The authors of Linguistic Bodies appeal to shared know-how to explain the social and participatory interactions upon which linguistic skills and agency rest. However, some issues lurk around the notion of shared know-how and require attention and clarification. In particular, one issue concerns the agent behind the shared know-how, a second one concerns whether shared know-how can be reducible to individual know-how or not. In this paper, I sustain that there is no single answer to the first issue; depending on the case, shared know-how can belong to the participants of a social activity or to the system the participants bring forth together. In relation to the second issue, I sustain, following the authors, a non-reductive account of shared know-how. I also suggest that responsiveness to others, which is a fundamental element of shared know-how, can be extended by perceptual learning.

We propose to smooth the entire objective function, rather than only the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur-Kiefer representation than the standard estimator, but it is also asymptotically differentiable. We exploit the latter to propose a quantile density estimator that does not suffer from the curse of dimensionality. This means estimating the conditional density function without worrying about the dimension of the covariate vector. It also allows for two-stage efficient quantile regression estimation. Our asymptotic theory holds uniformly with respect to the bandwidth and quantile level. Finally, we propose a rule of thumb for choosing the smoothing bandwidth that should approximate well the optimal bandwidth. Simulations confirm that our smoothed quantile regression estimator indeed performs very well in finite samples.

This chapter argues that the Ethics includes versions of the views about sensation and its role in the production of knowledge that are present in the TIE and the KV. ‘Idea of an affection’ replaces the earlier terms for sensation. A sensation is a modification of the mind closely associated with a modification of body and explained in terms of the mind-body relation. In the KV, Spinoza continues to treat sensation as the immediate perception of corporeal modification and continues to take the inference from sensation to the union of mind and body as a valid one. The KV is the earliest source of Spinoza's view that the idea-object model accounts for the mind-body relation. The only occurrence of sensatio in the CM might seem to tell against an account of sensation as something different from imagination. Accounts of reason and imagination in the Ethics considerably advance Spinoza's theory of knowledge.

Neste artigo, mapeio o terreno da discussão em torno das teorias da conspiração, destacando o problema de como defini-las, os fatores que levam à crença nas teorias da conspiração, os seus potenciais prejuízos e como devemos reagir a elas. Defendo que devemos avaliar as consequências da crença em uma teoria da conspiração para determinar se ela deve ser levada a serio ou não. Em bloco, as teorias da conspiração ameaçam a capacidade coletiva de produção de conhecimento e devemos nos preocupar com a sua difusão.

Clustering methods are valuable tools for the identifcation of patterns in high dimensional data with applications in many scientifc felds. However, quantifying uncertainty in clustering is a challenging problem, particularly when dealing with High Dimension Low Sample Size (HDLSS) data. We develop a U-statistics based clustering approach that assesses statistical signifcance in clustering and is specifcally tailored to HDLSS scenarios. These non-parametric methods rely on very few assumptions about the data, and thus can be applied to a wide range of dataset for which the Euclidean distance captures relevant features. Our main result is the development of a hierarchical signifcance clustering method. In order to do so, we first introduce an extension of a relevant U-statistic and develop its asymptotic theory. Additionally, as a preliminary step, we propose a binary non-nested signifcance clustering method and show its optimality in terms of expected values. Our approach is tested through multiple simulations and found to have more statistical power than competing alternatives in all scenarios considered. They are further showcased in three applications ranging from genetics to image recognition problems.