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Daily life frequently erupted like a slap: Elena Ferrante’s Neapolitan Novels as narrative intertexts of 1970s Italian feminist praxis

ABSTRACT This article historicises Elena Ferrante’s Neapolitan Series novels in terms of 1970s feminism. The novels provide a phenomenological account of the development of the autonomist Italian left in the 1970s, but unlike most histories of this process, they centre the gendered contradictions within that political milieu. This narrative arc demands to be contextualised with the ideas of Italian feminists Carla Lonzi, Mariarosa dalla Costa, and the major Anglophone thinkers of Marxist Feminism. The Neapolitan Series investigates the feminist potential of relationships among women, even women who have no political consciousness in the common sense of the term, a potential that is illuminated by contrast with the oppressive effect of the most ardent and sophisticated male Marxists. What is crucial however, and this is at the heart of this article, is the fact that Ferrante stages this skewering of male proletarian rebels amid the finely wrought depictions of the capitalist immiseration of working-class women’s lives and the inanity of bourgeois feminism. In short, this essay uses Ferrante (and her sensational popularity) as a way of thinking about the current revival of interest in 1970s feminism as an expression of the desire to reconcile the central tenets of Radical and Marxist Feminisms.

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Feminist Criticisms and Reinterpretations of Hegel

In 1970, the Italian feminist Carla Lonzi published her now-classic polemic urging women to “spit on Hegel”. Disregarding her advice, many subsequent feminist theorists and philosophers have engaged substantially with Hegel's thought, and a wide variety of feminist readings of Hegel have sprung up. The aim of this paper is to provide an overview of these different feminist criticisms and interpretations of Hegel. In introducing these various interpretations, I will show how they reflect a range of divergent feminist approaches to the history of philosophy as a whole. My aim is not only to describe but also to evaluate these approaches, with respect to their capacity to generate insightful and productive readings of Hegel's philosophy. I shall argue that what I will call the “essentialist” feminist approach to Hegel is the most fruitful, doing most to illuminate the contours of his thought and to open up new and creative ways of reading his works.To anticipate, in surveying the various feminist interpretations of Hegel, I will classify them as reflecting four different types of feminist approach to the history of philosophy. The first, “extensionist” approach draws upon the history of philosophy for conceptual resources to understand and explain women's social situation. The second approach is more critical, tracing the pervasiveness of “masculinist” assumptions and biases in the history of philosophy. To call views “masculinist” is to say that they uphold systematic and hierarchical contrasts between masculinity and femininity, contrasts which need not be explicit but may be sustained through contrasts between other ostensibly neutral concepts which actually have tacit gender connotations. This critical approach generates an overwhelmingly negative picture of the philosophical tradition. The third, “essentialist” approach complicates this picture, recovering and highlighting the strands within historical texts which revalorise concepts or items that are given feminine connotations. These often overlooked strands oppose the dominant masculinist tendencies in texts by assigning equal importance and value to “symbolically feminine” concepts. However, proponents of the fourth, “deconstructive” approach object that essentialist readings of philosophical texts accept and reinforce patterns of gender symbolism which feminists ought to challenge. Deconstructive feminists seek to expose and exacerbate the instability within these patterns of gender symbolism by tracing how philosophical texts continuously undermine the gender contrasts present within them.

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ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA

Despite recent advances in autonomous driving systems, accidents such as the fatal Uber crash in 2018 show these systems are still susceptible to edge cases. Such systems must be thoroughly tested and validated before being deployed in the real world to avoid such events. Testing in open-world scenarios can be difficult, time-consuming, and expensive. These challenges can be addressed by using driving simulators such as CARLA instead. A key part of such tests is adversarial testing, in which the goal is to find scenarios that lead to failures of the given system. While several independent efforts in testing have been made, a well-established testing framework that enables adversarial testing has yet to be made available for CARLA. We therefore propose ANTI-CARLA, an automated testing framework in CARLA for simulating adversarial weather conditions (e.g., heavy rain) and sensor faults (e.g., camera occlusion) that fail the system. The operating conditions in which a given system should be tested are specified in a scenario description language. The framework offers an efficient search mechanism that searches for adversarial operating conditions that will fail the tested system. In this way, ANTI-CARLA extends the CARLA simulator with the capability of performing adversarial testing on any given driving pipeline. We use ANTI-CARLA to test the driving pipeline trained with Learning By Cheating (LBC) approach. The simulation results demonstrate that ANTI-CARLA can effectively and automatically find a range of failure cases despite LBC reaching an accuracy of 100% in the CARLA benchmark.

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ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA

Despite recent advances in autonomous driving systems, accidents such as the fatal Uber crash in 2018 show these systems are still susceptible to edge cases. Such systems must be thoroughly tested and validated before being deployed in the real world to avoid such events. Testing in open-world scenarios can be difficult, time-consuming, and expensive. These challenges can be addressed by using driving simulators such as CARLA instead. A key part of such tests is adversarial testing, in which the goal is to find scenarios that lead to failures of the given system. While several independent efforts in testing have been made, a well-established testing framework that enables adversarial testing has yet to be made available for CARLA. We therefore propose ANTI-CARLA, an automated testing framework in CARLA for simulating adversarial weather conditions (e.g., heavy rain) and sensor faults (e.g., camera occlusion) that fail the system. The operating conditions in which a given system should be tested are specified in a scenario description language. The framework offers an efficient search mechanism that searches for adversarial operating conditions that will fail the tested system. In this way, ANTI-CARLA extends the CARLA simulator with the capability of performing adversarial testing on any given driving pipeline. We use ANTI-CARLA to test the driving pipeline trained with Learning By Cheating (LBC) approach. The simulation results demonstrate that ANTI-CARLA can effectively and automatically find a range of failure cases despite LBC reaching an accuracy of 100% in the CARLA benchmark.

Open Access
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