Discussing landscape compositional scenarios generated with maximization of non-expected utility decision models based on weighted entropies

Concept PaperThe search for hypothetical optimal solutions of landscape composition is a major issue in landscape planning and it can be outlined in a two-dimensional decision space involving economic value and landscape diversity, the latter being considered as a potential safeguard to the provision of services and externalities not accounted in the economic value. In this paper, we use decision models with different utility valuations combined with weighted entropies respectively incorporating rarity factors associated to Gini-Simpson and Shannon measures. A small example of this framework is provided and discussed for landscape compositional scenarios in the region of Nisa, Portugal. The optimal solutions relative to the different cases considered are assessed in the two-dimensional decision space using a benchmark indicator. The results indicate that the likely best combination is achieved by the solution using Shannon weighted entropy and a square root utility function, corresponding to a risk-averse behavior associated to the precautionary principle linked to safeguarding landscape diversity, anchoring for ecosystem services provision and other externalities. Further developments are suggested, mainly those relative to the hypothesis that the decision models here outlined could be used to revisit the stability-complexity debate in the field of ecological studiesinfo:eu-repo/semantics/publishedVersio

Biodiversity Conservation and Phylogenetic Systematics: Preserving our evolutionary heritage in an extinction crisis

Biodiversity; Nature conservatio

Exponential Family Predictive Representations of State.

Many agent-environment interactions can be framed as dynamical systems in which agents take actions and receive observations. These dynamical systems are diverse, representing such things as a biped walking, a stock price changing over time, the trajectory of a missile, or the shifting fish population in a lake. Often, interacting successfully with the environment requires the use of a model, which allows the agent to predict something about the future by summarizing the past. Two of the basic problems in modeling partially observable dynamical systems are selecting a representation of state and selecting a mechanism for maintaining that state. This thesis explores both problems from a learning perspective: we are interested in learning a predictive model directly from the data that arises as an agent interacts with its environment. This thesis develops models for dynamical systems which represent state as a set of statistics about the short-term future, as opposed to treating state as a latent, unobservable quantity. In other words, the agent summarizes the past into predictions about the short-term future, which allow the agent to make further predictions about the infinite future. Because all parameters in the model are defined using only observable quantities, the learning algorithms for such models are often straightforward and have attractive theoretical properties. We examine in depth the case where state is represented as the parameters of an exponential family distribution over a short-term window of future observations. We unify a number of different existing models under this umbrella, and predict and analyze new models derived from the generalization. One goal of this research is to push models with predictively defined state towards real-world applications. We contribute models and companion learning algorithms for domains with partial observability, continuous observations, structured observations, high-dimensional observations, and/or continuous actions. Our models successfully capture standard POMDPs and benchmark nonlinear timeseries problems with performance comparable to state-of-the-art models. They also allow us to perform well on novel domains which are larger than those captured by other models with predictively defined state, including traffic prediction problems and domains analogous to autonomous mobile robots with camera sensors.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58522/1/wingated_1.pd

Piospheres in semi-arid rangeland: Consequences of spatially constrained plant-herbivore interactions

This thesis explains two aspects of animal spatial foraging behaviour arising as a direct consequence of animals' need to drink water: the concentration of animal impacts, and the response of animals to those impacts. In semi-arid rangelands, the foraging range of free-ranging large mammalian herbivores is constrained by the distribution of drinking water during the dry season. Animal impacts become concentrated around these watering sites according to the geometrical relationship between the available foraging area and the distance from water, and the spatial distribution of animal impacts becomes organised along a utilisation gradient termed a "piosphere". During the dry season the temporal distribution of the impacts is determined by the day-to-day foraging behaviour of the animals. The specific conditions under which these spatial foraging processes determine the piosphere pattern have been identified in this thesis. At the core of this investigation are questions about the response of animals to the heterogeneity of their resources. Aspects of spatial foraging are widely commented on whilst explaining the consequences of piosphere phenomena for individual animal intake, population dynamics, feeding strategies and management. Implicated are our notions of optimal foraging, scale in animal response, and resource matching. This thesis addressed each of these. In the specific context of piospheres, the role of energy balance in optimal foraging was also tested. Field experiments for this thesis showed a relationship between goat browsing activity and measures of spatial impact. As a preliminary step to investigating animal response to resource heterogeneity, the spatial pattern of foraging behaviour/impacts was described using spatial statistics. Browsing activity varied daily revealing animal assessment of the spatial heterogeneity of their resources and an energetic basis for foraging decisions. This foraging behaviour was shown to be determined by individual plants rather than at larger scales of plant aggregation. A further experiment investigated the claim that defoliation has limited impact on browser intake rate, suggesting that piospheres may have few consequences for browser intake. This experiment identified a constraining influence of browse characteristics at the small scale on goat foraging by relating animal intake rate to plant bite size and distribution. Computer simulation experiments for this thesis supported these empirical findings by showing that the distribution of spatial impacts was sensitive to the marginal value of forage resources, and identified plant bite size and distribution as the causal factors in limiting animal intake rate in the presence of a piosphere. As a further description of spatial pattern, piospheres were characterised by applying a contemporary ecological theory that ranks resource patches into a spatial hierarchy. Ecosystem dynamics emerge from the interactions between these patches, with piospheres being an emergent property of a natural plant-herbivore system under specific conditions of constrained foraging. The generation of a piosphere was shown to be a function of intake constraints and available foraging area, whilst piosphere extent was shown to be independent of daily energy balance including expenditure on travel costs. A threshold distance for animal foraging range arising from a hypothesised conflict between daily energy intake and expenditure was shown not to exist, whereas evidence for an intermediate distance from water as a focus for accumulated foraging activity was identified. Individual animal foraging efficiency in the computer model was shown to be sensitive to the piosphere, while animal population dynamics were found to be determined in the longer term by dry season key resources near watering points. Time lags were found to operate in the maintenance of the gradient, and the density dependent moderation of the animal population. The latter was a direct result of the inability of animal populations to match the distribution of their resources with the distribution of their foraging behaviour, because of their daily drinking requirements. The result is that animal forage intake was compromised by the low density of dry season forage in the vicinity of a water point. This thesis also proposes that piospheres exert selection pressures on traits to maximise energy gain from the spatial heterogeneity of dry season resources, and that these have played a role in the evolution of large mammalian herbivores

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

Methodology for technology evaluation under uncertainty and its application in advanced coal gasification processes

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 273-287).Integrated gasification combined cycle (IGCC) technology has attracted interest as a cleaner alternative to conventional coal-fired power generation processes. While a number of pilot projects have been launched to experimentally test IGCC technologies, mathematical simulation remains a central part of the ongoing research efforts. A major challenge in modeling an IGCC power plant is the lack of real experience and reliable data. It is critical to properly understand the state of knowledge and evaluate the impact of uncertainty in every phase of the R&D process. A rigorous investigation of the effect of uncertainty on IGCC system requires accurate quantification of input uncertainty and efficient propagation of uncertainty through system models. This thesis proposes several uncertainty quantification methods which expand the sources of information that can be used for parameter estimation. Key features of these methods include the use of entropy maximization to translate subjective opinions to probability distribution functions, and a more flexible probability model that easily captures anomaly associated with small sample data. In addition, Bayesian estimation is extended to dynamic models. Aided by a computationally efficient algorithm, termed sequential Monte Carlo method, the Bayesian approach is shown to be an effective way to estimate time-variant parameters. Uncertainty propagation is performed using the deterministic equivalent modeling method (DEMM) which is based on polynomial chaos representation of random variables and probabilistic collocation algorithm. One major issue often overlooked in the analysis of IGCC models is to represent correlation in the input parameters. This thesis proposes the use of principal component analysis (PCA) to represent correlated random variables. The resulting formulation is the same as the truncated Karhunen-Lodve expansions. Explicit incorporation of correlation not only improves accuracy of the approximation but also reduces the overall computational time. A comprehensive study of the MIT-BP IGCC model is carried out to determine uncertainties of the key measures of performance and cost, including energy output, thermal efficiency, CO 2 emission, plant capital cost, and cost of electricity. Whenever possible, the probability distributions of input parameters are estimated based on realistic data. Experts' judgments are solicited if data acquisition is infeasible. Uncertainty analysis is conducted in a three-step approach. First, technology-related input parameters are taken into account to determine uncertainties of plant performance. Second, cost uncertainties are determined with only economic inputs in order to identify important economic parameters. Finally, the plant model is integrated with cost model and they are evaluated with the key technical and economic inputs identified in the previous steps. Our study indicates the property of coal feed has a substantial impact on the energy production of the IGCC plant, and subsequently on the cost of electricity. Immature technologies such as gasification and gas turbine have important bearing on model performance hence need to be addressed in future research.by Bo Gong.Ph.D

Information Geometry

This Special Issue of the journal Entropy, titled “Information Geometry I”, contains a collection of 17 papers concerning the foundations and applications of information geometry. Based on a geometrical interpretation of probability, information geometry has become a rich mathematical field employing the methods of differential geometry. It has numerous applications to data science, physics, and neuroscience. Presenting original research, yet written in an accessible, tutorial style, this collection of papers will be useful for scientists who are new to the field, while providing an excellent reference for the more experienced researcher. Several papers are written by authorities in the field, and topics cover the foundations of information geometry, as well as applications to statistics, Bayesian inference, machine learning, complex systems, physics, and neuroscience

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency