussians in High Dimensions

Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory. Given samples coming from a random one out of k Gaussian distributions in Rn, the learning problem asks to estimate the means and the covariance matrices of these Gaussians. This learning problem arises in many areas ranging from the natural sciences to the social sciences, and has also found many machine learning applications. Unfortunately, learning mixture of Gaussians is an information theoretically hard problem: in order to learn the parameters up to a reasonable accuracy, the number of samples required is exponential in the number of Gaussian components in the worst case. In this work, we show that provided we are in high enough dimensions, the class of Gaussian mixtures is learnable in its most general form under a smoothed analysis framework, where the parameters are randomly perturbed from an adversarial starting point. In particular, given samples from a mixture of Gaussians with randomly perturbed parameters, when n > {\Omega}(k^2), we give an algorithm that learns the parameters with polynomial running time and using polynomial number of samples. The central algorithmic ideas consist of new ways to decompose the moment tensor of the Gaussian mixture by exploiting its structural properties. The symmetries of this tensor are derived from the combinatorial structure of higher order moments of Gaussian distributions (sometimes referred to as Isserlis' theorem or Wick's theorem). We also develop new tools for bounding smallest singular values of structured random matrices, which could be useful in other smoothed analysis settings

Watching nightlife: affective labor, social media and surveillance

This article examines the affective labor of nightlife photographers within the surveillance economy of social media. I examine nightlife photographers as “below the line” cultural laborers who employ their identities and communicative capacities to create and circulate images of nightlife online. These images stimulate interaction that can be watched, tracked, and responded to by the databases of social media. The study draws on interviews with nightlife photographers to examine how they account for the creative and promotional aspects of their labor. I argue that the analytical capacities of social media databases, and the modes of promotion they facilitate, depend in the first instance on the affective labor of cultural intermediaries like nightlife photographers

Becoming-Bertha: virtual difference and repetition in postcolonial 'writing back', a Deleuzian reading of Jean Rhys’s Wide Sargasso Sea

Critical responses to Wide Sargasso Sea have seized upon Rhys’s novel as an exemplary model of writing back. Looking beyond the actual repetitions which recall Brontë’s text, I explore Rhys’s novel as an expression of virtual difference and becomings that exemplify Deleuze’s three syntheses of time. Elaborating the processes of becoming that Deleuze’s third synthesis depicts, Antoinette’s fate emerges not as a violence against an original identity. Rather, what the reader witnesses is a series of becomings or masks, some of which are validated, some of which are not, and it is in the rejection of certain masks, forcing Antoinette to become-Bertha, that the greatest violence lies

The effective shear and dilatational viscosity of a particle-laden interface in the dilute limit

The effective dilatational and shear viscosities of a particle-laden fluid interface are computed in the dilute limit under the assumption of an asymptotically vanishing viscosity ratio between both fluids. Spherical particles with a given contact angle of the fluid interface at the particle surface are considered. A planar fluid interface and a small Reynolds number are assumed. The theoretical analysis is based on a domain perturbation expansion in the deviation of the contact angle from $90^\circ$ up to the second order. The resulting effective dilatational viscosity shows a stronger dependence on the contact angle than the effective shear viscosity, and its magnitude is larger for all contact angles. As an application of the theory, the stability of a liquid cylinder decorated with particles is considered. The limits of validity of the theory and possible applications in terms of numerical simulations of particle-laden interfaces are discussed.Comment: 28 pages, 4 figure

POTs: Protective Optimization Technologies

Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. Not surprisingly, these decisions limit fairness frameworks' ability to capture a variety of harms caused by systems. We characterize fairness limitations using concepts from requirements engineering and from social sciences. We show that the focus on algorithms' inputs and outputs misses harms that arise from systems interacting with the world; that the focus on bias and discrimination omits broader harms on populations and their environments; and that relying on service providers excludes scenarios where they are not cooperative or intentionally adversarial. We propose Protective Optimization Technologies (POTs). POTs provide means for affected parties to address the negative impacts of systems in the environment, expanding avenues for political contestation. POTs intervene from outside the system, do not require service providers to cooperate, and can serve to correct, shift, or expose harms that systems impose on populations and their environments. We illustrate the potential and limitations of POTs in two case studies: countering road congestion caused by traffic-beating applications, and recalibrating credit scoring for loan applicants.Comment: Appears in Conference on Fairness, Accountability, and Transparency (FAT* 2020). Bogdan Kulynych and Rebekah Overdorf contributed equally to this work. Version v1/v2 by Seda G\"urses, Rebekah Overdorf, and Ero Balsa was presented at HotPETS 2018 and at PiMLAI 201