Messy Subjectivities: The Popular, Affective and Technical Consistencies of Early Nineteenth-Century Staffordshire Ware

This article investigates how Staffordshire figurines and dinnerware, which were popular in early nineteenth-century England and its colonies, were complicit in forging emergent social, aesthetic and subjective consciousness. Staffordshire ware was influenced by diverse technical, economic and aesthetic factors, including the circulation of print media, private property, colonialism and Romanticism. At the same time, the wares both engendered Romantic versions of subjectivity that amplified the importance of the private individual, while generating emergent sites of contestation that exceeded them. The collection of Staffordshire figurines and dinnerware was a media and technical consistency where owners could inhabit the tensions of at times conflicting social, material, affective and performative affinities or antagonisms. This discussion draws from Walter Benjamin to develop a way of thinking with how the material and media specificity of Staffordshire ware could have co-composed heterogeneous knowledges, practices and subjectivities that undermined the Romantic individual. By examining the multifaceted qualities, affects and contexts of Staffordshire ware in detail I aim to develop new terms and practices with which to activate emergent versions of historicity, corporeality and world-making

Performing Surfaces: Designing Research-creation for Agentive Embodiment

In this discussion I first outline material intersections of bodies and everyday objects that script habitual choreographies as affective extensions of the body. I then consider the dynamism of the shifts between habitual consciousness and non-consciousness as a site of agentive, embodied transformation within contexts of movement, multiples and sensory experience. I engage willfulness, stickiness, clumsiness and apparatuses of sensory notation as potential techniques for transforming consciousness and non-consciousness in habitual action. Finally, I invite readers to recreate the sensory dimensions of their own habitual experience by experimenting with some 'do it yourself ' exercises

(Re)Calibration, Standard-Setting and the Shaping of Investment Law and Arbitration

Calibrating or (re)calibrating investment law and arbitration—depending on whether the exercise takes place for the first or a subsequent time—is different from rebalancing investment law and arbitration. A balancing exercise denotes a situation in which different elements are equal or in the correct proportions to maintain a sort of equilibrium. This Essay argues that investment law and arbitration are not necessarily about creating a situation in which all “elements” are in balance and that (re)calibrating is an interesting starting point for a discussion about the contemporary regime of investment law and arbitration, and especially to explore, understand, or visualize the current and future developments in the field. The central questions in this Essay include determining what the standards are and who sets them. This Essay examines (re)calibration and looks at the process from the vantage point of the standards which are used for such (re)calibration and evaluates how the standards have evolved substantially over the years and how new treaties—in an exercise of (re)calibration—are in fact following or adapting to these new standards

Host States\u27 Due Diligence Obligations in International Investment Law

Due diligence is present in a variety of aspects of the protection of foreign investors in international investment law and plays an important role in several aspects of the protection of foreign investors. In particular, certain standards of investment protection, notably full protection and security ( FPS ) include an obligation for the State to act with due diligence. This articles seeks to establish an explanatory framework for past and future decisions of arbitral tribunals which have applied or will be confronted to applications of the due diligence standard in international investment law, by providing a typology of the different possible applications of the standard in relation to the obligations of the host State. It addresses the role of due diligence in the law governing State responsibility, and the application of due diligence in the customary norms relating to the protection of aliens. Based on these two sections, it next discusses the principle in contemporary investment law, focusing on the application of due diligence in the FPS, the international minimum standard ( IMS ) and the fair and equitable treatment ( FET ) standards of treatment. It then addresses the question of whether applying due diligence allows for the possibility of taking into account the relative capacities of host States, and the consequences the application of the due diligence standard has on the compensation for damages

Dynamic Steerable Blocks in Deep Residual Networks

Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account. We investigate the generalized notion of frames designed with image properties in mind, as alternatives to this parametrization. We show that frame-based ResNets and Densenets can improve performance on Cifar-10+ consistently, while having additional pleasant properties like steerability. By exploiting these transformation properties explicitly, we arrive at dynamic steerable blocks. They are an extension of residual blocks, that are able to seamlessly transform filters under pre-defined transformations, conditioned on the input at training and inference time. Dynamic steerable blocks learn the degree of invariance from data and locally adapt filters, allowing them to apply a different geometrical variant of the same filter to each location of the feature map. When evaluated on the Berkeley Segmentation contour detection dataset, our approach outperforms all competing approaches that do not utilize pre-training. Our results highlight the benefits of image-based regularization to deep networks

Towards End-to-End Lane Detection: an Instance Segmentation Approach

Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed "bird's-eye view" transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results