The hierarchy of directional interactions in visual motion processing

It is well known that context influences our perception of visual motion direction. For example, spatial and temporal context manipulations can be used to induce two well-known motion illusions: direction repulsion and the direction after-effect (DAE). Both result in inaccurate perception of direction when a moving pattern is either superimposed on (direction repulsion), or presented following adaptation to (DAE), another pattern moving in a different direction. Remarkable similarities in tuning characteristics suggest that common processes underlie the two illusions. What is not clear, however, is whether the processes driving the two illusions are expressions of the same or different neural substrates. Here we report two experiments demonstrating that direction repulsion and the DAE are, in fact, expressions of different neural substrates. Our strategy was to use each of the illusions to create a distorted perceptual representation upon which the mechanisms generating the other illusion could potentially operate. We found that the processes mediating direction repulsion did indeed access the distorted perceptual representation induced by the DAE. Conversely, the DAE was unaffected by direction repulsion. Thus parallels in perceptual phenomenology do not necessarily imply common neural substrates. Our results also demonstrate that the neural processes driving the DAE occur at an earlier stage of motion processing than those underlying direction repulsion.

SoC memory hierarchy derivation from dataflow graphs

Hardware synthesis from dataflow graphs of signal processing systems is a growing research area as focus shifts to high level design methodologies. For data intensive systems, dataflow based synthesis can lead to an inefficient usage of memory due to the restrictive nature of synchronous dataflow and its inability to easily model data reuse. This paper explores how dataflow graph changes can be used to drive both the on-chip and off-chip memory organisation and how these memory architectures can be mapped to a hardware implementation. By exploiting the data reuse inherent to many image processing algorithms and by creating memory hierarchies, off-chip memory bandwidth can be reduced by a factor of a thousand from the original dataflow graph level specification of a motion estimation algorithm, with a minimal increase in memory size. This analysis is verified using results gathered from implementation of the motion estimation algorithm on a Xilinx Virtex-4 FPGA, where the delay between the memories and processing elements drops from 14.2 ns down to 1.878 ns through the refinement of the memory architecture. Care must be taken when modeling these algorithms however, as inefficiencies in these models can be easily translated into overuse of hardware resources.

Valved Dataflow For FPGA Memory Hierarchy Synthesis

For modern FPGA, implementation of memory intensive processing applications such as high end image and video processing systems necessitates manual design of complex multilevel memory hierarchies incorporating off-chip DDR and onchip BRAM and LUT RAM. In fact, automated synthesis of multi-level memory hierarchies is an open problem facing high level synthesis technologies for FPGA devices. In this paper we describe the first automated solution to this problem.
By exploiting a novel dataflow application modelling dialect, known as Valved Dataflow, we show for the first time how, not only can such architectures be automatically derived, but also that the resulting implementations support real-time processing for current image processing application standards such as H.264. We demonstrate the viability of this approach by reporting the performance and cost of hierarchies automatically generated for Motion Estimation, Matrix Multiplication and Sobel Edge Detection applications on Virtex-5 FPGA.

Hierarchy as a barrier to advancement for women in academic medicine

Background: Research on barriers to professional advancement for women in academic medicine has not adequately considered the role of environmental factors and how the structure of organizations affects professional advancement and work experiences. This article examines the impact of the hierarchy, including both the organization's hierarchical structure and professionals' perceptions of this structure, in medical school organization on faculty members' experience and advancement in academic medicine. Methods: As part of an inductive qualitative study of faculty in five disparate U.S. medical schools, we interviewed 96 medical faculty at different career stages and in diverse specialties, using in-depth semistructured interviews, about their perceptions about and experiences in academic medicine. Data were coded and analysis was conducted in the grounded theory tradition. Results: Our respondents saw the hierarchy of chairs, based on the indeterminate tenure of department chairs, as a central characteristic of the structure of academic medicine. Many faculty saw this hierarchy as affecting inclusion, reducing transparency in decision making, and impeding advancement. Indeterminate chair terms lessen turnover and may create a bottleneck for advancement. Both men and women faculty perceived this hierarchy, but women saw it as more consequential. Conclusions: The hierarchical structure of academic medicine has a significant impact on faculty work experiences, including advancement, especially for women. We suggest that medical schools consider alternative models of leadership and managerial styles, including fixed terms for chairs with a greater emphasis on inclusion. This is a structural reform that could increase opportunities for advancement especially for women in academic medicine. © 2010 Copyright Mary Ann Liebert, Inc.

Efficient tracking of human poses using a manifold hierarchy

In this paper a 3D human pose tracking framework is presented. A new dimensionality reduction method (Hierarchical Temporal Laplacian Eigenmaps) is introduced to represent activities in hierarchies of low dimensional spaces. Such a hierarchy provides increasing independence between limbs, allowing higher flexibility and adaptability that result in improved accuracy. Moreover, a novel deterministic optimisation method (Hierarchical Manifold Search) is applied to estimate efficiently the position of the corresponding body parts. Finally, evaluation on public datasets such as HumanEva demonstrates that our approach achieves a 62.5mm-65mm average joint error for the walking activity and outperforms state-of-the-art methods in terms of accuracy and computational cost.