A binary self-organizing map and its FPGA implementation

A binary Self Organizing Map (SOM) has been designed and implemented on a Field Programmable Gate Array (FPGA) chip. A novel learning algorithm which takes binary inputs and maintains tri-state weights is presented. The binary SOM has the capability of recognizing binary input sequences after training. A novel tri-state rule is used in updating the network weights during the training phase. The rule implementation is highly suited to the FPGA architecture, and allows extremely rapid training. This architecture may be used in real-time for fast pattern clustering and classification of the binary features

A modified neural network model for Lobula Giant Movement Detector with additional depth movement feature

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron that is located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of the approaching object and its proximity. It has been found that it can respond to looming stimuli very quickly and can trigger avoidance reactions whenever a rapidly approaching object is detected. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper proposes a modified LGMD model that provides additional movement depth direction information. The proposed model retains the simplicity of the previous neural network model, adding only a few new cells. It has been tested on both simulated and recorded video data sets. The experimental results shows that the modified model can very efficiently provide stable information on the depth direction of movement

A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector

Performativity, fabrication and trust: exploring computer-mediated moderation

Based on research conducted in an English secondary school, this paper explores computer mediated moderation as a performative tool. The Module Assessment Meeting (MAM) was the moderation approach under investigation. I mobilise ethnographic data generated by a key informant, and triangulated with that from other actors in the setting, in order to examine some of the meanings underpinning moderation within a performative environment. Drawing on the work of Ball (2003), Lyotard (1979) and Foucault (1977, 1979), I argue that in this particular case performativity has become entrenched in teachers’ day-to-day practices, and not only affects those practices but also teachers’ sense of self. I suggest that MAM represented performative and fabricated conditions and (re)defined what the key participant experienced as a vital constituent of her educational identities - trust. From examining the case in point, I hope to have illustrated for those interested in teachers’ work some of the implications of the interface between technology and performativity

‘I’m not your mother’: British social realism, neoliberalism and the maternal subject in Sally Wainwright’s Happy Valley (BBC1 2014-2016)

This article examines Sally Wainwright's Happy Valley (BBC1, 2014–2016) in the context of recent feminist attempts to theorise the idea of a maternal subject. Happy Valley, a police series set in an economically disadvantaged community in West Yorkshire, has been seen as expanding the genre of British social realism, in its focus on strong Northern women, by giving it ‘a female voice’ (Gorton, 2016: 73). I argue that its challenge is more substantial. Both the tradition of British social realism on which the series draws, and the neoliberal narratives of the family which formed the discursive context of its production, I argue, are founded on a social imaginary in which the mother is seen as responsible for the production of the selves of others, but cannot herself be a subject. The series itself, however, places at its centre an active, articulate, mobile and angry maternal subject. In so doing, it radically contests both a tradition of British social realism rooted in male nostalgia and more recent neoliberal narratives of maternal guilt and lifestyle choice. It does this through a more fundamental contestation: of the wider cultural narratives about selfhood and the maternal that underpin both. Its reflective maternal subject, whose narrative journey involves acceptance of an irrecoverable loss, anger and guilt as a crucial aspect of subjectivity, and who embodies an ethics of relationality, is a figure impossible in conventional accounts of subject and nation. She can be understood, however, in terms of recent feminist theories of the maternal

Corrigendum to “Palaeohydrogeology and Transport Parameters Derived from 4He and Cl Profiles in Aquitard Pore Waters in a Large Multilayer Aquifer System, Central Australia”

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the article titled “Palaeohydrogeology and Transport Parameters Derived from 4 He and Cl Profiles in Aquitard Pore Waters in a Large Multilayer Aquifer System, Central Australia” [1], Mr. Stanley D. Smith was missing from the authors’ list. Mr. Stanley made a significant contribution in helping with the core sampling protocol, canister leak testing, and discussing modelling methods. The corrected authors’ list is shown above

Assessing uncertainty and complexity in regional-scale crop model simulations

Crop models are imperfect approximations to real world interactions between biotic and abiotic factors. In some situations, the uncertainties associated with choices in model structure, model inputs and parameters can exceed the spatiotemporal variability of simulated yields, thus limiting predictability. For Indian groundnut, we used the General Large Area Model for annual crops (GLAM) with an existing framework to decompose uncertainty, to first understand how skill changes with added model complexity, and then to determine the relevant uncertainty sources in yield and other prognostic variables (total biomass, leaf area index and harvest index). We developed an ensemble of simulations by perturbing GLAM parameters using two different input meteorology datasets, and two model versions that differ in the complexity with which they account for assimilation. We found that added complexity improved model skill, as measured by changes in the root mean squared error (RMSE), by 5-10% in specific pockets of western, central and southern India, but that 85% of the groundnut growing area either did not show improved skill or showed decreased skill from such added complexity. Thus, adding complexity or using overly complex models at regional or global scales should be exercised with caution. Uncertainty analysis indicated that, in situations where soil and air moisture dynamics are the major determinants of productivity, predictability in yield is high. Where uncertainty for yield is high, the choice of weather input data was found critical for reducing uncertainty. However, for other prognostic variables (including leaf area index, total biomass and the harvest index) parametric uncertainty was generally the most important source, with a contribution of up to 90% in some cases, suggesting that regional-scale data additional to yield to constrain model parameters is needed. Our study provides further evidence that regional-scale studies should explicitly quantify multiple uncertainty sources

Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

<p>Abstract</p> <p>Background</p> <p>In practice many biological time series measurements, including gene microarrays, are conducted at time points that seem to be interesting in the biologist's opinion and not necessarily at fixed time intervals. In many circumstances we are interested in finding targets that are expressed periodically. To tackle the problems of uneven sampling and unknown type of noise in periodicity detection, we propose to use robust regression.</p> <p>Methods</p> <p>The aim of this paper is to develop a general framework for robust periodicity detection and review and rank different approaches by means of simulations. We also show the results for some real measurement data.</p> <p>Results</p> <p>The simulation results clearly show that when the sampling of time series gets more and more uneven, the methods that assume even sampling become unusable. We find that M-estimation provides a good compromise between robustness and computational efficiency.</p> <p>Conclusion</p> <p>Since uneven sampling occurs often in biological measurements, the robust methods developed in this paper are expected to have many uses. The regression based formulation of the periodicity detection problem easily adapts to non-uniform sampling. Using robust regression helps to reject inconsistently behaving data points.</p> <p>Availability</p> <p>The implementations are currently available for Matlab and will be made available for the users of R as well. More information can be found in the web-supplement <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p