Bayesian Optimisation for Planning in Dynamic Environments

This thesis addresses the problem of trajectory planning for monitoring extreme values of an environmental phenomenon that changes in space and time. The most relevant case study corresponds to environmental monitoring using an autonomous mobile robot for air, water and land pollution monitoring. Since the dynamics of the phenomenon are initially unknown, the planning algorithm needs to satisfy two objectives simultaneously: 1) Learn and predict spatial-temporal patterns and, 2) find areas of interest (e.g. high pollution), addressing the exploration-exploitation trade-off. Consequently, the thesis brings the following contributions: Firstly, it applies and formulates Bayesian Optimisation (BO) to planning in robotics. By maintaining a Gaussian Process (GP) model of the environmental phenomenon the planning algorithms are able to learn the spatial and temporal patterns. A new family of acquisition functions which consider the position of the robot is proposed, allowing an efficient trajectory planning. Secondly, BO is generalised for optimisation over continuous paths, not only determining where and when to sample, but also how to get there. Under these new circumstances, the optimisation of the acquisition function for each iteration of the BO algorithm becomes costly, thus a second layer of BO is included in order to effectively reduce the number of iterations. Finally, this thesis presents Sequential Bayesian Optimisation (SBO), which is a generalisation of the plain BO algorithm with the goal of achieving non-myopic trajectory planning. SBO is formulated under a Partially Observable Markov Decision Process (POMDP) framework, which can find the optimal decision for a sequence of actions with their respective outcomes. An online solution of the POMDP based on Monte Carlo Tree Search (MCTS) allows an efficient search of the optimal action for multistep lookahead. The proposed planning algorithms are evaluated under different scenarios. Experiments on large scale ozone pollution monitoring and indoor light intensity monitoring are conducted for simulated and real robots. The results show the advantages of planning over continuous paths and also demonstrate the benefit of deeper search strategies using SBO

Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions

This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID19 for each state in the United States. The IHME models have received extensive attention in social and mass media, and have influenced policy makers at the highest levels of the United States government. For effective policy making the accurate assessment of uncertainty, as well as accurate point predictions, are necessary because the risks inherent in a decision must be taken into account, especially in the present setting of a novel disease affecting millions of lives. To assess the accuracy of the IHME models, we examine both forecast accuracy as well as the predictive performance of the 95% prediction intervals provided by the IHME models. We find that the initial IHME model underestimates the uncertainty surrounding the number of daily deaths substantially. Specifically, the true number of next day deaths fell outside the IHME prediction intervals as much as 70% of the time, in comparison to the expected value of 5%. In addition, we note that the performance of the initial model does not improve with shorter forecast horizons. Regarding the updated models, our analyses indicate that the later models do not show any improvement in the accuracy of the point estimate predictions. In fact, there is some evidence that this accuracy has actually decreased over the initial models. Moreover, when considering the updated models, while we observe a larger percentage of states having actual values lying inside the 95% prediction intervals (PI), our analysis suggests that this observation may be attributed to the widening of the PIs. The width of these intervals calls into question the usefulness of the predictions to drive policy making and resource allocation

Predicting the HMA-LMA status in marine sponges by machine learning

The dichotomy between high microbial abundance (HMA) and low microbial abundance (LMA) sponges has been observed in sponge-microbe symbiosis, although the extent of this pattern remains poorly unknown. We characterized the differences between the microbiomes of HMA (n=19) and LMA (n=17) sponges (575 specimens) present in the Sponge Microbiome Project. HMA sponges were associated with richer and more diverse microbiomes than LMA sponges, as indicated by the comparison of alpha diversity metrics. Microbial community structures differed between HMA and LMA sponges considering Operational Taxonomic Units (OTU) abundances and across microbial taxonomic levels, from phylum to species. The largest proportion of microbiome variation was explained by the host identity. Several phyla, classes, and OTUs were found differentially abundant in either group, which were considered “HMA indicators” and “LMA indicators”. Machine learning algorithms (classifiers) were trained to predict the HMA-LMA status of sponges. Among nine different classifiers, higher performances were achieved by Random Forest trained with phylum and class abundances. Random Forest with optimized parameters predicted the HMA-LMA status of additional 135 sponge species (1,232 specimens) without a priori knowledge. These sponges were grouped in four clusters, from which the largest two were composed of species consistently predicted as HMA (n=44) and LMA (n=74). In summary, our analyses shown distinct features of the microbial communities associated with HMA and LMA sponges. The prediction of the HMA-LMA status based on the microbiome profiles of sponges demonstrates the application of machine learning to explore patterns of host-associated microbial communities

A systematic review of participatory scenario planning to envision mountain social-ecological systems futures

Mountain social-ecological systems (MtSES) provide crucial ecosystem services to over half of humanity. However, populations living in these highly varied regions are now confronted by global change. It is critical that they are able to anticipate change to strategically manage resources and avoid potential conflict. Yet, planning for sustainable, equitable transitions for the future is a daunting task, considering the range of uncertainties and the unique character of MtSES. Participatory scenario planning (PSP) can help MtSES communities by critically reflecting on a wider array of innovative pathways for adaptive transformation. Although the design of effective approaches has been widely discussed, how PSP has been employed in MtSES has yet to be examined. Here, we present the first systematic global review of single- and multiscalar, multisectoral PSP undertaken in MtSES, in which we characterize the process, identify strengths and gaps, and suggest effective ways to apply PSP in MtSES. We used a nine-step process to help guide the analysis of 42 studies from 1989 screened articles. Our results indicate a steady increase in relevant studies since 2006, with 43% published between 2015 and 2017. These studies encompass 39 countries, with over 50% in Europe. PSP in MtSES is used predominantly to build cooperation, social learning, collaboration, and decision support, yet meeting these objectives is hindered by insufficient engagement with intended end users. MtSES PSP has focused largely on envisioning themes of governance, economy, land use change, and biodiversity, but has overlooked themes such as gender equality, public health, and sanitation. There are many avenues to expand and improve PSP in MtSES: to other regions, sectors, across a greater diversity of stakeholders, and with a specific focus on MtSES paradoxes. Communicating uncertainty, monitoring and evaluating impacts, and engendering more comparative approaches can further increase the utility of PSP for addressing MtSES challenges, with lessons for other complex social-ecological systems

A systematic review of participatory scenario planning to envision mountain social-ecological systems futures

Mountain social-ecological systems (MtSES) provide crucial ecosystem services to over half of humanity. However, populations living in these highly varied regions are now confronted by global change. It is critical that they are able to anticipate change to strategically manage resources and avoid potential conflict. Yet, planning for sustainable, equitable transitions for the future is a daunting task, considering the range of uncertainties and the unique character of MtSES. Participatory scenario planning (PSP) can help MtSES communities by critically reflecting on a wider array of innovative pathways for adaptive transformation. Although the design of effective approaches has been widely discussed, how PSP has been employed in MtSES has yet to be examined. Here, we present the first systematic global review of single- and multiscalar, multisectoral PSP undertaken in MtSES, in which we characterize the process, identify strengths and gaps, and suggest effective ways to apply PSP in MtSES. We used a nine-step process to help guide the analysis of 42 studies from 1989 screened articles. Our results indicate a steady increase in relevant studies since 2006, with 43% published between 2015 and 2017. These studies encompass 39 countries, with over 50% in Europe. PSP in MtSES is used predominantly to build cooperation, social learning, collaboration, and decision support, yet meeting these objectives is hindered by insufficient engagement with intended end users. MtSES PSP has focused largely on envisioning themes of governance, economy, land use change, and biodiversity, but has overlooked themes such as gender equality, public health, and sanitation. There are many avenues to expand and improve PSP in MtSES: to other regions, sectors, across a greater diversity of stakeholders, and with a specific focus on MtSES paradoxes. Communicating uncertainty, monitoring and evaluating impacts, and engendering more comparative approaches can further increase the utility of PSP for addressing MtSES challenges, with lessons for other complex social-ecological systems. © 2020 by the author(s)

Auxin Response Factor2 (ARF2) and Its Regulated Homeodomain Gene HB33 Mediate Abscisic Acid Response in Arabidopsis

The phytohormone abscisic acid (ABA) is an important regulator of plant development and response to environmental stresses. In this study, we identified two ABA overly sensitive mutant alleles in a gene encoding Auxin Response Factor2 (ARF2). The expression of ARF2 was induced by ABA treatment. The arf2 mutants showed enhanced ABA sensitivity in seed germination and primary root growth. In contrast, the primary root growth and seed germination of transgenic plants over-expressing ARF2 are less inhibited by ABA than that of the wild type. ARF2 negatively regulates the expression of a homeodomain gene HB33, the expression of which is reduced by ABA. Transgenic plants over-expressing HB33 are more sensitive, while transgenic plants reducing HB33 by RNAi are more resistant to ABA in the seed germination and primary root growth than the wild type. ABA treatment altered auxin distribution in the primary root tips and made the relative, but not absolute, auxin accumulation or auxin signal around quiescent centre cells and their surrounding columella stem cells to other cells stronger in arf2-101 than in the wild type. These results indicate that ARF2 and HB33 are novel regulators in the ABA signal pathway, which has crosstalk with auxin signal pathway in regulating plant growth