Categorisation as Topographic Mapping between Uncorrelated Spaces

Abstract. In this paper, I propose a neurophysiologically plausible account for the evolution of arbitrary, categorical mental relationships. Topographic, or structure-preserving, mappings are widespread within animal brains. If they can be shown to generate behaviours in simulation, it is plausible that they are responsible for them in vivo. One behaviour has puzzled philosophers, psychologists and linguists alike: the categorical nature of language and its arbitrary associations between categories of form and meaning. I show here that arbitrary categorical relationships can arise when a topographic mapping is developed between continuous, but uncorrelated activation spaces. This is shown first by simulation, then identified in humans with synaesthesia. The independence of form and meaning as sensory or conceptual spaces automatically results in a categorial structure being imposed on each, as our brains attempt to link the spaces with topographic maps. This result suggests a neurophysiologically plausible explanation of categorisation in language

Towards music perception by redundancy reduction and unsupervised learning in probabilistic models

PhDThe study of music perception lies at the intersection of several disciplines: perceptual psychology and cognitive science, musicology, psychoacoustics, and acoustical signal processing amongst others. Developments in perceptual theory over the last fifty years have emphasised an approach based on Shannon’s information theory and its basis in probabilistic systems, and in particular, the idea that perceptual systems in animals develop through a process of unsupervised learning in response to natural sensory stimulation, whereby the emerging computational structures are well adapted to the statistical structure of natural scenes. In turn, these ideas are being applied to problems in music perception. This thesis is an investigation of the principle of redundancy reduction through unsupervised learning, as applied to representations of sound and music. In the first part, previous work is reviewed, drawing on literature from some of the fields mentioned above, and an argument presented in support of the idea that perception in general and music perception in particular can indeed be accommodated within a framework of unsupervised learning in probabilistic models. In the second part, two related methods are applied to two different low-level representations. Firstly, linear redundancy reduction (Independent Component Analysis) is applied to acoustic waveforms of speech and music. Secondly, the related method of sparse coding is applied to a spectral representation of polyphonic music, which proves to be enough both to recognise that the individual notes are the important structural elements, and to recover a rough transcription of the music. Finally, the concepts of distance and similarity are considered, drawing in ideas about noise, phase invariance, and topological maps. Some ecologically and information theoretically motivated distance measures are suggested, and put in to practice in a novel method, using multidimensional scaling (MDS), for visualising geometrically the dependency structure in a distributed representation.Engineering and Physical Science Research Counci

Self Organisation and Hierarchical Concept Representation in Networks of Spiking Neurons

The aim of this work is to introduce modular processing mechanisms for cortical functions implemented in networks of spiking neurons. Neural maps are a feature of cortical processing found to be generic throughout sensory cortical areas, and self-organisation to the fundamental properties of input spike trains has been shown to be an important property of cortical organisation. Additionally, oscillatory behaviour, temporal coding of information, and learning through spike timing dependent plasticity are all frequently observed in the cortex. The traditional self-organising map (SOM) algorithm attempts to capture the computational properties of this cortical self-organisation in a neural network. As such, a cognitive module for a spiking SOM using oscillations, phasic coding and STDP has been implemented. This model is capable of mapping to distributions of input data in a manner consistent with the traditional SOM algorithm, and of categorising generic input data sets. Higher-level cortical processing areas appear to feature a hierarchical category structure that is founded on a feature-based object representation. The spiking SOM model is therefore extended to facilitate input patterns in the form of sets of binary feature-object relations, such as those seen in the field of formal concept analysis. It is demonstrated that this extended model is capable of learning to represent the hierarchical conceptual structure of an input data set using the existing learning scheme. Furthermore, manipulations of network parameters allow the level of hierarchy used for either learning or recall to be adjusted, and the network is capable of learning comparable representations when trained with incomplete input patterns. Together these two modules provide related approaches to the generation of both topographic mapping and hierarchical representation of input spaces that can be potentially combined and used as the basis for advanced spiking neuron models of the learning of complex representations

PERCEIVE project - Deliverable D5.4 "Report of the comparative analysis of the correlation between topics emergent from regional discourses on the one hand, and the awareness and perceptions of the EU (from Eurobarometer) on the other hand"

The report at hand focuses on the statistical test of an eventual relationship between media representation of EU regional cohesion policy, among other explanatory variables, and individuals’ level of European identification as well as their different definitions of being European. At present time, it is largely acknowledged that individuals do not possess an innate sense of being European, rather, the meaning of such status is socially constructed. Accordingly, extant research has explored the role of the media in shaping the opinions of the general public. However, only recently has research started focusing on the potential role of cohesion policy in shaping EU identity and many aspects of this phenomenon are still unexplored. We claim that by extending knowledge in this still unfolding area our work contributes to the wider debate on European identity in several ways: a) by performing a media analysis in seven different countries we offer one of the first international evidences as most of the media analyses are conducted in individual national contexts; b) our study is based on a large ad-hoc designed survey which allows us to capture so far largely overlooked aspects of EU identity such as the multiplicity and synchronicity of levels – i.e. individual, regional and national – in a way that Eurobarometer-based research could not do so far; c) our survey also allowed us to explore in unprecedented depth the factors associated with different definitions of being European; d) we analyse media in a bottom-up way, that is, without using pre-coded frames of valence characterising most of extant research; e) along with standard interpretive techniques, we use formal methods for representing national media spaces such as topic modelling and sentiment analysis. We believe that this mixed method approach makes our study more replicable than purely qualitative ones, still, nuances and complexities of extracting meaning from text are better preserved than purely quantitative studies

Are they sustainable?

DL 57/2016/CP1453/CT0105 SFRH/BD/61544/2009 UIDB/04647/2020 UIDP/04647/2020In this study, past and current land-use and land-cover (LULC) change trajectories between 1947 and 2018 were analysed in terms of sustainability using a unique set of nine detailed, high-precision LULC thematic maps for the municipality of Portimão (Algarve region), Portugal. Several Geographic Information System (GIS)-based spatial analysis techniques were used to process LULC data and assess the spatiotemporal dynamics of LULC change processes. The dynamics of LULC change were explored by analysing LULC change trajectories. In addition, spatial pattern metrics were introduced to further investigate and quantify the spatial patterns of such LULC change trajectories. The findings show that Portimão has been experiencing complex LULC changes. Nearly 52% of the study area has undergone an LULC change at least once during the 71-year period. The analysis of spatial pattern metrics on LULC change trajectories confirmed the emergence of more complex, dispersed, and fragmented shapes when patches of land were converted from non-built categories into artificial surface categories from 1947 to 2018. The combined analysis of long-term LULC sequences by means of LULC change trajectories and spatial pattern metrics provided useful, actionable, and robust empirical information that can support sustainable spatial planning and smart growth, which is much needed since the results of this study have shown that the pattern of LULC change trajectories in Portimão municipality has been heading towards unsustainability.publishersversionpublishe

Latent Gaussian processes with composite likelihoods for data-driven disease stratification

Machine learning has caused a seismic shift on how clinical patient data is being used and interpreted. It can be harnessed for more effective and efficient healthcare that can benefit both patients and medical practitioners through personalised health solutions. Disease stratification is an important task in personalised medicine and has the potential to help medical researchers better understand diseases. In collaboration with the Helsinki Biobank and the Helsinki University Hospital, we aim to better understand clinical patient records comprising of multiple likelihoods (with noisy and missing values) by embedding these high-dimensional observations in to a low-dimensional space while capturing the similarity between the observations. In this thesis, we propose an unsupervised, generative model that can identify this latent clustering among patients while making use of all available data (i.e., in a heterogeneous data setting). We make use of deep neural networks and Gaussian process latent variable models (GPLVM) to create a form of non-linear dimensionality reduction for heterogeneous data. The key principle in our model is to use the output of latent GPs (sparse GPs) to modulate the parameters of the different likelihoods through link functions. The intractability introduced by the composite likelihoods is overcome by making use of sampling-based variational inference with quadrature. We make use of deep neural networks to parameterise the variational inference to introduce a constraint that balances between locality and dissimilarity preservation in the latent space. We demonstrated the effectiveness of our model on toy datasets and clinical data of Parkinson's disease patients treated at the HUS Helsinki University Hospital. Our approach identifies sub-groups from the heterogeneous patient data and we evaluated the differences in characteristics among the identified clusters using standard statistical tests

Pressure transients in water distribution networks: understanding their contribution to pipe repairs

Drinking water infrastructure functions to provide a service to meet customer demands and health requirements. Pipe repairs are one of the biggest challenges of ageing water infrastructure in the UK and world wide. Pressure transients resulting from sudden interruptions of the movement of the water can be caused by routine value operations. In a single pipeline one extreme event can burst a pipe. However the occurrences and impact of pressure transients in operational water distribution systems were not currently fully understood. This research developed new insights and understanding of pressure transient occurrences and their contribution to observed pipe repair rates. A large scale field monitoring program, including deploying and managing high-speed (100 Hz) instrumentation for 11 months, was designed and implemented to cover 67 district metered areas (DMA) subdivided into 79 pressure zones. In total 144 locations were monitored. The data was analysed using a novel method, termed transient fingerprint. This allowed the identification of discrete pressure transients and their three fundamental components (magnitude, duration and numbers of occurrences) leading to a quantitative interpretation of pressure transients. Evolutionary polynomial regression modelling was used to assess the impact of directly measured pressure transient data in context with static pressure, age, diameter and soil variables on 64 cast iron pipes. The analysis suggested that high magnitude, short duration repeatedly occurring pressure transients can have an adverse effect on the pipes. The extrapolation of pressure transient analysis into 7978 cast iron pipes showed inconclusive results suggesting that more accurate pressure transient data is required for each pipe in the network. Additional analysis carried out on 25 asbestos cement pipes, with actual measurements of pressure transients for each pipe, confirmed an adverse effect of pressure transient on water network observed in cast iron pipes. This research has provided an understanding of the occurrence of pressure transients that has implications on pipe management strategies. Mitigation techniques to locate pressure transient sources based on the project outcomes could be utilised to better manage distribution systems and ultimately reduce future pipe replacements and associated costs

Pressure transients in water distribution networks: understanding their contribution to pipe repairs

Drinking water infrastructure functions to provide a service to meet customer demands and health requirements. Pipe repairs are one of the biggest challenges of ageing water infrastructure in the UK and world wide. Pressure transients resulting from sudden interruptions of the movement of the water can be caused by routine value operations. In a single pipeline one extreme event can burst a pipe. However the occurrences and impact of pressure transients in operational water distribution systems were not currently fully understood. This research developed new insights and understanding of pressure transient occurrences and their contribution to observed pipe repair rates. A large scale field monitoring program, including deploying and managing high-speed (100 Hz) instrumentation for 11 months, was designed and implemented to cover 67 district metered areas (DMA) subdivided into 79 pressure zones. In total 144 locations were monitored. The data was analysed using a novel method, termed transient fingerprint. This allowed the identification of discrete pressure transients and their three fundamental components (magnitude, duration and numbers of occurrences) leading to a quantitative interpretation of pressure transients. Evolutionary polynomial regression modelling was used to assess the impact of directly measured pressure transient data in context with static pressure, age, diameter and soil variables on 64 cast iron pipes. The analysis suggested that high magnitude, short duration repeatedly occurring pressure transients can have an adverse effect on the pipes. The extrapolation of pressure transient analysis into 7978 cast iron pipes showed inconclusive results suggesting that more accurate pressure transient data is required for each pipe in the network. Additional analysis carried out on 25 asbestos cement pipes, with actual measurements of pressure transients for each pipe, confirmed an adverse effect of pressure transient on water network observed in cast iron pipes. This research has provided an understanding of the occurrence of pressure transients that has implications on pipe management strategies. Mitigation techniques to locate pressure transient sources based on the project outcomes could be utilised to better manage distribution systems and ultimately reduce future pipe replacements and associated costs

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

An evidential reasoning geospatial approach to transport corridor susceptibility zonation

PhD ThesisGiven the increased hazards faced by transport corridors such as climate induced extreme weather, it is essential that local spatial hot-spots of potential landslide susceptibility can be recognised. Traditionally, geotechnical survey and monitoring approaches have been used to recognise spatially landslide susceptibility zones. The increased availability of affordable very high resolution remotely-sensed datasets, such as airborne laser scanning (ALS) and multispectral aerial imagery, along with improved geospatial digital map data-sets, potentially allows the automated recognition of vulnerable earthwork slopes. However, the challenge remains to develop the analytical framework that allows such data to be integrated in an objective manner to recognise slopes potentially susceptible to failure. In this research, an evidential reasoning multi-source geospatial integration approach for the broad-scale recognition and prediction of landslide susceptibility in transport corridors has been developed. Airborne laser scanning and Ordnance Survey DTM data is used to derive slope stability parameters (slope gradient, aspect, terrain wetness index (TWI), stream power index (SPI) and curvature), while Compact Airborne Spectrographic Imager (CASI) imagery, and existing national scale digital map data-sets are used to characterise the spatial variability of land cover, land use and soil type. A novel approach to characterisation of soil moisture distribution within transport corridors is developed that incorporates the effects of the catchment contribution to local zones of moisture concentration in earthworks. In this approach, the land cover and soil type of the wider catchment are used to estimate the spatial contribution of precipitation contributing to surface runoff, which in turn is used to parameterise a weighted terrain accumulation flow model. The derived topographic and land use properties of the transport corridor are integrated within the evidential reasoning approach to characterise numeric measures of belief, disbelief and uncertainty regarding slope instability spatially within the transport corridor. Evidential reasoning was employed as it offers the ability to derive an objective weighting of the relative importance of each derived property to the final estimation of landslide susceptibility, whilst allowing the uncertainty of the properties to be taken into account. The developed framework was applied to railway transport earthworks located near Haltwhistle in northern England, UK. This section of the Carlisle-Newcastle rail line has a ii history of instability with the occurrence of numerous minor landslides in recent years. Results on spatial distribution of soil moisture indicate considerable contribution of the surrounding wider catchment topography to the localised zones of moisture accumulation. The degrees of belief and disbelief indicated the importance of slope with gradients between 250 to 350 and concave curvature. Permeable soils with variable intercalations accounted for over 80% of slope instability with 5.1% of the earthwork cuttings identified as relatively unstable in contrast to 47.5% for the earthwork embankment. The developed approach was found to have a goodness of fit of 88.5% with respect to the failed slopes used to parametrise the evidential reasoning model and an overall predictive capability of 77.75% based on independent validation dataset.TETFUND Nigeria, Nasarawa State University and my family members for their financial support towards the completion of the PhD programme