A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Evolutionary and convergence stability for continuous phenotypes in finite populations derived from two-allele models.

The evolution of a quantitative phenotype is often envisioned as a trait substitution sequence where mutant alleles repeatedly replace resident ones. In infinite populations, the invasion fitness of a mutant in this two-allele representation of the evolutionary process is used to characterize features about long-term phenotypic evolution, such as singular points, convergence stability (established from first-order effects of selection), branching points, and evolutionary stability (established from second-order effects of selection). Here, we try to characterize long-term phenotypic evolution in finite populations from this two-allele representation of the evolutionary process. We construct a stochastic model describing evolutionary dynamics at non-rare mutant allele frequency. We then derive stability conditions based on stationary average mutant frequencies in the presence of vanishing mutation rates. We find that the second-order stability condition obtained from second-order effects of selection is identical to convergence stability. Thus, in two-allele systems in finite populations, convergence stability is enough to characterize long-term evolution under the trait substitution sequence assumption. We perform individual-based simulations to confirm our analytic results.

THE USE OF SIMULATIONS IN EVOLUTIONARY POPULATION GENETICS: APPLICATIONS ON HUMANS, OWLS AND VIRTUAL ORGANISMS

Summary (in English) Computer simulations provide a practical way to address scientific questions that would be otherwise intractable. In evolutionary biology, and in population genetics in particular, the investigation of evolutionary processes frequently involves the implementation of complex models, making simulations a particularly valuable tool in the area. In this thesis work, I explored three questions involving the geographical range expansion of populations, taking advantage of spatially explicit simulations coupled with approximate Bayesian computation. First, the neutral evolutionary history of the human spread around the world was investigated, leading to a surprisingly simple model: A straightforward diffusion process of migrations from east Africa throughout a world map with homogeneous landmasses replicated to very large extent the complex patterns observed in real human populations, suggesting a more continuous (as opposed to structured) view of the distribution of modern human genetic diversity, which may play a better role as a base model for further studies. Second, the postglacial evolution of the European barn owl, with the formation of a remarkable coat-color cline, was inspected with two rounds of simulations: (i) determine the demographic background history and (ii) test the probability of a phenotypic cline, like the one observed in the natural populations, to appear without natural selection. We verified that the modern barn owl population originated from a single Iberian refugium and that they formed their color cline, not due to neutral evolution, but with the necessary participation of selection. The third and last part of this thesis refers to a simulation-only study inspired by the barn owl case above. In this chapter, we showed that selection is, indeed, effective during range expansions and that it leaves a distinguished signature, which can then be used to detect and measure natural selection in range-expanding populations. Résumé (en français) Les simulations fournissent un moyen pratique pour répondre à des questions scientifiques qui seraient inabordable autrement. En génétique des populations, l'étude des processus évolutifs implique souvent la mise en oeuvre de modèles complexes, et les simulations sont un outil particulièrement précieux dans ce domaine. Dans cette thèse, j'ai exploré trois questions en utilisant des simulations spatialement explicites dans un cadre de calculs Bayésiens approximés (approximate Bayesian computation : ABC). Tout d'abord, l'histoire de la colonisation humaine mondiale et de l'évolution de parties neutres du génome a été étudiée grâce à un modèle étonnement simple. Un processus de diffusion des migrants de l'Afrique orientale à travers un monde avec des masses terrestres homogènes a reproduit, dans une très large mesure, les signatures génétiques complexes observées dans les populations humaines réelles. Un tel modèle continu (opposé à un modèle structuré en populations) pourrait être très utile comme modèle de base dans l'étude de génétique humaine à l'avenir. Deuxièmement, l'évolution postglaciaire d'un gradient de couleur chez l'Effraie des clocher (Tyto alba) Européenne, a été examiné avec deux séries de simulations pour : (i) déterminer l'histoire démographique de base et (ii) tester la probabilité qu'un gradient phénotypique, tel qu'observé dans les populations naturelles puisse apparaître sans sélection naturelle. Nous avons montré que la population actuelle des chouettes est sortie d'un unique refuge ibérique et que le gradient de couleur ne peux pas s'être formé de manière neutre (sans l'action de la sélection naturelle). La troisième partie de cette thèse se réfère à une étude par simulations inspirée par l'étude de l'Effraie. Dans ce dernier chapitre, nous avons montré que la sélection est, en effet, aussi efficace dans les cas d'expansion d'aire de distribution et qu'elle laisse une signature unique, qui peut être utilisée pour la détecter et estimer sa force.

Evolutionary ecology of learning: insights from fruit flies

Ecologically and evolutionarily oriented research on learning has traditionally been carried out on vertebrates and bees. While less sophisticated than those animals, fruit flies (Drosophila) are capable of several forms of learning, and have an advantage of a short generation time, which makes them an ideal system for experimental evolution studies. This review summarizes the insights into evolutionary questions about learning gained in the last decade from evolutionary experiments on Drosophila. These experiments demonstrate that Drosophila have the genetic potential to evolve substantially improved learning performance in ecologically relevant learning tasks. In at least one set of selected populations the improved learning generalized to another task than that used to impose selection, involving a different behavior, different stimuli, and a different sensory channel for the aversive reinforcement. This improvement in learning ability was associated with reduction in other fitness-related traits, such as larval competitive ability and lifespan, pointing out to evolutionary trade-offs of improved learning. These trade-offs were confirmed by other evolutionary experiments where reduction in learning performance was observed as a correlated response to selection for tolerance to larval nutritional stress or for delayed aging. Such trade-offs could be one reason why fruit flies have not fully used up their evolutionary potential for learning ability. Finally, another evolutionary experiment with Drosophila provided the first direct evidence for the long-standing ideas that learning can under some circumstances accelerate and in other slow down genetically-based evolutionary change. These results demonstrate the usefulness of fruit flies as a model system to address evolutionary questions about learning.

Nest-box design for the study of diurnal raptors and owls is still an overlooked point in ecological, evolutionary and conservation studies: a review

The use of artificial nest-boxes has led to significant progress in bird conservation and in our understanding of the functional and evolutionary ecology of free-ranging birds that exploit cavities for roosting and reproduction. Nest-boxes and their improved accessibility have made it easier to perform comparative and experimental field investigations. However, concerns about the generality and applicability of scientific studies involving birds breeding in nest-boxes have been raised because the occupants of boxes may differ from conspecifics occupying other nest sites. Here we review the existing evidence demonstrating the importance of nest-box design to individual life-history traits in three falcon (Falconiformes) and seven owl (Strigiformes) species, as well as the extent to which publications on these birds describe the characteristics of exploited artificial nest-boxes in their 'methods' sections. More than 60% of recent publications did not provide any details on nest-box design (e.g. size, shape, material), despite several calls >15 years ago to increase the reporting of such information. We exemplify and discuss how variation in nest-box characteristics can affect or confound conclusions from nest-box studies and conclude that it is of overall importance to present details of nest-box characteristics in scientific publications.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

New insights into the long-term evolutionary history of the avian MHC

SummaryGene duplication and neofunctidnalization are important processes in the evolution of phenotypic complexity. They account for important evolutionary novelties that confer ecological adaptation, such as the major histocompatibility complex (MHC), a multigene family with a central role in vertebrates' adaptive immune system. Multigene families, which evolved in large part through duplication, represent promising systems to study the still strongly depbated relative roles of neutral and adaptive processes in the evolution of phenotypic complexity. Detailed knowledge on ecological function and a well-characterized evolutionary history place the mammals' MHC amongst ideal study systems. However mammalian MHCs usually encompass several million base pairs and hold a large number of functional and non-functional duplicate genes, which makes their study complex. Avian MHCs on the other hand are usually way more compact, but the reconstruction of. their evolutionary history has proven notoriously difficult. However, no focused attempt has been undertaken so far to study the avian MHC evolutionary history in a broad phylogenetic context and using adequate gene regions.In the present PhD, we were able to make important contributions to the understanding of the long-term evolution of the avian MHC class II Β (MHCI1B). First, we isolated and characterized MHCIIB genes in barn owl (Tyto alba?, Strigiformes, Tytonidae), a species from an avian lineage in which MHC has not been studied so far. Our results revealed that with only two functional MHCIIB genes the MHC organization of barn owl may be similar to the 'minimal essential' MHC of chicken (Gallus gallus), indicating that simple MHC organization may be ancestral to birds. Taking advantage of the sequence information from barn owl, we studied the evolution of MHCIIB genes in 13 additional species of 'typical' owls (Strigiformes, Strigidae). Phylogenetic analyses revealed that according to their function, in owls the peptide-binding region (PBR) encoding exon 2 and the non-PBR encoding exon 3 evolve by different patterns. Exon 2 exhibited an evolutionary history of positive selection and recombination, while exon 3 traced duplication history and revealed two paralogs evolving divergently from each other in owls, and in a shorebird, the great snipe {Gallinago media). The results from exon 3 were the first ever from birds to demonstrate gene orthology in species that diverged tens of millions of years ago, and strongly questioned whether the taxa studied before provided an adequate picture of avian MHC evolution. In a follow-up study, we aimed at explaining a striking pattern revealed by phylogenetic trees analyzing the owl sequences along with MHCIIB sequences from other birds: One owl paralog (termed DAB1) grouped with sequences of passerines and falcons, while the other (DAB2) grouped with wildfowl, penguins and birds of prey. This could be explained by either a duplication event preceding the evolution of these bird orders, or by convergent evolution of similar sequences in a number of orders. With extensive phylogenetic analyses we were able to show, that indeed a duplication event preceeded the major avian radiation -100 my ago, and that following this duplication, the paralogs evolved under positive selection. Furthermore, we showed that the divergently evolving amino acid residues in the MHCIIB-encoded β-chain potentially interact with the MHCI I α-chain, and that molecular coevolution of the interacting residues may have been involved in the divergent evolution of the MHCIIB paralogs.The findings of this PhD are of particular interest to the understanding of the evolutionary history of the avian MHC and, by providing essential information on long-term gene history in the avian MHC, open promising perspectives for advances in the understanding of the evolution of multigene families in general, and for avian MHC organization in particular. Amongst others I discuss the importance of including protein structure in the phylogenetic study of multigene families, and the roles of ecological versus molecular selection pressures. I conclude by providing a population genomic perspective on avian MHC, which may serve as a basis for future research to investigate the relative roles of neutral processes involving effective population size effects and of adaptation in the evolution of avian MHC diversity and organization.RésuméLa duplication de gènes et leur néo-fonctionnalisation sont des processus importants dans l'évolution de la complexité phénotypique. Ils sont impliqués dans l'apparition d'importantes nouveautés évolutives favorisant l'adaptation écologique, comme c'est le cas pour le complexe majeur d'histocompatibilité

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Evaluation of organ-specific peripheral doses after 2-dimensional, 3-dimensional and hybrid intensity modulated radiation therapy for breast cancer based on Monte Carlo and convolution/superposition algorithms: implications for secondary cancer risk assessment.

To make a comprehensive evaluation of organ-specific out-of-field doses using Monte Carlo (MC) simulations for different breast cancer irradiation techniques and to compare results with a commercial treatment planning system (TPS). Three breast radiotherapy techniques using 6MV tangential photon beams were compared: (a) 2DRT (open rectangular fields), (b) 3DCRT (conformal wedged fields), and (c) hybrid IMRT (open conformal+modulated fields). Over 35 organs were contoured in a whole-body CT scan and organ-specific dose distributions were determined with MC and the TPS. Large differences in out-of-field doses were observed between MC and TPS calculations, even for organs close to the target volume such as the heart, the lungs and the contralateral breast (up to 70% difference). MC simulations showed that a large fraction of the out-of-field dose comes from the out-of-field head scatter fluence (>40%) which is not adequately modeled by the TPS. Based on MC simulations, the 3DCRT technique using external wedges yielded significantly higher doses (up to a factor 4-5 in the pelvis) than the 2DRT and the hybrid IMRT techniques which yielded similar out-of-field doses. In sharp contrast to popular belief, the IMRT technique investigated here does not increase the out-of-field dose compared to conventional techniques and may offer the most optimal plan. The 3DCRT technique with external wedges yields the largest out-of-field doses. For accurate out-of-field dose assessment, a commercial TPS should not be used, even for organs near the target volume (contralateral breast, lungs, heart).