Multilabel Prototype Generation for data reduction in K-Nearest Neighbour classification

Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel kNN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving—both in terms of efficiency and classification performance—the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.This research was partially funded by the Spanish Ministerio de Ciencia e Innovación through the MultiScore (PID2020-118447RA-I00) and DOREMI (TED2021-132103A-I00) projects. The first author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”

Advances in quantum machine learning

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.Comment: 38 pages, 17 Figure

Visual Representation Learning with Limited Supervision

The quality of a Computer Vision system is proportional to the rigor of data representation it is built upon. Learning expressive representations of images is therefore the centerpiece to almost every computer vision application, including image search, object detection and classification, human re-identification, object tracking, pose understanding, image-to-image translation, and embodied agent navigation to name a few. Deep Neural Networks are most often seen among the modern methods of representation learning. The limitation is, however, that deep representation learning methods require extremely large amounts of manually labeled data for training. Clearly, annotating vast amounts of images for various environments is infeasible due to cost and time constraints. This requirement of obtaining labeled data is a prime restriction regarding pace of the development of visual recognition systems. In order to cope with the exponentially growing amounts of visual data generated daily, machine learning algorithms have to at least strive to scale at a similar rate. The second challenge consists in the learned representations having to generalize to novel objects, classes, environments and tasks in order to accommodate to the diversity of the visual world. Despite the evergrowing number of recent publications tangentially addressing the topic of learning generalizable representations, efficient generalization is yet to be achieved. This dissertation attempts to tackle the problem of learning visual representations that can generalize to novel settings while requiring few labeled examples. In this research, we study the limitations of the existing supervised representation learning approaches and propose a framework that improves the generalization of learned features by exploiting visual similarities between images which are not captured by provided manual annotations. Furthermore, to mitigate the common requirement of large scale manually annotated datasets, we propose several approaches that can learn expressive representations without human-attributed labels, in a self-supervised fashion, by grouping highly-similar samples into surrogate classes based on progressively learned representations. The development of computer vision as science is preconditioned upon the seamless ability of a machine to record and disentangle pictures' attributes that were expected to only be conceived by humans. As such, particular interest was dedicated to the ability to analyze the means of artistic expression and style which depicts a more complex task than merely breaking an image down to colors and pixels. The ultimate test for this ability is the task of style transfer which involves altering the style of an image while keeping its content. An effective solution of style transfer requires learning such image representation which would allow disentangling image style and its content. Moreover, particular artistic styles come with idiosyncrasies that affect which content details should be preserved and which discarded. Another pitfall here is that it is impossible to get pixel-wise annotations of style and how the style should be altered. We address this problem by proposing an unsupervised approach that enables encoding the image content in such a way that is required by a particular style. The proposed approach exchanges the style of an input image by first extracting the content representation in a style-aware way and then rendering it in a new style using a style-specific decoder network, achieving compelling results in image and video stylization. Finally, we combine supervised and self-supervised representation learning techniques for the task of human and animals pose understanding. The proposed method enables transfer of the representation learned for recognition of human poses to proximal mammal species without using labeled animal images. This approach is not limited to dense pose estimation and could potentially enable autonomous agents from robots to self-driving cars to retrain themselves and adapt to novel environments based on learning from previous experiences

Assessment of the variability of spatial interpolation methods using elevation and drill hole data over the Magmont mine area, south-east Missouri

Spatial interpolation methods are widely used in fields of geoscience such as mineral exploration. Interpolation methods translate the distribution of discrete data into continuous field over a given study area. Many methods exist and operate differently. Choosing judiciously the best interpolation method calls for an understanding of the algorithm, the intent or goal of the investigation and the knowledge of the study area. In the field of mineral exploration, accurate assessment is important because both overestimation and underestimation at spatially defined variables result in varied consequences. Assessment of methods' variability can be used as an additional criterion to help make an informed choice. Here, eight interpolation methods were tested on two spatial data sets consisting of topographic surface elevations and subsurface elevations of the top and the bottom of lead orebody at the Magmont mine area, in South-east Missouri. Variability between the interpolation methods was assessed based on statistical paired t-test of each method against a reference value, geometric analysis the map algebra tool in Arcmap 10.4.1 and comparison of their algorithms. Two of the methods returned values not significantly different from the reference value while the others were less robust. In testing model variability a second time on a reduced sample size, results suggest that interpolation methods are sensitive to sample size. Similarly, building the orebody top and bottom surfaces from information on the depths across the mineralized intersection showed dissemblance among methods. Key words: spatial interpolation, GIS, Magmont mine area, variability, math algebra, paired t-test

The behavioural ecology of the whooper swan (Cygnus cygnus cygnus)

The behaviour and ecology of Whooper Swans (Cygnus cygnus) were studied on the wintering grounds in Scotland and the summering grounds in Iceland, with a view to extending our general knowledge of the biology of this little studied species. Comparisons are drawn between feeding behaviour shown in terrestrial, freshwater and marine habitats. In Central Scotland Whooper Swans were found to feed mainly on agricultural land and to actively select stubble fields, where they fed on waste grain, from their arrival in autumn until mid-winter. They then changed to feeding on grass from mid-winter until their departure in the spring. They were found to have adopted an activity pattern similar to that of geese, i. e. they were diurnal and flew each morning and evening between a roost-site and a feeding site. The daily activity cycle of feeding varies between habitats, but the differences are not fully explained by functional requirements. The factors affecting the timing of morning and evening flights are discussed. The length of the feeding day increased with daylength and the level of feeding per hour increased so that more time was spent feeding in the spring than at any time during the winter. Although Whooper Swans were found to compensate to some extent for the shortness of winter days by departing to the roost later relative to sunset, it is suggested that it is in the spring when their energetic requirements are highest; they need to store enough energy for migration and breeding and/or moult. The percentage of birds head-up was found to decline curvi-linearly with increasing flock size while the percentage feeding increased. Since there was no apparent relationship between peck rate and flock size, birds in larger flocks gain from increased food intake. A seasonal change in flock size was noted in Central Scotland with larger flocks occurring more frequently between autumn arrival and mid-winter than from mid-winter to spring departure. Although other factors may be involved as well, it is suggested that the advantages of flocking to Whooper Swans may vary depending on whether the food is patchily distributed (waste grain), or relatively uniformly distributed (grass). Differences in the amount of time allocated to feeding and vigilance were analyzed according to age and breeding status, and seasonal changes are discussed. The breeding success of the Whooper Swan, measured using the percentage of cygnets and the mean brood size, was found to vary greatly from year to year. Measurements of mean brood size in Iceland during summer were found to correspond well with those in Scotland the following winter. A particularly poor breeding season in 1979, a year with a very late spring, was noted both in Iceland and on the wintering grounds in Scotland. Whooper Swans are monogamous and territorial. The female does most of the nest building and all of the incubation, while the male remains on the territory, usually either vigilant or feeding. The range of behaviours exhibited by males and females during the incubation and fledging are described and the time allocated to them is analyzed. Displays between adjacent territory holders are described for the first time. These displays were found to be commoner during the fledging period than during incubation. Females were also observed to take part in defence against intruders and it is suggested that an important role of the Whooper Swan's territory is to provide a safe feeding area for the family after hatching. Behavioural co-operation between mates helped to maintain a high degree of protection for the nest and cygnets. Cygnets maintained closer proximity to each other than to their parents and tended to associate with a single parent; usually the female. As cygnets aged, distances between them and from them to their parents increased and their parents spent more time feeding and less time vigilant. The behaviour of non-breeding birds is also described and it is suggested that non-breeders tend to moult in a separate flock from failed breeders. Moult and migration are also discussed in order to provide as full a picture as possible of the Whooper Swan'

Algorithms for multi-point range query and reverse nearest neighbour search

Ph.DDOCTOR OF PHILOSOPH