‘Just be confident girls!’: Confidence Chic as Neoliberal Governmentality

In our injurious patriarchal cultures, unconfidence is almost inescapable when inhabiting womanhood. However, recently the promotion of self-confidence has surfaced as the site for expanded, heightened and more insidious modes of regulation, often spearheaded by those very institutions invested in women’s insecurities. This notably includes consumer women’s magazines. Contemporary publications are marked by an intensified preoccupation with taking readers ‘from crisis to confidence’, offering even dedicated sections (e.g. ‘confidence revolution’ and ‘Bye-bye body hang-ups’ in Cosmopolitan UK) and issues—see, for example, Elle UK’s January 2015 ‘Confidence Issue: A Smart Woman’s Guide to Self-Belief’. Clearly, this sector is a fundamental player in the confidence movement-market, bringing together a range of interested parties, not least ‘love your body’ (LYB) advertisers like Dove (see Gill and Elias 2014), and enjoying an extensive audience reach, both in terms of numbers and geography—a reach increased to unprecedented degrees by online versions

Large deviation principles for the Ewens-Pitman sampling model

Let $M_{l,n}$ be the number of blocks with frequency $l$ in the exchangeable random partition induced by a sample of size $n$ from the Ewens-Pitman sampling model. We show that, as $n$ tends to infinity, $n^{-1}M_{l,n}$ satisfies a large deviation principle and we characterize the corresponding rate function. A conditional counterpart of this large deviation principle is also presented. Specifically, given an initial sample of size $n$ from the Ewens-Pitman sampling model, we consider an additional sample of size $m$. For any fixed $n$ and as $m$ tends to infinity, we establish a large deviation principle for the conditional number of blocks with frequency $l$ in the enlarged sample, given the initial sample. Interestingly, the conditional and unconditional large deviation principles coincide, namely there is no long lasting impact of the given initial sample. Potential applications of our results are discussed in the context of Bayesian nonparametric inference for discovery probabilities.Comment: 30 pages, 2 figure

Mediating intimacy online: authenticity, magazines and chasing the clicks

This paper offers a production-based study of online consumer magazines for – and largely by – millennial women, with a particular focus on sex and relationship content. Adopting a feminist discourse analytic approach and a solidary-critical position, I examine 62 interviews conducted with producers, mainly writers and editors, from 12 publications based in the UK and Spain. The analysis maps how notions of intimacy penetrate different dimensions of the magazine, along with networks of influence for the development of content about sex and relationships, marked by a perceived shift from ‘experts’ to ‘real life’. The ways in which producers describe the particularities of woman’s magazine online journalism and dis/articulate a range of critiques are also explored. The paper highlights the increasing importance of ideas about authenticity for these media, making connections to online cultures, a reinvigorated interest in feminism, and contemporary branding strategies. Ultimately, I argue that journalists at women’s magazines simultaneously (re)produce, suffer and contest sexist media, deserving further feminist scholarly attention, and our solidarity as well as critique

Postfeminist sexpertise on the “porn and men issue”: A transnational perspective

Focusing on women’s online magazines produced between 2012 and 2014 in the UK and in Spain, this chapter examines peer responses to women feeling distressed about their male partners’ consumption of pornographies, in addition to editorial content around the subject. Moving beyond ‘for and against’ positions, and driven by a social justice agenda, the chapter utilises this commentary about hetero-male- oriented pornographies as a point of analytical entry into the kinds of gendered and sexual pleasures, bodies, subjectivities and intimate relational possibilities contemporary (new) media and public sex and relationship advice bring into being and render (un)intelligible. In doing so, it seeks to contribute to feminist interrogations of the politics of mediated intimacy and pornification under neoliberalism and postfeminism, incorporating a much-needed transnational perspective

Consolidated bioprocessing of starchy substrates into ethanol by industrial Saccharomyces cerevisiae strains secreting fungal amylases

The development of a yeast strain that converts raw starch to ethanol in one step (called Consolidated Bioprocessing, CBP) could significantly reduce the commercial costs of starch-based bioethanol. An efficient amylolytic Saccharomyces cerevisiae strain suitable for industrial bioethanol production was developed in this study. Codon-optimized variants of the Thermomyces lanuginosus glucoamylase (TLG1) and Saccharomycopsis fibuligera -amylase (SFA1) genes were -integrated into two S. cerevisiae yeast with promising industrial traits, i.e., strains M2n and MEL2. The recombinant M2n[TLG1-SFA1] and MEL2[TLG1-SFA1] yeast displayed high enzyme activities on soluble and raw starch (up to 8118 and 4461nkat/g dry cell weight, respectively) and produced about 64g/L ethanol from 200g/L raw corn starch in a bioreactor, corresponding to 55% of the theoretical maximum ethanol yield (g of ethanol/g of available glucose equivalent). Their starch-to-ethanol conversion efficiencies were even higher on natural sorghum and triticale substrates (62 and 73% of the theoretical yield, respectively). This is the first report of direct ethanol production from natural starchy substrates (without any pre-treatment or commercial enzyme addition) using industrial yeast strains co-secreting both a glucoamylase and -amylase

Negative index of refraction, perfect lenses and transformation optics -- some words of caution

In this paper we show that a negative index of refraction is not a direct implication of transformation optics with orientation-reversing diffeomorphisms. Rather a negative index appears due to a specific choice of sign freedom. Furthermore, we point out that the transformation designed lens, which relies on the concept of spacetime folding, does not amplify evanescent modes, in contrast to the Pendry-Veselago lens. Instead, evanescent modes at the image point are produced by a duplicated source and thus no imaging of the near field (perfect lensing) takes place.Comment: 13 pages, 3 figures, LaTe

A Berry-Esseen theorem for Pitman's α\alpha-diversity

This paper is concerned with the study of the random variable $K_n$ denoting the number of distinct elements in a random sample $(X_1, \dots, X_n)$ of exchangeable random variables driven by the two parameter Poisson-Dirichlet distribution, $PD(\alpha,\theta)$. For $\alpha\in(0,1)$, Theorem 3.8 in \cite{Pit(06)} shows that $\frac{K_n}{n^{\alpha}}\stackrel{\text{a.s.}}{\longrightarrow} S_{\alpha,\theta}$ as $n\rightarrow+\infty$. Here, $S_{\alpha,\theta}$ is a random variable distributed according to the so-called scaled Mittag-Leffler distribution. Our main result states that \sup_{x \geq 0} \Big| \ppsf\Big[\frac{K_n}{n^{\alpha}} \leq x \Big] - \ppsf[S_{\alpha,\theta} \leq x] \Big| \leq \frac{C(\alpha, \theta)}{n^{\alpha}} holds with an explicit constant $C(\alpha, \theta)$. The key ingredients of the proof are a novel probabilistic representation of $K_n$ as compound distribution and new, refined versions of certain quantitative bounds for the Poisson approximation and the compound Poisson distribution

Emergence of Object Segmentation in Perturbed Generative Models

We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments can be perturbed locally relative to a given background without affecting the realism of a scene. Our approach is to first train a generative model of a layered scene. The layered representation consists of a background image, a foreground image and the mask of the foreground. A composite image is then obtained by overlaying the masked foreground image onto the background. The generative model is trained in an adversarial fashion against a discriminator, which forces the generative model to produce realistic composite images. To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background. Because the generator is unaware of the shift before computing its output, it must produce layered representations that are realistic for any such random perturbation. Finally, we learn to segment an image by defining an autoencoder consisting of an encoder, which we train, and the pre-trained generator as the decoder, which we freeze. The encoder maps an image to a feature vector, which is fed as input to the generator to give a composite image matching the original input image. Because the generator outputs an explicit layered representation of the scene, the encoder learns to detect and segment objects. We demonstrate this framework on real images of several object categories.Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Spotlight presentatio