Imaging with Diffraction Tomography

The problem of cross sectional (tomographic) imaging bf objects with diffracting sources is addressed. Specifically the area of investigation is the effect of multiple scattering and attenuation phenomena in diffraction imaging. This work reviews the theory and limits of first order diffraction tomography and studies iterative techniques that can be used to improve the quality of tomographic imaging with diffracting sources. Conventional (straight-ray) tomographic algorithms are not valid when used with acoustic or microwave energy. Thus more sophisticated algorithms are needed; First order diffraction tomography uses a linearized version of the wave equation and gives an especially simple reconstruction algorithm. This work reviews first order approximations to the scattered field and studies the quality of the reconstructions when the assumptions behind these approximations are violated. It will be shown that the Born approximation is valid when the phase change across the object is less than it and the Rytov approximation is valid when the refractive index changes by less than two or three percent. Better reconstructions will be based on higher order approximations to the scattered field. This work describes two fixed point algorithms (the Born and the Rytov approximations) and an algebraic approach to more accurately calculate the scattered fields. The limits of each of these approaches is discussed and simulated results are shown. Finally a review of higher order inversion techniques is presented. Each of these techniques is reviewed and some of their limitations are discussed

Mainstreaming ecosystem science in spatial planning practice : exploiting a hybrid opportunity space

This paper develops a framework for improved mainstreaming of ecosystem science in policy and decision-making within a spatial planning context. Ecosystem science is advanced as a collective umbrella to capture a body of work and approaches rooted in social-ecological systems thinking, spawning a distinctive ecosystem terminology: ecosystem approach, ecosystem services, ecosystem services framework and natural capital. The interface between spatial planning and ecosystem science is explored as a theoretical opportunity space to improve mainstreaming processes adapting Rogers’ (2003) diffusion model. We introduce the twin concepts of hooks (linking ecosystem science to a key policy or legislative term, duty or priority that relate to a particular user group) and ‘bridges’ (linking ecosystem science to a term, concept or policy priority that is used and readily understood across multiple groups and publics) as translational mechanisms in transdisciplinary mainstreaming settings. We argue that ecosystem science can be embedded into the existing work priorities and vocabularies of spatial planning practice using these hooks and bridges. The resultant framework for mainstreaming is then tested, drawing on research funded as part of the UK National Ecosystem Assessment Follow-On programme (2012-2014), within 4 case studies; each reflecting different capacities, capabilities, opportunities and barriers. The results reveal the importance of leadership, political buy in, willingness to experiment outside established comfort zones and social learning as core drivers supporting mainstreaming processes. Whilst there are still significant challenges in mainstreaming in spatial planning settings, the identification and use of hooks and bridges collectively, enables traction to be gained for further advances; moving beyond the status quo to generate additionality and potential behaviour change within different modes of mainstreaming practice. This pragmatic approach has global application to help improve the way nature is respected and taken account of in planning systems nationally and globally

Decision-Theoretic Planning with non-Markovian Rewards

A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for decision-theoretic planning with non-Markovian rewards. The current version of NMRDPP implements, under a single interface, a family of methods based on existing as well as new approaches which we describe in detail. These include dynamic programming, heuristic search, and structured methods. Using NMRDPP, we compare the methods and identify certain problem features that affect their performance. NMRDPPs treatment of non-Markovian rewards is inspired by the treatment of domain-specific search control knowledge in the TLPlan planner, which it incorporates as a special case. In the First International Probabilistic Planning Competition, NMRDPP was able to compete and perform well in both the domain-independent and hand-coded tracks, using search control knowledge in the latter

On the Expressivity and Applicability of Model Representation Formalisms

A number of first-order calculi employ an explicit model representation formalism for automated reasoning and for detecting satisfiability. Many of these formalisms can represent infinite Herbrand models. The first-order fragment of monadic, shallow, linear, Horn (MSLH) clauses, is such a formalism used in the approximation refinement calculus. Our first result is a finite model property for MSLH clause sets. Therefore, MSLH clause sets cannot represent models of clause sets with inherently infinite models. Through a translation to tree automata, we further show that this limitation also applies to the linear fragments of implicit generalizations, which is the formalism used in the model-evolution calculus, to atoms with disequality constraints, the formalisms used in the non-redundant clause learning calculus (NRCL), and to atoms with membership constraints, a formalism used for example in decision procedures for algebraic data types. Although these formalisms cannot represent models of clause sets with inherently infinite models, through an additional approximation step they can. This is our second main result. For clause sets including the definition of an equivalence relation with the help of an additional, novel approximation, called reflexive relation splitting, the approximation refinement calculus can automatically show satisfiability through the MSLH clause set formalism.Comment: 15 page

Network conduciveness with application to the graph-coloring and independent-set optimization transitions

We introduce the notion of a network's conduciveness, a probabilistically interpretable measure of how the network's structure allows it to be conducive to roaming agents, in certain conditions, from one portion of the network to another. We exemplify its use through an application to the two problems in combinatorial optimization that, given an undirected graph, ask that its so-called chromatic and independence numbers be found. Though NP-hard, when solved on sequences of expanding random graphs there appear marked transitions at which optimal solutions can be obtained substantially more easily than right before them. We demonstrate that these phenomena can be understood by resorting to the network that represents the solution space of the problems for each graph and examining its conduciveness between the non-optimal solutions and the optimal ones. At the said transitions, this network becomes strikingly more conducive in the direction of the optimal solutions than it was just before them, while at the same time becoming less conducive in the opposite direction. We believe that, besides becoming useful also in other areas in which network theory has a role to play, network conduciveness may become instrumental in helping clarify further issues related to NP-hardness that remain poorly understood

Perfectionism and self-conscious emotions in British and Japanese students: Predicting pride and embarrassment after success and failure

Regarding self-conscious emotions, studies have shown that different forms of perfectionism show different relationships with pride, shame, and embarrassment depending on success and failure. What is unknown is whether these relationships also show cultural variations. Therefore, we conducted a study investigating how self-oriented and socially prescribed perfectionism predicted pride and embarrassment after success and failure comparing 363 British and 352 Japanese students. Students were asked to respond to a set of scenarios where they imagined achieving either perfect (success) or flawed results (failure). In both British and Japanese students, self-oriented perfectionism positively predicted pride after success and embarrassment after failure whereas socially prescribed perfectionism predicted embarrassment after success and failure. Moreover, in Japanese students, socially prescribed perfectionism positively predicted pride after success and self-oriented perfectionism negatively predicted pride after failure. The findings have implications for our understanding of perfectionism indicating that the perfectionism–pride relationship not only varies between perfectionism dimensions, but may also show cultural variations

Automatically generating streamlined constraint models with ESSENCE and CONJURE

Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously, effective streamlined models have been constructed by hand, requiring an expert to examine closely solutions to small instances of a problem class and identify regularities. We present a system that automatically generates many conjectured regularities for a given Essence specification of a problem class by examining the domains of decision variables present in the problem specification. These conjectures are evaluated independently and in conjunction with one another on a set of instances from the specified class via an automated modelling tool-chain comprising of Conjure, Savile Row and Minion. Once the system has identified effective conjectures they are used to generate streamlined models that allow instances of much larger scale to be solved. Our results demonstrate good models can be identified for problems in combinatorial design, Ramsey theory, graph theory and group theory - often resulting in order of magnitude speed-ups.Postprin

Editorial: Special Section on Statistical and Perceptual Audio Processing

Human perception has always been an inspiration for automatic processing systems, not least because tasks such as speech recognition only exist because people do them—and, indeed, without that example we might wonder if they were possible at all. As computational power grows, we have increasing opportunities to model and duplicate perceptual abilities with greater fidelity, and, most importantly, based on larger and larger amounts of raw data describing both what signals exist in the real world, and how people respond to them. The power to deal with large data sets has meant that approaches that were once mere theoretical possibilities, such as exhaustive search of exponentially-sized codebooks, or real-time direct convolution of long sequences, have become increasingly practical and even unremarkable. A major consequence of this is the growth of statistical or corpus-based approaches, where complex relations, discriminations, or structures are inferred directly from example data (for instance by optimizing the parameters of a very general algorithm). An increasing number of complex tasks can be given empirically optimal solutions based on large, representative datasets. The traditional idea of perceptually-inspired processing is to develop a machine algorithm for a complex task such as melody recognition or source separation through inspiration and introspection about how individuals perform the task, and on the basis of direct psychological or neurophysiological data. The results can appear to be at odds with the statistical perspective, since perceptually-motivated work is often ad-hoc, comprising many stages whose individual contributions are difficult to separate. We believe that it is important to unify these two approaches: to employ rigorous, exhaustive techniques taking advantage of the statistics of large data sets to develop and solve perceptually-based and subjectively-defined problems. With this in mind, we organized a one-day workshop on Statistical and Perceptual Audio Processing as a satellite to the International Conference on Spoken Language Processing (ICSLP-INTERSPEECH), held in Jeju, Korea, in September 2004

Tilt order parameters, polarity and inversion phenomena in smectic liquid crystals

The order parameters for the phenomenological description of the smectic-{\it A} to smectic-{\it C} phase transition are formulated on the basis of molecular symmetry and structure. It is shown that, unless the long molecular axis is an axis of two-fold or higher rotational symmetry, the ordering of the molecules in the smectic-{\it C} phase gives rise to more than one tilt order parameter and to one or more polar order parameters. The latter describe the indigenous polarity of the smectic-{\it C} phase, which is not related to molecular chirality but underlies the appearance of spontaneous polarisation in chiral smectics. A phenomenological theory of the phase transition is formulated by means of a Landau expansion in two tilt order parameters (primary and secondary) and an indigenous polarity order parameter. The coupling among these order parameters determines the possibility of sign inversions in the temperature dependence of the spontaneous polarisation and of the helical pitch observed experimentally for some chiral smectic-{\it $C^{\ast}$} materials. The molecular interpretation of the inversion phenomena is examined in the light of the new formulation.Comment: 12 pages, 5 figures, RevTe

Effects of experimentally added salmon subsidies on resident fishes via direct and indirect pathways

Artificial additions of nutrients of differing forms such as salmon carcasses and analog pellets (i.e. pasteurized fishmeal) have been proposed as a means of stimulating aquatic productivity and enhancing populations of anadromous and resident fishes. Nutrient mitigation to enhance fish production in stream ecosystems assumes that the central pathway by which effects occur is bottom-up, through aquatic primary and secondary production, with little consideration of reciprocal aquatic-terrestrial pathways. The net outcome (i.e. bottom-up vs. top-down) of adding salmon-derived materials to streams depend on whether or not these subsidies indirectly intensify predation on in situ prey via increases in a shared predator or alleviate such predation pressure. We conducted a 3-year experiment across nine tributaries of the N. Fork Boise River, Idaho, USA, consisting of 500-m stream reaches treated with salmon carcasses (n = 3), salmon carcass analog (n = 3), and untreated control reaches (n = 3). We observed 2–8 fold increases in streambed biofilms in the 2–6 weeks following additions of both salmon subsidy treatments in years 1 and 2 and a 1.5-fold increase in standing crop biomass of aquatic invertebrates to carcass additions in the second year of our experiment. The consumption of benthic invertebrates by stream fishes increased 110–140% and 44–66% in carcass and analog streams in the same time frame, which may have masked invertebrate standing crop responses in years 3 and 4. Resident trout directly consumed 10.0–24.0 g·m-2·yr-1 of salmon carcass and \u3c1–11.0 g·m-2·yr-1 of analog material, which resulted in 1.2–2.9 g·m-2·yr-1 and 0.03–1.4 g·m-2·yr-1 of tissue produced. In addition, a feedback flux of terrestrial maggots to streams contributed 0.0–2.0 g·m-2·yr-1 to trout production. Overall, treatments increased annual trout production by 2–3 fold, though density and biomass were unaffected. Our results indicate the strength of bottom-up and top-down responses to subsidy additions was asymmetrical, with top-down forces masking bottom-up effects that required multiple years to manifest. The findings also highlight the need for nutrient mitigation programs to consider multiple pathways of energy and nutrient flow to account for the complex effects of salmon subsidies in stream-riparian ecosystems