Explore the vast range of titles available.

The mid-seventeenth century English social movement known as the Levellers was perhaps the first liberal-democratic social movement. Among their communicative strategies, to garner supporters while challenging the authorities, humor figured prominently. In this article, the nature of this levelling laughter is highlighted and juxtaposed against Puritan injunctions to mourning and objections against humor. Regarding the latter, four such objections are distinguished and elucidated: “damnable heresies”, “strange opinions”, “fearful divisions”, and “loosenesse of life and manners”. Finally, it is suggested that the Puritan repudiation of the Levellers highlights the need for social movements of democratic dissent against various aspects of the given status quo to use incongruous and relief humor to prompt reflection without relying too heavily on boorishly flouting social prohibitions for the sake of the pleasures of superiority and release. It also suggests that humor will do better in a culture already tolerant of pluralism, comfortable with a measure of non-literal ambiguity, and committed to democratic deliberation.

THE AUTHOR OF THIS ARTICLE ATTEMPTS TO DRAW OUT, THROUGH OFTEN IMPLICIT VIEWS MACHIAVELLI HELD REGARDING MAN AND THE NATURE OF HUMAN ACTION BY SITUATING THEM WITHIN THE PREVAILING COSMOLOGY OF HIS DAY. SPECIAL ATTENTION IS PAYED TO MACHIAVELLI'S TREATMENT OF THE PIVOTAL STRUGGLE BETWEEN FORTUNA AND VITRU. THE ARTICLE EXPLORES THESE TERMS IN MACHIAVELLI'S WORKS AND PLACES THEM WITHIN THE CONTEXT OF THE COSMOLOGY OF HIS DAY.

The purpose of this dissertation is to erect a theoretical framework for liberal individualism upon the foundational notions of 'self-ownership' and 'friendship'. It proceeds by attempting sympathetically to explore the meaning of these notions in the thought of three specific thinkers--Etienne de La Boetie (1530-53), Richard Overton (fl. 1640-60), and Max Stirner (1806-56)--in their respective contexts--mid-sixteenth century France, mid-seventeenth century England, and mid-nineteenth century Prussia. It is argued that self-ownership and friendship were used consistently in reflection upon a variety of emerging, epochally modern, socio-political phenomena: including the Renaissance, urbanization, the discovery of the New World, the rise of the absolutist state, the advent of printing, the growth of popular literacy, the Reformation, the English Revolution, the establishment of democratic institutions, the growth of the market economy, the French Revolution, the industrial revolution, the 'murder of God', and the birth of ideology. It is further contended that these notions were deployed in resistance to specific contemporary arguments. Hence, the arguments of La Boetie, Overton, and Stirner are contrasted against those of Niccolo Machiavelli, Thomas Edwards, and Karl Marx (and Friedrich Engels), respectively. La Boetie, Overton and Stirner used 'self-ownership' to describe and defend the ethically inviolable reality of the autonomous individual person, and 'friendship' to name the ethically appropriate dynamics of individualist human relations. The conclusion culls from the separate studies of La Boetie, Overton, and Stirner, a consistent set of substantive qualities for the notions of 'self-ownership' and 'friendship'.

In this article we present new results on neural networks with linear threshold activation functions x ↦ 1 { x > 0 } . We precisely characterize the class of functions that are representable by such neural networks and show that 2 hidden layers are necessary and sufficient to represent any function representable in the class. This is a surprising result in the light of recent exact representability investigations for neural networks using other popular activation functions like rectified linear units (ReLU). We also give upper and lower bounds on the sizes of the neural networks required to represent any function in the class. Finally, we design an algorithm to solve the empirical risk minimization (ERM) problem to global optimality for these neural networks with a fixed architecture. The algorithm’s running time is polynomial in the size of the data sample, if the input dimension and the size of the network architecture are considered fixed constants. The algorithm is unique in the sense that it works for any architecture with any number of layers, whereas previous polynomial time globally optimal algorithms work only for restricted classes of architectures. Using these insights, we propose a new class of neural networks that we call shortcut linear threshold neural networks . To the best of our knowledge, this way of designing neural networks has not been explored before in the literature. We show that these neural networks have several desirable theoretical properties.