The problem of learning long-term dependencies in sequences using Recurrent Neural Networks (RNNs) is still a major challenge. Recent methods have been suggested to solve this problem by constraining the transition matrix to be unitary during training which ensures that its norm is equal to one and prevents exploding gradients. These methods either have limited expressiveness or scale poorly with the size of the network when compared with the simple RNN case, especially when using stochastic gradient descent with a small mini-batch size. Our contributions are as follows; we first show that constraining the transition matrix to be unitary is a special case of an orthogonal constraint. Then we present a new parametrisation of the transition matrix which allows efficient training of an RNN while ensuring that the matrix is always orthogonal. Our results show that the orthogonal constraint on the transition matrix applied through our parametrisation gives similar benefits to the unitary constraint, without the time complexity limitations.

BACKGROUND: Despite ongoing reduction in genotyping costs, genomic studies involving large numbers of species with low economic value (such as Black Tiger prawns) remain cost prohibitive. In this scenario DNA pooling is an attractive option to reduce genotyping costs. However, genotyping of pooled samples comprising DNA from many individuals is challenging due to the presence of errors that exceed the allele frequency quantisation size and therefore cannot be simply corrected by clustering techniques. The solution to the calibration problem is a correction to the allele frequency to mitigate errors incurred in the measurement process. We highlight the limitations of the existing calibration solutions such as the fact they impose assumptions on the variation between allele frequencies 0, 0.5, and 1.0, and address a limited set of error types. We propose a novel machine learning method to address the limitations identified. RESULTS: The approach is tested on SNPs genotyped with the Sequenom iPLEX platform and compared to existing state of the art calibration methods. The new method is capable of reducing the mean square error in allele frequency to half that achievable with existing approaches. Furthermore for the first time we demonstrate the importance of carefully considering the choice of training data when using calibration approaches built from pooled data. CONCLUSION: This paper demonstrates that improvements in pooled allele frequency estimates result if the genotyping platform is characterised at allele frequencies other than the homozygous and heterozygous cases. Techniques capable of incorporating such information are described along with aspects of implementation.

Due to frost and insufficient exposure to sunlight, some grape bunches remain undeveloped during harvesting. For automation of harvesting, it is required to automatically identify the mature grape bunches. This paper presents a sequence of image processing and computational intelligence methods to identify mature grape bunches. It's a two-step process where in the first step the grape bunches are separated from the background of an image and in the second step the grape bunch is classified into mature and undeveloped group. We achieved 96.88% accuracy on the images obtained from a strip of vineyard in Cambridge, Tasmania.

New sensor streams are being generated at a rapidly increasing rate. The sources of these streams are a diverse set of networked sensors, diverse both in sensing hardware and sensing modality. Machine learning algorithms are ideally placed to develop generalized methods for stream analysis. One exemplar problem is the detection and analysis of periodic structure within these streams. Our contribution is the proposal of a new machine learning framework that (i) classifies a signal as periodic or aperiodic, (ii) further analyses the signal to find periodic structure using a neural network, and (iii) groups the motifs in the periodic signals using a modified Self Organising Map algorithm. We also demonstrate the framework using data generated by an Oyster heart rate sensor. We find that the generalized approach our classifier improves the detection of signal periods by reducing the number of functions classified as periodic from 11% to 9%; however, most benefit occurs for period calculation with the number of erroneously calculated periods reducing from 14% to 4%.

The combination of low density SNP arrays and DNA pooling is a fast and cost effective approach to genotyping that opens up basic genomics to a range of new applications and studies. However we have identified significant limitations in the existing approach to calculating allele frequencies with DNA pooling. These limitations include a reduced ability to deal with SNP to SNP variation via the standard interpolation method. Our contribution is a new hierarchical learning framework which resolves these drawbacks. The framework involves a hierarchy of two greedily trained layers of learners. The first layer learns the bias of each SNP then applies a calibration to reduce SNP bias by mapping into a common coordinate system across all SNPs. The second layer learns an allele frequency function exploiting the global SNP data. A range of algorithms have been applied including linear regression, neural network and support vector regression. The framework has been tested on pooled samples of Black Tiger prawns that have been genotyped with low density Sequenom iPLEX panels. Analysis of pooled samples and the corresponding individually genotyped SNP samples indicate the pooling approach introduces an allele frequency RMS error of 0.12. The existing calibration approach corrects ~14% of the error. Our hierarchical approach is 4.5 times as effective by correcting for ~64% of the introduced error. This is a significant reduction and has the potential to enable genetic studies previously not possible due to allele frequency error. Although testing so far is limited to low density SNP arrays the approach was developed to generalize to other SNP genotyping technologies.