São Paulo, centro e periferia: a retórica ambiental e os limites da política urbana

The text, referring to the Metropolitan Region of São Paulo, puts a focus on the urban environmental issue, in whish the environment consists not only of natural processes and dynamics, but also includes the relations between them and the social processes and dynamics. Two extreme situations highlight the matter: i) the outlying settlements on the fringes along the water supply areas and environmentally sensitive areas; ii) the decrease of population at the central and consolidated areas, which have a potential to increase density. From this point, the urban projects made for the central area of São Paulo, the core of the metropolitan area are discussed. It turns evident then that the inclusion of the environmental dimension in the urban issue, in a sense that is not just rhetoric, brings to light the intrinsic limitations of Urban Policies.O texto, referenciando-se na Região Metropolitana de São Paulo, coloca um foco na questão ambiental urbana, onde o ambiente não consiste apenas em dinâmicas e processos naturais, mas inclui as relações entre estes e as dinâmicas e os processos sociais. Duas situações extremas expressam a questão: os assentamentos precários nas franjas periféricas junto aos mananciais e em áreas ambientalmente sensíveis e áreas centrais, consolidadas, que perdem população, mas têm potencial de adensamento. A partir desse ponto, são discutidos os projetos urbanos formulados para a área central do município de São Paulo, núcleo da Região Metropolitana. Evidencia-se, então, que a inserção da dimensão ambiental na questão urbana, de modo que não seja apenas retórica, traz à luz as próprias limitações das políticas urbanas

Neural networks trained with SGD learn distributions of increasing complexity

The ability of deep neural networks to generalise well even when they interpolate their training data has been explained using various "simplicity biases". These theories postulate that neural networks avoid overfitting by first learning simple functions, say a linear classifier, before learning more complex, non-linear functions. Meanwhile, data structure is also recognised as a key ingredient for good generalisation, yet its role in simplicity biases is not yet understood. Here, we show that neural networks trained using stochastic gradient descent initially classify their inputs using lower-order input statistics, like mean and covariance, and exploit higher-order statistics only later during training. We first demonstrate this distributional simplicity bias (DSB) in a solvable model of a neural network trained on synthetic data. We empirically demonstrate DSB in a range of deep convolutional networks and visual transformers trained on CIFAR10, and show that it even holds in networks pre-trained on ImageNet. We discuss the relation of DSB to other simplicity biases and consider its implications for the principle of Gaussian universality in learning.Comment: Source code available at https://github.com/sgoldt/dist_inc_com

Bootstrapping traceless symmetric O(N)O(N) scalars

We use numerical bootstrap techniques to study correlation functions of a traceless symmetric tensors of $O(N)$ with two indexes $t_{ij}$. We obtain upper bounds on operator dimensions for all the relevant representations and several values of $N$. We discover several families of kinks, which do not correspond to any known model and we discuss possible candidates. We then specialize to the case $N=4$, which has been conjectured to describe a phase transition in the antiferromagnetic real projective model $ARP^{3}$. Lattice simulations provide strong evidence for the existence of a second order phase transition, while an effective field theory approach does not predict any fixed point. We identify a set of assumptions that constrain operator dimensions to a closed region overlapping with the lattice prediction. The region is still present after pushing the numerics in the single correlator case or when considering a mixed system involving $t$ and the lowest dimension scalar singlet.Comment: 47 pages, 27 figure

Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime

Deep neural networks can achieve remarkable generalization performances while interpolating the training data perfectly. Rather than the U-curve emblematic of the bias-variance trade-off, their test error often follows a "double descent" - a mark of the beneficial role of overparametrization. In this work, we develop a quantitative theory for this phenomenon in the so-called lazy learning regime of neural networks, by considering the problem of learning a high-dimensional function with random features regression. We obtain a precise asymptotic expression for the bias-variance decomposition of the test error, and show that the bias displays a phase transition at the interpolation threshold, beyond which it remains constant. We disentangle the variances stemming from the sampling of the dataset, from the additive noise corrupting the labels, and from the initialization of the weights. Following up on Geiger et al. 2019, we first show that the latter two contributions are the crux of the double descent: they lead to the overfitting peak at the interpolation threshold and to the decay of the test error upon overparametrization. We then quantify how they are suppressed by ensemble averaging the outputs of K independently initialized estimators. When K is sent to infinity, the test error remains constant beyond the interpolation threshold. We further compare the effects of overparametrizing, ensembling and regularizing. Finally, we present numerical experiments on classic deep learning setups to show that our results hold qualitatively in realistic lazy learning scenarios.Comment: 29 pages, 12 figure

GLOBALIZAÇÃO, INFORMALIDADE E REGULAÇÃO EM CIDADES LATINO-AMERICANAS

O texto procura identificar e comparar, em algumas metrópoles latino-americanas, a manifestação de processos recentes e simultâneos: término de regimes militares autoritários, democratização e descentralização político-administrativa, reestruturação econômica e desregulamentação, ao lado deuma histórica e crescente irregularidade, informalidade e precarização do espaço urbano. Observam-se, nos diferentes países, similitudes dos impactos e das reações a esses processos, particularmente na esfera local – municipal e metropolitana. A partir desse quadro, reflete sobre identidades e sobre os padrões urbano-ambientais presentes nessas cidades

Hybrid contractual landscapes of governance: Generation of fragmented regimes of public accountability through urban regeneration

In this article we explore the idea of public accountability in the contemporary entrepreneurial governance of cities, which are influenced by market dependency and private sector involvement. We specifically focus on the fragmentation of public accountability through hybrid contractual landscapes of governance, in which the public and private sector actors interactively produce a diversity of instruments to ensure performance in service. This is in sharp contrast to the traditional vague norms and values appealed to by urban planning institutions, to safeguard the public interest. We argue that within these complex contractual governance environments public accountability is produced by public and private sector actors, through highly diverse sets of contractual relations and diverse control instruments that define responsibilities of diverse actors who are involved in a project within a market-dependent planning and policy making environment, which contains context-specific characteristics set by the specific rules of public-private collaboration. These complexities mean public accountability has become fragmented and largely reduced to performance control. Moreover, our understanding of contractual urban governance remains vague and unclear due to very limited empirical studies focusing on the actual technologies of contractual urban development. By deciphering the complex hybrid landscapes of contractual governance, with comparative empirical evidence from The Netherlands, UK and Brazil, we demonstrate how public accountability is assuming a more ‘contractual’ and unpredictable meaning in policy and plan implementation process