Tracking Uncertainty Propagation from Model to Formalization: Illustration on Trust Assessment

International audienceThis paper investigates the use of the URREF ontology to characterize and track uncertainties arising within the modeling and formalization phases. Estimation of trust in reported information, a real-world problem of interest to practitioners in the field of security, was adopted for illustration purposes. A functional model of trust was developed to describe the analysis of reported information, and it was implemented with belief functions. When assessing trust in reported information, the uncertainty arises not only from the quality of sources or information content, but also due to the inability of models to capture the complex chain of interactions leading to the final outcome and to constraints imposed by the representation formalism. A primary goal of this work is to separate known approximations, imperfections and inaccuracies from potential errors, while explicitly tracking the uncertainty from the modeling to the formalization phases. A secondary goal is to illustrate how criteria of the URREF ontology can offer a basis for analyzing performances of fusion systems at early stages, ahead of implementation. Ideally, since uncertainty analysis runs dynamically, it can use the existence or absence of observed states and processes inducing uncertainty to adjust the tradeoff between precision and performance of systems on-the-fly

Geomorfología glaciar del flanco noroeste del volcán Hectes Tholus, Marte

Tesis Doctoral de la Universidad Complutense de Madrid, Facultad de Ciencias Geológicas, Departamento de Geodinámica, leída el 05-11-2015El volcán Hecates Tholus (32.18°N, 150.28ºE; cuadrante MC-7), de unos 180 km de diámetro y 5.300 metros de altura, es el único edificio de la provincia volcánica de Elysium, en las Tierras Bajas de Marte, en el que se han descrito rasgos geomorfológicos que podrían estar causados por procesos glaciares. Además, distintos autores relacionan la red radial de canales que surcan las laderas del volcán como causadas por la fusión de un antiguo casquete glaciar en la cima del edificio, siendo éste un ejemplo más de las intensas interacciones magma-agua en esta región del planeta, cercana al antiguo océano marciano y que dieron lugar a fenómenos muy interesantes, como los terrenos caóticos de Galaxias Chaos, a pocos kilómetros del volcán. Una característica muy particular de este edificio volcánico es la presencia de dos depresiones anidadas en la base de la ladera Noroeste, de 20 y 60 km de diámetro. La menor de ellas (Depresión A), situada a mayor altitud, ha sido interpretada por algunos autores como causada por una erupción lateral del volcán hace unos 350 Ma. Sin embargo, la de mayor diámetro y situada a menor altitud (Depresión B), no tiene un origen claro, aunque se han discutido distintas hipótesis. En cualquier caso, es especialmente en el interior de estas depresiones donde se han encontrado los rasgos geomorfológicos que podrían estar causados por actividad glacial, como posibles cordones morrénicos y depósitos de till...Hecates Tholus volcano (32.18°N, 150.28ºE; MC-7 quadrangle) is the only edifice of the Elysium volcanic province, at the lowlands of Mars, showing evidence of glacial activity, as deduced from the geomorphological study. This work completes the previous regional works with the aim of refine our knowledge about the glacial events occurred at this site of Mars. We build a detailed geomorphological mapping (1:100.000 in scale) of the lower NW flank of the edifice (31.8º-33.08ºN, 148.37º-149.38ºE), where the glacial ”marsforms” concentrate, based on the use of CTX images. Moreover, we performed detailed crater size-frequency distribution, geomorphological, morphometric, compositional, and thermal analysis to finish the cartography and get the necessary evidences to model the glacial evolution of the area. Those analyses were possible thanks to the use of a wide variety of images, including HRSC, HiRISE, MOC, and THEMIS, as well as HRSC-derived topographic data, THEMIS-derived Brightness surface temperature, TES-derived thermal inertia maps, and SHARAD ground penetration radargrams, everything integrated into a Geographic Information System...Depto. de Geodinámica, Estratigrafía y PaleontologíaFac. de Ciencias GeológicasTRUEunpu

A multi-scale approach to assessing the spatio-temporal variability of seasonal snow in the Clutha Catchment, New Zealand

Seasonal snow is an important, but under-observed component of New Zealand's hydrological cycle. Measurement and characterisation of seasonal snow is complicated because it varies over a range of spatial and temporal scales. This makes spatially distributed in situ observations difficult to acquire, and limits efforts to scale point-based observations up to larger areas. Sparse observations of seasonal snow lead to reduced understanding of seasonal snow processes, and subsequent uncertainty in efforts to model seasonal snow. This thesis addresses these issues within the Clutha Catchment, New Zealand's largest, by leveraging remote sensing and geospatial approaches to map and characterise seasonal snow both regionally, and at very high spatial resolution over a small alpine basin. A daily snow covered area (SCA) time series and regional scale snow cover climatology is derived from MODIS imagery for the period 20002016. Metrics including annual snow cover duration (SCD) anomaly and daily SCA and snowline elevation (SLE) were derived and assessed for temporal trends. On average, SCA peaks in late June (~30 % of the catchment area), with 10 % of the catchment area sustaining snow cover for > 120 d yr -1. A persistent mid-winter reduction in SCA is attributed to the prevalence of winter blocking anticyclones in the New Zealand region. No significant decrease in SCD occurred over the period 20002016, but substantial spatial and temporal variability was observed. Raster principal component analysis (rPCA) identified distinct modes of spatial variability within the time series. Spatio-temporal variability extends beyond that associated with topographic controls, which can result in out of phase snow cover conditions across the catchment. Specific spatial modes of SCD are associated with anomalous airflow from the NE, E and SE. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. In order to resolve sub-MODIS scale processes, the potential of remotely piloted aircraft system (RPAS) photogrammetry to map snow depth was evaluated within an alpine catchment of the Pisa Range. Differencing between snow-covered and snow-free digital surface models (DSMs) acquired during 2016 provided high resolution snow depth maps. The accuracy of snow depth maps was thoroughly assessed with in situ snow probe measurements, and by analysing residuals for snow-free areas between DSMs. This accuracy assessment demonstrated repeatability and revealed substantial departures of errors from a normal distribution. This reflects the influence of DSM co-registration and terrain characteristics on vertical uncertainty. Error propagation provided lower uncertainties for snow depth (±0.08 m, 90 % c.l.) than the characterization of uncertainties on snow-free areas (±0.14 m). Comparisons between RPAS and in situ snow depth measurements confirm this level of performance. Semivariogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine-scale spatial variability. Following the successful evaluation of RPAS photogrammetry for mapping snow depth, further snow depth maps were acquired for 2017. A total of six snow depth maps that resolved fine scale spatial variability in snow distribution facilitated the assessment of topographic controls on snow depth and snow water equivalent (SWE) distribution. Topographic controls were assessed via regression tree analysis between snow depth and terrain indices, including the kernel density of tussock vegetation (KDtussock), elevation (ELEV), the topographic position index (TPI), a Shade index (Shade), and Sx (maximum upwind slope). Despite substantial differences in both total snow volume and spatial distribution, the range of spatial-autocorrelation for snow depth was comparable for both winters at 20 – 30 m. Regression tree modelling reproduced some of the observed spatial structure, and demonstrated temporal variability in the relative importance of controlling parameters. The impact of varying wind regimes on the spatial distribution of snow was highlighted. These findings illustrate the complexity of atmospheric controls on SCD within the Clutha Catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological catchment (∼0.4 km2) at very high resolution. Snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. This thesis demonstrates the utility of mapping snow at differing spatial scales for improved understanding of seasonal snow processes and highlights the need to robustly capture dynamic processes in spatial snow models

Distribution and characterization of bacterial communities in diverse Antarctic ecosystems by high-troughput sequencing

Geological events and historical climate changes have eliminated or reduced most life in Antarctica to mainly microbial organisms in relatively simple communities. Due to its exceptional location, millennia long isolations and extreme climatic conditions, the continent offers a spectacular and unique background for fundamental scientific research and the testing of hypotheses. Notwithstanding the fact that Antarctica is still considered by many to be one of the last pristine environments on Earth, it is not only threatened by climate change, which particularly has severe effects on parts of West and Maritime Antarctica, but also by an ever increasing number of tourists and even scientists themselves. Studies on Antarctic biota are relatively scarce, and despite the fact that bacteria are fundamental to the Antarctic ecosystems, only a minority of the studies focus on these organisms. This results in a lacuna in the knowledge about the diversity, distribution and functioning of and the relationships between these organisms under the extreme Antarctic conditions. The recent advent of High-throughput sequencing (HTS) applications enables to sequence millions of DNA-fragments in a very short time, allowing us to visualise bacterial communities at a very high resolution, without the necessity for prior isolation of the organisms. In this PhD-study, we have applied some of these new technologies in order to investigate the bacterial diversity of different habitats throughout the Antarctic. In a first study (Chapter 2), we have compared the results obtained by pyrosequencing and compared these with the results of a previous isolation campaign. As expected, a much larger diversity of bacteria were found with pyrosequencing. While five bacterial phyla were recovered by cultivation, this was the case for 22 phyla with the NGS-approach, and a large amount of unknown diversity was evident. At the same time, it became clear that also the part of the 16S rRNA gene that was sequenced had an impact on the perceived diversity, with the V1-V2 fragments resulting in ~50% more OTUs than the V3-V2 fragments and only a limited amount of overlap in the genera recovered was noticed. In contrast, more chimeric sequences were identified in the V3-V2 amplicons. Notwithstanding the fact that pyrosequencing yielded a higher diversity, there was very little overlap with the cultivation approach, with only about 4 % of the OTUs recovered by cultivation found with pyrosequencing. In contrast, we also noticed that some singleton pyrosequencing OTUs where easily grown on growth media, and hence were not errors in the pyrosequencing data. This study thus showed that several factors could have a large impact on the perceived diversity, and that complementary techniques are necessary to discover the total bacterial diversity. In a second study (Chapter 3), we have examined the effects of both different bedrock types (granite and gneiss) and the presence of macrobiota (mosses, lichens and algae) on the composition of bacterial communities in high-altitude inland soils of different regions if the western Sør Rondane Mountains (Queen Maud Land, East Antarctica), near the Belgian Princess Elisabeth research station. We have used the at present most used HTS-platform, Illumina’s MiSeq, which allows sequencing longer gene fragments and yields more sequences compared to pyrosequencing. We combined this with the ARISA genetic fingerprinting technique. We demonstrated that organic carbon was the most significant parameter in structuring bacterial communities, followed by pH, electric conductivity, bedrock type and moisture content, while spatial distance was of less importance. Diversity showed a positive correlation with moisture content. Acidobacteria and Actinobacteria dominated dry gneiss derived mineral soils, while Proteobacteria, Cyanobacteria, Armatimonadetes and candidate phylum FBP were dominant in samples with a high organic carbon content. A large part of the unexplained variation is probably caused by the absence of data about important nutrients in our dataset (nitrogen and phosphorous), together with microclimatic and topographic differences between sample locations, and noise and stochasticity. In a last study we again used the Illumina MiSeq platform to perform a pan-continental charting of benthic and littoral microbial mats. In total, 138 samples from lakes in eight Antarctic regions and two Sub-Antarctic islands were analysed. We found a significant trend of increasing biodiversity with decreasing latitude from 85° to 54° S, although this than again decreased until 45° S. The mean annual temperature appeared to have a highly significant effect on community structure between Sub-Antarctica and Antarctica, while, besides the geographical distance, electric conductivity, and to a lesser extent pH, was important in explaining differences between samples on the Antarctic continent. In this study, too, a very high unknown diversity was observed. Particularly Cyanobacteria and Alphaproteobacteria dominated freshwater microbial mats, while Bacteroidetes and the alphaproteobacterial Rhodobacteraceae family dominated saline lakes. The Sub-Antarctic Marion Island was highly deviant with very low species richness, dominated by Janthinobacterium (Betaproteobacteria). In conclusion this thesis supports the hypothesis that for Bacteria in the Antarctic Region, too, distinct biogeographic patterns exist and that the environment exerts large selective pressures on community structure and composition, complemented by biotic factors. There is a high amount of heterogeneity at both local and continental scale due to both spatial distance and local differences in environmental variables such as electric conductivity, pH, moisture content, organic carbon and microclimate. Although we only were able sample a fraction of the continent, it is expected that similar patterns hold across the entire continent. However, additional sampling and in depth (metagenomics) sequencing linked to extensive environmental data, combined with phylogenetic analysis is needed to resolve important questions such as within and inter-continental dispersal, functioning and correlation of observed patterns to environmental data

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

New Global Perspectives on Archaeological Prospection

This volume is a product of the 13th International Conference on Archaeological Prospection 2019, which was hosted by the Department of Environmental Science in the Faculty of Science at the Institute of Technology Sligo. The conference is held every two years under the banner of the International Society for Archaeological Prospection and this was the first time that the conference was held in Ireland. New Global Perspectives on Archaeological Prospection draws together over 90 papers addressing archaeological prospection techniques, methodologies and case studies from 33 countries across Africa, Asia, Australasia, Europe and North America, reflecting current and global trends in archaeological prospection. At this particular ICAP meeting, specific consideration was given to the development and use of archaeological prospection in Ireland, archaeological feedback for the prospector, applications of prospection technology in the urban environment and the use of legacy data. Papers include novel research areas such as magnetometry near the equator, drone-mounted radar, microgravity assessment of tombs, marine electrical resistivity tomography, convolutional neural networks, data processing, automated interpretive workflows and modelling as well as recent improvements in remote sensing, multispectral imaging and visualisation

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.https://digitalcommons.unomaha.edu/isqafacbooks/1000/thumbnail.jp

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science