title: Autoencoder-based techniques for improved classification in settings with high dimensional and small sized data

authors: Daoud, Maisa

subjects: Autoencoder, cancer prediction models

abstract: Neural network models have been widely tested and analysed usinglarge sized high dimensional datasets.  In real world application prob-lems,  the available datasets are often limited in size due to reasonsrelated to the cost or difficulties encountered while collecting the data.This  limitation  in  the  number  of  examples  may  challenge  the  clas-sification  algorithms  and  degrade  their  performance.   A  motivatingexample for this kind of problem is predicting the health status of atissue given its gene expression, when the number of samples availableto learn from is very small.Gene expression data has distinguishing characteristics attracting themachine  learning  research  community.   The  high  dimensionality  ofthe data is one of the integral features that has to be considered whenbuilding predicting models.  A single sample of the data is expressedby thousands of gene expressions compared to the benchmark imagesand texts that only have a few hundreds of features and commonlyused for analysing the existing models.  Gene expression data samplesare  also  distributed  unequally  among  the  classes;  in  addition,  theyinclude noisy features which degrade the prediction accuracy of themodels.   These  characteristics  give  rise  to  the  need  for  using  effec-tive dimensionality reduction methods that are able to discover thecomplex relationships between the features such as the autoencoders.
This  thesis  investigates  the  problem  of  predicting  from  small  sizedhigh  dimensional  datasets  by  introducing  novel  autoencoder-basedtechniques  to  increase  the  classification  accuracy  of  the  data.   Twoautoencoder-based  methods  for  generating  synthetic  data  examplesand synthetic representations of the data were respectively introducedin the first stage of the study.  Both of these methods are applicableto the testing phase of the autoencoder and showed successful in in-creasing the predictability of the data.Enhancing the autoencoder’s ability in learning from small sized im-balanced  data  was  investigated  in  the  second  stage  of  the  projectto come up with techniques that improved the autoencoder’s gener-ated  representations.   Employing  the  radial  basis  activation  mecha-nism used in radial-basis function networks, which learn in a super-vised manner, was a solution provided by this thesis to enhance therepresentations learned by unsupervised algorithms.  This techniquewas later applied to stochastic variational autoencoders and showedpromising results in learning discriminating representations from thegene expression data.The contributions of this thesis can be described by a number of differ-ent methods applicable to different stages (training and testing) anddifferent autoencoder models (deterministic and stochastic) which, in-dividually, allow for enhancing the predictability of small sized highdimensional datasets compared to well known baseline methods.

text: Autoencoder-based techniques for improved classification in settings with high dimensional and small sized data Neural network models have been widely tested and analysed usinglarge sized high dimensional datasets.  In real world application prob-lems,  the available datasets are often limited in size due to reasonsrelated to the cost or difficulties encountered while collecting the data.This  limitation  in  the  number  of  examples  may  challenge  the  clas-sification  algorithms  and  degrade  their  performance.   A  motivatingexample for this kind of problem is predicting the health status of atissue given its gene expression, when the number of samples availableto learn from is very small.Gene expression data has distinguishing characteristics attracting themachine  learning  research  community.   The  high  dimensionality  ofthe data is one of the integral features that has to be considered whenbuilding predicting models.  A single sample of the data is expressedby thousands of gene expressions compared to the benchmark imagesand texts that only have a few hundreds of features and commonlyused for analysing the existing models.  Gene expression data samplesare  also  distributed  unequally  among  the  classes;  in  addition,  theyinclude noisy features which degrade the prediction accuracy of themodels.   These  characteristics  give  rise  to  the  need  for  using  effec-tive dimensionality reduction methods that are able to discover thecomplex relationships between the features such as the autoencoders.
This  thesis  investigates  the  problem  of  predicting  from  small  sizedhigh  dimensional  datasets  by  introducing  novel  autoencoder-basedtechniques  to  increase  the  classification  accuracy  of  the  data.   Twoautoencoder-based  methods  for  generating  synthetic  data  examplesand synthetic representations of the data were respectively introducedin the first stage of the study.  Both of these methods are applicableto the testing phase of the autoencoder and showed successful in in-creasing the predictability of the data.Enhancing the autoencoder’s ability in learning from small sized im-balanced  data  was  investigated  in  the  second  stage  of  the  projectto come up with techniques that improved the autoencoder’s gener-ated  representations.   Employing  the  radial  basis  activation  mecha-nism used in radial-basis function networks, which learn in a super-vised manner, was a solution provided by this thesis to enhance therepresentations learned by unsupervised algorithms.  This techniquewas later applied to stochastic variational autoencoders and showedpromising results in learning discriminating representations from thegene expression data.The contributions of this thesis can be described by a number of differ-ent methods applicable to different stages (training and testing) anddifferent autoencoder models (deterministic and stochastic) which, in-dividually, allow for enhancing the predictability of small sized highdimensional datasets compared to well known baseline methods.

year: 2020

title: The effects of tides on swash statistics on an intermediate beach

authors: Guedes, Rafael Marcondes Carvalho

subjects: intermediate beach

abstract: Swash hydrodynamics were investigated on an intermediate beach using runup data obtained from video images. Under mild, near-constant, offshore wave conditions, the presence of a sandbar and the tidally controlled water depth over its crest determined whether most of the incoming waves broke before reaching the shoreline. This forced a change in the pattern of wave energy dissipation across the surf zone between low and high tide, which was reflected by changes to swash on time scales of a few hours. Significant runup height (Rs, defined as 4 times the standard deviation of the waterline time series), was found to vary by a factor of 2 between low tide, when most of the waves were breaking over the sandbar (Rs/Hs ≈ 1.5, where Hs is the offshore significant wave height) and high tide, when the waves were barely breaking (Rs/Hs ≈ 2.7). The increase in wave energy dissipation during low tide was also associated with changes in swash maxima distribution, a decrease in mean swash period, and increasing energy at infragravity frequencies. Bispectral analysis suggested that this infragravity modulation might have been connected with the presence of secondary waves.

text: The effects of tides on swash statistics on an intermediate beach Swash hydrodynamics were investigated on an intermediate beach using runup data obtained from video images. Under mild, near-constant, offshore wave conditions, the presence of a sandbar and the tidally controlled water depth over its crest determined whether most of the incoming waves broke before reaching the shoreline. This forced a change in the pattern of wave energy dissipation across the surf zone between low and high tide, which was reflected by changes to swash on time scales of a few hours. Significant runup height (Rs, defined as 4 times the standard deviation of the waterline time series), was found to vary by a factor of 2 between low tide, when most of the waves were breaking over the sandbar (Rs/Hs ≈ 1.5, where Hs is the offshore significant wave height) and high tide, when the waves were barely breaking (Rs/Hs ≈ 2.7). The increase in wave energy dissipation during low tide was also associated with changes in swash maxima distribution, a decrease in mean swash period, and increasing energy at infragravity frequencies. Bispectral analysis suggested that this infragravity modulation might have been connected with the presence of secondary waves.

year: 2011

title: Sustainability and Indigeneity

authors: Daya-Winterbottom, Trevor

abstract: In the lead up to the Rio Conference on Environment and Development 1992, New Zealand enshrined the concept of the sustainable management of natural and physical resources in statute law by enacting the Resource Management Act 1991. Implementing the concept of sustainable management in domestic law gave rise to a tension between anthropocentric and ecological approaches for implementing the statute that were not resolved by the Courts until 2014. However, the adoption of an ecological approach to implementing the statute did not assuage the desire for statutory reform unlocked by the previous debate. Most recently, the reform trajectory has resulted in Parliamentary scrutiny of an exposure draft Bill that replaces the concept of sustainable management with a new concept of the welfare and wellbeing of the environment rooted firmly in indigenous perspectives on earth governance, guardianship, and intergenerational equity. My paper will critically interrogate the implications of adopting the concept of the welfare and wellbeing of the environment for the future of sustainability, and what implications this approach could have for environmental law generally in the Anthropocene both comparatively and internationally.

text: Sustainability and Indigeneity In the lead up to the Rio Conference on Environment and Development 1992, New Zealand enshrined the concept of the sustainable management of natural and physical resources in statute law by enacting the Resource Management Act 1991. Implementing the concept of sustainable management in domestic law gave rise to a tension between anthropocentric and ecological approaches for implementing the statute that were not resolved by the Courts until 2014. However, the adoption of an ecological approach to implementing the statute did not assuage the desire for statutory reform unlocked by the previous debate. Most recently, the reform trajectory has resulted in Parliamentary scrutiny of an exposure draft Bill that replaces the concept of sustainable management with a new concept of the welfare and wellbeing of the environment rooted firmly in indigenous perspectives on earth governance, guardianship, and intergenerational equity. My paper will critically interrogate the implications of adopting the concept of the welfare and wellbeing of the environment for the future of sustainability, and what implications this approach could have for environmental law generally in the Anthropocene both comparatively and internationally.

year: 2022