An Interactive Tool for the Computational Exploration of Integrodifference Population Models

Mathematical modeling of population dynamics can provide novel insight to the growth and dispersal patterns for a variety of species populations, and has become vital to the preservation of biodiversity on a global-scale. These growth and dispersal stages can be modeled using integrodifference equations that are discrete in time and continuous in space. Previous studies have identified metrics that can determine whether a given species will persist or go extinct under certain model parameters. However, a need for computational tools to compute these metrics has limited the scope and analysis within many of these studies. We aim to create computational tools that facilitate numerical explorations for a number of associated integrodifference equations, allowing modelers to explore results using a selection of models under a robust parameter set

Fast flowing populations are not well mixed

In evolutionary dynamics, well-mixed populations are almost always associated with all-to-all interactions; mathematical models are based on complete graphs. In most cases, these models do not predict fixation probabilities in groups of individuals mixed by flows. We propose an analytical description in the fast-flow limit. This approach is valid for processes with global and local selection, and accurately predicts the suppression of selection as competition becomes more local. It provides a modelling tool for biological or social systems with individuals in motion.Comment: 19 pages, 8 figure

Investigating life-history polymorphism: modelling mites

The thesis presents research on the life-history polymorphism in the mite Sancassania berlesei. Males of this species are andropolymorphic: there are two distinct male phenotypes. One, the fighter, develops a third thickened leg pair, with which it kills off other fighters and males which do not exhibit a third thickened leg pair, the non-fighters. A review of the life-history of S. berlesei is given, focussing on its general biology, diet, dispersal and mating behaviour. This is followed by a review of the andropolymorphism, and the current understanding of the mechanisms underlying it. The major conclusions from the experimental work presented in this thesis are that fighters primarily develop at low population densities; though the proportion of males becoming fighters at any given density may change over time. This change is likely to be due to condition-dependence. Data is presented to illuminate these matters and a model is developed linking fighter development to the costs of being a fighter (in terms of survival) and the benefits of being a fighter (in terms of fecundity). The sex ratio in S. berlesei is 1:1, and there is no evidence of density or frequency-dependent deviations from this. A delay in food supply at maturation delays the time of maximum fecundity of females for about seven days and lowers their overall egg output. Density-dependent effects reduce the overall daily fecundity of females in higher densities. Female survival is affected by density, food present and rearing conditions. Nearly all eggs laid by S. berlesei hatch regardless of the conditions. Eggs laid in very poor conditions hatched even earlier than the average time of between day three and four. At density two, animals do synchronise their frequency, when isolated together from egg stage. Poor conditions reverse female density-dependence from convex to concave with the lowest life expectancy at intermediate densities. The trade-off between survival and fecundity is the likely cause. Amalgamating the results from the previous experiments, the influence of stochastic population dynamics on male strategy was then modelled. The results indicate that the fighter morph development rule is sensitive to the probability of low population densities arising. When low densities occur, there is a selective advantage to being a fighter. With increasing probability of lower densities, becoming a fighter is more feasible. The ESS rule changes, while in a stable high density environment a density-dependent fighter rule is never selected for. This indicates an influence of stochastic population dynamics on life-history evolution. Modelling demographic stochasticity in the fighter rule shows some buffering effect of this form of stochasticity. The fighter morph determination rule is less sensitive to environmental stochasticity with a high frequency of low densities. Using an agent based model with diploid genetics, I show that under high densities a fighter male is less successful at passing on his genes than a non-fighter. At a density of one male, the fighter gains no advantage to developing the fighter phenotype (as he is not competing with other males). In this case, the advantage may arise through future increases in density (such as through immigration or maturation of offspring). The density-dependent fighter development rule is then switched within the model from density-dependent to frequency-dependent, and the model indicates, that even under the frequency-dependent rule a possible ratio of fighters to non-fighters could exist. The system does not reach this state due to condition-dependence in reality. Following on from the findings discussed above, that morph determination has a condition-dependent component, I develop an argument that relates the observed forms of morph determination (density-dependent and frequency-dependent) in three closely related species of mites via an underlying condition-dependence. It is shown that condition-dependence is likely the linking factor between frequency and density-dependence. This is shown to be possibly a rule for all species displaying polymorphism which includes physical alterations of their bodies

Spatial modeling and uncertainty analysis for subsurface feature mapping : integration of geostatistical concepts and image-based machine learning model validation

Spatial modeling of subsurface features and uncertainty analysis plays a pivotal role in the integration of data analytics and machine learning techniques in the petroleum industry. As the energy landscape is always changing, and new technologies are emerging, the demand for accurate assessments of uncertainty to inform high-value decision-making is of utmost importance. Nonetheless, the same longstanding methods are used due to their simplicity and the lack of immediate necessity for change. However, with improvements and the implementation of proper workflows, the current methods for calculating uncertainties and validating machine learning models can be more effectively addressed. We developed multiscale methods for data analytics and machine learning. These approaches integrate geostatistical concepts to enhance the precision and reliability of subsurface modeling techniques. We address the challenge of integrating multiple datasets with varying accuracies and volume support sizes. We emphasize the importance of accounting for different sources of uncertainty in spatial modeling workflows. Leveraging geostatistical concepts, such as semivariograms, and dispersion variance, a novel approach is introduced to calculate a more precise measure of error when imputing smaller scale datasets with larger scale datasets. This refined measure of error allows for the direct integration of these datasets in spatial modeling workflows. Once all the uncertainty in our models is accounted for, we must check if our models are accurate. Therefore, we focus on the validation of machine learning models, particularly those tailored for image data. Image-based models often necessitate pre-processing steps, such as resizing and augmentation, to improve data quality for training. To ensure the performance and suitability of these models for real-world datasets, proper validation techniques are imperative. We propose integrating the concept of minimum acceptance criteria with the multi-scale Structural Similarity Index (MS-SSIM) for improved model checking. This enables a more accurate evaluation of model performance in reproducing original images and predicting new ones, surpassing conventional approaches such as mean squared error (MSE) and single-scale SSIM. Our multiscale approaches for data analytics and machine learning establish a comprehensive framework for addressing uncertainty and validating image-based models. The incorporation of geostatistical principles in calculating uncertainty and proper selection criteria for image-based model validation are showcased on subsurface data; however, they are versatile and applicable across various domains. Ultimately, they contribute to the safe and effective deployment of machine learning models for spatial modeling, advancing the field towards more reliable and informed decision-making.Petroleum and Geosystems Engineerin

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

Segmentation of Polar Mesospheric Summer Echoes using Fully Convolutional Network

Polar Mesospheric Summer Echoes (PMSE) are strong coherent radar echoes that occur in the 80 to 90 km altitude range of the mesosphere during the Arctic summer months. These echoes are of significant interest to the space physics community as they provide insight into changes that occur in the atmosphere. To better understand these changes, large datasets of PMSE echoes need to be analysed. In this study, we aimed to develop a deep learning model that could segment PMSE signal data for analysis on larger EISCAT VHF datasets. For the task, we employed a UNet and a UNet++ architecture and tested how pretrained weights from other source domains perform. Next, different loss functions were tested and last the novel object-level augmentation method ObjectAug was employed with other image-level augmentation methods to increase model performance and reduce potential overfitting due to a small training dataset. The results indicate that using randomly initialized weights was the better option for the PMSE target domain and that the use of different loss functions only had a small impact on model performance. When using image- and object-level augmentation the best performing model was reached. It was also seen that there exist inconsistencies in the PMSE signal groundtruth labels. Dividing the inconsistencies into two categories: Granular and Coarse, it was seen that using object-level augmentation had a significantly higher performance on the Granular labelled PMSE signal samples. Overall, our study indicates that the best performing model can be used to segment PMSE for larger datasets or as a supportive tool for further labelling of PMSE signal data

REMOTE SENSING DATA ANALYSIS FOR ENVIRONMENTAL AND HUMANITARIAN PURPOSES. The automation of information extraction from free satellite data.

This work is aimed at investigating technical possibilities to provide information on environmental parameters that can be used for risk management. The World food Program (WFP) is the United Nations Agency which is involved in risk management for fighting hunger in least-developed and low-income countries, where victims of natural and manmade disasters, refugees, displaced people and the hungry poor suffer from severe food shortages. Risk management includes three different phases (pre-disaster, response and post disaster) to be managed through different activities and actions. Pre disaster activities are meant to develop and deliver risk assessment, establish prevention actions and prepare the operative structures for managing an eventual emergency or disaster. In response and post disaster phase actions planned in the pre-disaster phase are executed focusing on saving lives and secondly, on social economic recovery. In order to optimally manage its operations in the response and post disaster phases, WFP needs to know, in order to estimate the impact an event will have on future food security as soon as possible, the areas affected by the natural disaster, the number of affected people, and the effects that the event can cause to vegetation. For this, providing easy-to-consult thematic maps about the affected areas and population, with adequate spatial resolution, time frequency and regular updating can result determining. Satellite remote sensed data have increasingly been used in the last decades in order to provide updated information about land surface with an acceptable time frequency. Furthermore, satellite images can be managed by automatic procedures in order to extract synthetic information about the ground condition in a very short time and can be easily shared in the web. The work of thesis, focused on the analysis and processing of satellite data, was carried out in cooperation with the association ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action), a center of research which works in cooperation with the WFP in order to provide IT products and tools for the management of food emergencies caused by natural disasters. These products should be able to facilitate the forecasting of the effects of catastrophic events, the estimation of the extension and location of the areas hit by the event, of the affected population and thereby the planning of interventions on the area that could be affected by food insecurity. The requested features of the instruments are: • Regular updating • Spatial resolution suitable for a synoptic analysis • Low cost • Easy consultation Ithaca is developing different activities to provide georeferenced thematic data to WFP users, such a spatial data infrastructure for storing, querying and manipulating large amounts of global geographic information, and for sharing it between a large and differentiated community; a system of early warning for floods, a drought monitoring tool, procedures for rapid mapping in the response phase in a case of natural disaster, web GIS tools to distribute and share georeferenced information, that can be consulted only by means of a web browser. The work of thesis is aimed at providing applications for the automatic production of base georeferenced thematic data, by using free global satellite data, which have characteristics suitable for analysis at a regional scale. In particular the main themes of the applications are water bodies and vegetation phenology. The first application aims at providing procedures for the automatic extraction of water bodies and will lead to the creation and update of an historical archive, which can be analyzed in order to catch the seasonality of water bodies and delineate scenarios of historical flooded areas. The automatic extraction of phenological parameters from satellite data will allow to integrate the existing drought monitoring system with information on vegetation seasonality and to provide further information for the evaluation of food insecurity in the post disaster phase. In the thesis are described the activities carried on for the development of procedures for the automatic processing of free satellite data in order to produce customized layers according to the exigencies in format and distribution of the final users. The main activities, which focused on the development of an automated procedure for the extraction of flooded areas, include the research of an algorithm for the classification of water bodies from satellite data, an important theme in the field of management of the emergencies due to flood events. Two main technologies are generally used: active sensors (radar) and passive sensors (optical data). Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season, while passive sensors can only be used in the daytime cloud free conditions. Even if with radar technologies is possible to get information on the ground in all weather conditions, it is not possible to use radar data to obtain a continuous archive of flooded areas, because of the lack of a predetermined frequency in the acquisition of the images. For this reason the choice of the dataset went in favor of MODIS (Moderate Resolution Imaging Spectroradiometer), optical data with a daily frequency, a spatial resolution of 250 meters and an historical archive of 10 years. The presence of cloud coverage prevents from the acquisition of the earth surface, and the shadows due to clouds can be wrongly classified as water bodies because of the spectral response very similar to the one of water. After an analysis of the state of the art of the algorithms of automated classification of water bodies in images derived from optical sensors, the author developed an algorithm that allows to classify the data of reflectivity and to temporally composite them in order to obtain flooded areas scenarios for each event. This procedure was tested in the Bangladesh areas, providing encouraging classification accuracies. For the vegetation theme, the main activities performed, here described, include the review of the existing methodologies for phenological studies and the automation of the data flow between inputs and outputs with the use of different global free satellite datasets. In literature, many studies demonstrated the utility of the NDVI (Normalized Difference Vegetation Index) indices for the monitoring of vegetation dynamics, in the study of cultivations, and for the survey of the vegetation water stress. The author developed a procedure for creating layers of phenological parameters which integrates the TIMESAT software, produced by Lars Eklundh and Per Jönsson, for processing NDVI indices derived from different satellite sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer) AND SPOT (Système Pour l'Observation de la Terre) VEGETATION. The automated procedure starts from data downloading, calls in a batch mode the software and provides customized layers of phenological parameters such as the starting of the season or length of the season and many others