A latent feature analysis of the neural representation of conceptual knowledge

Bayesian probabilistic analysis offers a new approach to characterize semantic representations by inferring the most likely feature structure directly from the patterns of brain activity. In this study, infinite latent feature models [1] are used to recover the semantic features that give rise to the brain activation vectors when people think about properties associated with 60 concrete concepts. The semantic features recovered by ILFM are consistent with the human ratings of the shelter, manipulation, and eating factors that were recovered by a previous factor analysis. Furthermore, different areas of the brain encode different perceptual and conceptual features. This neurally-inspired semantic representation is consistent with some existing conjectures regarding the role of different brain areas in processing different semantic and perceptual properties. © 2012 Springer-Verlag.

Queer Spaces, Sexual Violence and the Desire for Safety

Queer politics and spaces have historically been associated with ideals of sexual liberation. They are conceptualised as spaces where sex, and its intersections with intimacy, friendship and love can be explored outside of normative frameworks which value monogamous reproductive heterosexuality at the expense of other non-normative sexual expressions. In recent years, however, autonomous queer spaces such as the global Queeruption gatherings and other queer community spaces in Australia have become increasingly concerned with the presence and danger of sexual violence in queer communities. Almost without exception, this danger has been responded to through the creation of ‘safe(r) spaces’ policies, generally consisting of a set of guidelines and proscribed behaviours which individuals must agree to in order to participate in or attend the event or space. The guidelines themselves tend to privilege of sexual politics of affirmative verbal consent, insisting that such consent should be sought prior to any physical or sexual contact, inferring that a failure to do so is ethically unacceptable within. This chapter reflects on the attempts to construct queer communities as ‘safer spaces,’ arguing that the concepts of consent and safety are inadequate to develop a queer response to sexual violence. Such a response, it argues, must be based on the openness to possibilities and refusal of sexual restrictions and regulations that have always been central elements of queer theory and politics.

Loss of Synaptic Connectivity, Particularly in Second Order Neurons Is a Key Feature of Diabetic Retinal Neuropathy in the Ins2Akita Mouse

Retinal neurodegeneration is a key component of diabetic retinopathy (DR), although the detailed neuronal damage remains ill-defined. Recent evidence suggests that in addition to amacrine and ganglion cell, diabetes may also impact on other retinal neurons. In this study, we examined retinal degenerative changes in Ins2^Akita diabetic mice. In scotopic electroretinograms (ERG), b-wave and oscillatory potentials were severely impaired in 9-month old Ins2^Akita mice. Despite no obvious pathology in fundoscopic examination, optical coherence tomography (OCT) revealed a progressive thinning of the retina from 3 months onwards. Cone but not rod photoreceptor loss was observed in 3-month-old diabetic mice. Severe impairment of synaptic connectivity at the outer plexiform layer (OPL) was detected in 9-month old Ins2Akita mice. Specifically, photoreceptor presynaptic ribbons were reduced by 25% and postsynaptic boutons by 70%, although the density of horizontal, rod- and cone-bipolar cells remained similar to non-diabetic controls. Significant reductions in GABAergic and glycinergic amacrine cells and Brn3a+ retinal ganglion cells were also observed in 9-month old Ins2^Akita mice. In conclusion, the Ins2^Akita mouse develops cone photoreceptor degeneration and the impairment of synaptic connectivity at the OPL, predominately resulting from the loss of postsynaptic terminal boutons. Our findings suggest that the Ins2^Akita mouse is a good model to study diabetic retinal neuropathy.

Hybrid address spaces: A methodology for implementing scalable high-level programming models on non-coherent many-core architectures

This paper introduces hybrid address spaces as a fundamental design methodology for implementing scalable runtime systems on many-core architectures without hardware support for cache coherence. We use hybrid address spaces for an implementation of MapReduce, a programming model for large-scale data processing, and the implementation of a remote memory access (RMA) model. Both implementations are available on the Intel SCC and are portable to similar architectures. We present the design and implementation of HyMR, a MapReduce runtime system whereby different stages and the synchronization operations between them alternate between a distributed memory address space and a shared memory address space, to improve performance and scalability. We compare HyMR to a reference implementation and we find that HyMR improves performance by a factor of 1.71× over a set of representative MapReduce benchmarks. We also compare HyMR with Phoenix++, a state-of-art implementation for systems with hardware-managed cache coherence in terms of scalability and sustained to peak data processing bandwidth, where HyMR demon- strates improvements of a factor of 3.1× and 3.2× respectively. We further evaluate our hybrid remote memory access (HyRMA) programming model and assess its performance to be superior of that of message passing.

Weaving behavior into feature models for embedded system families

Product Line software Engineering depends on capturing the commonality and variability within a family of products, typically using feature modeling, and using this information to evolve a generic reference architecture for the family. For embedded systems, possible variability in hardware and operating system platforms is an added complication. The design process can be facilitated by first exploring the behavior associated with features. In this paper we outline a bidirectional feature modeling scheme that supports the capture of commonality and variability in the platform environment as well as within the required software. Additionally, 'behavior' associated with features can be included in the overall model. This is achieved by integrating the UCM path notation in a way that exploits UCM's static and dynamic stubs to capture behavioral variability and link it to the feature model structure. The resulting model is a richer source of information to support the architecture development process.