Stochastic Network Design: Models and Scalable Algorithms

Many natural and social phenomena occur in networks. Examples include the spread of information, ideas, and opinions through a social network, the propagation of an infectious disease among people, and the spread of species within an interconnected habitat network. The ability to modify a phenomenon towards some desired outcomes has widely recognized benefits to our society and the economy. The outcome of a phenomenon is largely determined by the topology or properties of its underlying network. A decision maker can take management actions to modify a network and, therefore, change the outcome of the phenomenon. A management action is an activity that changes the topology or other properties of a network. For example, species that live in a small area may expand their population and gradually spread into an interconnected habitat network. However, human development of various structures such as highways and factories may destroy natural habitats or block paths connecting different habitat patches, which results in a population decline. To facilitate the dispersal of species and help the population recover, artificial corridors (e.g., a wildlife highway crossing) can be built to restore connectivity of isolated habitats, and conservation areas can be established to restore historical habitats of species, both of which are examples of management actions. The set of management actions that can be taken is restricted by a budget, so we must find cost-effective allocations of limited funding resources. In the thesis, the problem of finding the (nearly) optimal set of management actions is formulated as a discrete and stochastic optimization problem. Specifically, a general decision-making framework called stochastic network design is defined to model a broad range of similar real-world problems. The framework is defined upon a stochastic network, in which edges are either present or absent with certain probabilities. It defines several metrics to measure the outcome of the underlying phenomenon and a set of management actions that modify the network or its parameters in specific ways. The goal is to select a subset of management actions, subject to a budget constraint, to maximize a specified metric. The major contribution of the thesis is to develop scalable algorithms to find high- quality solutions for different problems within the framework. In general, these problems are NP-hard, and their objective functions are neither submodular nor super-modular. Existing algorithms, such as greedy algorithms and heuristic search algorithms, either lack theoretical guarantees or have limited scalability. In the thesis, fast approximate algorithms are developed under three different settings that are gradually more general. The most restricted setting is when a network is tree-structured. For this case, fully polynomial-time approximation schemes (FPTAS) are developed using dynamic programming algorithms and rounding techniques. A more general setting is when networks are general directed graphs. We use a sampling technique to convert the original stochastic optimization problem into a deterministic optimization problem and develop a primal-dual algorithm to solve it efficiently. In the previous two problem settings, the goal is to maximize connectivity of networks. In the most general setting, the goal is to maximize the number of nodes being connected and minimize the distance between these connected nodes. For example, we do not only want the species to reach a large number of habitat areas but also want them to be able to get there within a reasonable amount of time. The scalable algorithms for this setting combine a fast primal-dual algorithm and a sampling procedure. Three real-world problems from the areas of computational sustainability and emergency response are used to evaluate these algorithms. They are the barrier removal problem aimed to determine which instream barriers to remove to help fish access their historical habitats in a river network, the spatial conservation planning problem to determine which habitat units to set as conservation areas to encourage the dispersal of endangered species in a landscape, and the pre-disaster preparation problem aimed to minimize the disruption of emergency medical services by natural disasters. In these three problems, the developed algorithms are much more scalable than the existing state-of-the-arts and produce high-quality solutions

A semiparametric approach to estimating reference price effects in sales response models

It is well known that store-level brand sales may not only depend on contemporaneous influencing factors like current own and competitive prices or other marketing activities, but also on past prices representing customer response to price dynamics. On the other hand, non- or semiparametric regression models have been proposed in order to accommodate potential nonlinearities in price response, and related empirical findings for frequently purchased consumer goods indicate that price effects may show complex nonlinearities, which are difficult to capture with parametric models. In this contribution, we combine nonparametric price response modeling and behavioral pricing theory. In particular, we propose a semiparametric approach to flexibly estimating price-change or reference price effects based on store-level sales data. We compare different representations for capturing symmetric vs. asymmetric and proportional vs. disproportionate price-change effects following adaptation-level and prospect theory, and further compare our flexible autoregressive model specifications to parametric benchmark models. Functional flexibility is accommodated via P-splines, and all models are estimated within a fully Bayesian framework. In an empirical study, we demonstrate that our semiparametric dynamic models provide more accurate sales forecasts for most brands considered compared to competing benchmark models that either ignore price dynamics or just include them in a parametric way

Network extraction by routing optimization

Routing optimization is a relevant problem in many contexts. Solving directly this type of optimization problem is often computationally unfeasible. Recent studies suggest that one can instead turn this problem into one of solving a dynamical system of equations, which can instead be solved efficiently using numerical methods. This results in enabling the acquisition of optimal network topologies from a variety of routing problems. However, the actual extraction of the solution in terms of a final network topology relies on numerical details which can prevent an accurate investigation of their topological properties. In this context, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. In particular, in this framework, final graph acquisition is a challenging problem in-and-of-itself. Here we introduce a method to extract networks topologies from dynamical equations related to routing optimization under various parameters' settings. Our method is made of three steps: first, it extracts an optimal trajectory by solving a dynamical system, then it pre-extracts a network and finally, it filters out potential redundancies. Remarkably, we propose a principled model to address the filtering in the last step, and give a quantitative interpretation in terms of a transport-related cost function. This principled filtering can be applied to more general problems such as network extraction from images, thus going beyond the scenarios envisioned in the first step. Overall, this novel algorithm allows practitioners to easily extract optimal network topologies by combining basic tools from numerical methods, optimization and network theory. Thus, we provide an alternative to manual graph extraction which allows a grounded extraction from a large variety of optimal topologies.Comment: 17 pages, 7 main Figures, 3 SI figure

Computers from plants we never made. Speculations

We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analog electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots' network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, $\alpha$-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing inspired by physics, chemistry and biology. Essays presented to Julian Miller on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew Adamatzky (Springer, 2017

Locating Amazonian Dark Earths (ADE) in the Brazilian Amazon using Satellite Imagery

Amazonian Dark Earths (ADE) are patches of archaeological soils scattered throughout the Amazon Basin. These soils are anthropogenic and most evidence suggests that they are the result of unintentional cultural deposits as well as intentional efforts of Amerindian populations to improve the quality of their farmlands. ADE are a mixture of charcoal, organic matter and the underlying Oxisol soil. ADE are extremely fertile soils in comparison to the surrounding Oxisols and they are sought after by local residents for agricultural purposes. In the first chapter I discuss the value and physical properties of ADE in detail. Research is being conducted to learn how ADE were created and to explore the possibility of replicating them to sequester carbon and to reclaim depleted soils in the Amazon Basin. This dissertation seeks to assist in that effort by attempting to map currently unknown ADE sites hidden beneath the dense tropical forest canopy

Amazonian Dark Earths

Review article of Amazonian Dark Earths: Origins, Properties, Management. Johannes Lehmann, Dirse C. Kern, Bruno Glaser, William I. Woods, eds. Dordrecht, The Netherlands: Kluwer Academic Publishers, 2003. 505 pp., $279.00 (cloth). ISBN 978-1-4020-1839-8 [http://www.springer.com/]. Amazonian Dark Earths: Explorations in Space and Time. Bruno Glaser and William I. Woods, eds. Berlin: Springer-Verlag., 2004. 216 pp.,$189 (cloth). ISBN 978-3-54000754-8 [http://www.springer.com/]. Amazonian Dark Earths: Wim Sombroek’s Vision. William I. Woods, Wenceslau G. Teixeira, Johannes Lehmann, Christoph Steiner, Antoinette M.G.A. WinklerPrins, and Lilian Rebellato, eds. New York: Springer Science and Business Media B.V., 2009. 502 pp., $249 (cloth). ISBN 978-1-4020-9030-1 [http://www.springer.com/]

Once despised now desired: innovative land use and management of multilayered Pumice Soils in the Taupo and Galatea areas, central North Island, New Zealand

The tour brings together innovative land use change and management associated with dairy farming, and land-based effluent disposal, on weakly weathered and multi-layered, glass-rich, Pumice Soils (Vitrands) in the Taupo and Galatea areas. These changes and their effects, together with environmental and sustainability issues, form a central theme of the trip. Four main stops are planned, two before lunch and two after: (1) plantation pine-to-dairy farm conversion and impacts, the Taupo eruption deposits (AD 232 ± 10) and the Taupo soil, at Tahorakuri; (2) overview of the application of secondary-treated wastewater and nitrogen leaching and uptake, Rotokawa; (3) a sequence of five Holocene tephras and buried soils, including Kaharoa eruption deposits (AD 1314 ± 12) and the Galatea soil, Smeith Farm, Murupara; and (4) enhancing pasture production on ‘new’ soils formed by excavating and mixing (‘flipping’) buried soil horizons (paleosols) on Smeith’s farm. During the trip − which helps mark Waikato University’s 50th anniversary − we will see a spectacular range of volcanic and fluvial landscapes and deposits, together with impacts of tectonism, as we traverse the famous Taupo Volcanic Zone ((TVZ) in the central volcanic region. Landforms and soils dominated by tephras (volcanic ash) become generally younger towards the loci of volcanic activity. Extensive areas of soils have been formed repeatedly from the fragmental eruptive products of the two most frequently active and productive rhyolite (silica-rich) volcanic centres known, namely Taupo and Okataina. Thus soil stratigraphy and upbuilding pedogenesis form a second theme on the trip. The first part of the guidebook thus contains sections including (i) volcanism and its products, (ii) Quaternary volcanism in TVZ including deposits erupted recently from Taupo and Tarawera volcanoes from which Pumice Soils have been formed, (iii) tephra-derived soils including Pumice Soils, their classification, special problems, and (low) fertility, (iv) allophane and its formation, and (v) the interplay between geological and pedological processes relating to tephras (upbuilding pedogenesis). The second part then comprises notes and illustrations pertaining to each stop (note that figure and table numbers are self-contained at each stop, or not used). Broad overviews of the region’s geology are covered by Leonard et al. (2010), and the soils are outlined by Rijkse and Guinto (2010) and S-map. Further compilations of data are available in tour guides by Lowe (2008) and Lowe et al. (2010)

Applications of river formation dynamics

River formation dynamics is a metaheuristic where solutions are constructed by iteratively modifying the values associated to the nodes of a graph. Its gradient orientation provides interesting features such as the fast reinforcement of new shortcuts, the natural avoidance of cycles, and the focused elimination of blind alleys. Since the method was firstly proposed in 2007, several research groups have applied it to a wide variety of application domains, such as telecommunications, software testing, industrial manufacturing processes, or navigation. In this paper we review the main works of the last decade where the river formation dynamics metaheuristic has been applied to solve optimization problems

Towards Applying River Formation Dynamics in Continuous Optimization Problems

River Formation Dynamics (RFD) is a metaheuristic that has been successfully used by different research groups to deal with a wide variety of discrete combinatorial optimization problems. However, no attempt has been done to adapt it to continuous optimization domains. In this paper we propose a first approach to obtain such objective, and we evaluate its usefulness by comparing RFD results against those obtained by other more mature metaheuristics for continuous domains. In particular, we compare with the results obtained by Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, and Social Spider Optimization

Investigation of natural environment by space means. Geobotany, Geomorphology, soil sciences, agricultural lands, landscape study

Reports given by Soviet specialists at a meeting of Socialist countries on remote sensing of the earth using aerospace methods are presented