'Nothing good ever came out of Japan': Race-mixing and the limits of diaspora

This paper argues that postcolonial notions of diaspora are premised on immigrant subjectivities and standpoints which do not fully apprehend the mixed-race / bi-racial experience and the local effect of cultural hybridity in Western settings. The paper was prompted by a recent conversation with Dee, the daughter of a Japanese warbride. As a child Dee recalled being told by her friend's mother that 'nothing good ever came out of Japan'. The significance of constant interpolations into 'Asianness' by statements such as these; by the 'where do you come from?' question and by more blatant discriminations are inadequately addressed by traditional and postcolonial notions of diaspora. 'Roots' and 'routes' imagery feature prominently in discussions of diaspora and hybridity which aim to decolonise culture and identity in deconstructive moves that highlight their flexible, multiple, contractedness. While it has been argued that even these conceptualisations are problematic because they privilege orders of explanation, theory and standpoint that are forced back into line with traditional notions of discrete 'races', cultures, ethnicities and identities, cultural studies and postcolonial theorists do not appear to find this contradiction overly troubling. Lodged in bodies that do not easily conflate to neat either/or cultures, politics and genetics, race-mixing also defies and yet return us to culture and biology. However, I argue that their refractions though the same tired old orders of racial, ethnic, cultural and national differentiation prevent us from disregarding the discursive effects of racism and racialisation.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Noise in detection of qubit states using a quantum point contact

We present a model for detection of the states of a coupled quantum dots (qubit) by a quantum point contact. Most proposals for measurements of states of quantum systems are idealized. However in a real laboratory the measurements cannot be perfect due to practical devices and circuits. The models using ideal devices are not sufficient for describing the detection information of the states of the quantum systems. Our model therefore includes the extension to a non-ideal measurement device case using an equivalent circuit. We derive a quantum trajectory that describes the stochastic evolution of the state of the system of the qubit and the measuring device. We calculate the noise power spectrum of tunnelling events in an ideal and a non-ideal quantum point contact measurement respectively. We found that, for the strong coupling case it is difficult to obtain information of the quantum processes in the qubit by measurements using a non-ideal quantum point contact. The noise spectra can also be used to estimate the limits of applicability of the ideal model.