Risk analysis in aquaculture: A step-by-step introduction with worked examples

This publication is based on materials covered and outputs generated during the Workshop on Risk Assessment Methodologies and Tools for Aquaculture in Sub-Saharan Africa, which was jointly held by WorldFish and FAO in Siavonga, Zambia on 28 June - 2 July 2010. The workshop was delivered as a training exercise to 17 participants from seven sub-Saharan countries and was designed to highlight current methodologies and tools available for environmental risk analysis in aquaculture development. A key focus of the workshop was to encourage participants to consider hypothetical but realistic scenarios and to discuss issues relevant to evaluating the environmental risks of a given activity or scenario. This publication presents selected scenarios from the workshop and the outcomes of the deliberative process as developed by the participants. This publication is factual but not comprehensive, therefore any statements or estimations of risk do not represent the actual risks arising from the described scenario. It is intended to serve as an easily readable introduction to risk analysis, highlighting worked examples that will provide guidance on how a risk analysis may be approached in a similar situation.

Some examples of multiple solutions in limiting flaw calculations using PD6493:1991 fracture mechanics analyses

The use of PC-based PD6493:1991 fracture assessment procedures has revealed that, under certain circumstances, flaws of different dimensions may be found as being limiting or critical for identical applied conditions. The main causes for multiple solutions are a steep applied stress gradient, residual stress relaxation and flaw re-characterisation. This work uses several case studies to illustrate some of the circumstances under which multiple solutions occurs.

Extensions of a Theory of Networks for Approximation and Learning: Outliers and Negative Examples

Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multi-dimensional function. From this point of view, this form of learning is closely related to regularization theory. In this note, we extend the theory by introducing ways of dealing with two aspects of learning: learning in the presence of unreliable examples and learning from positive and negative examples. The first extension corresponds to dealing with outliers among the sparse data. The second one corresponds to exploiting information about points or regions in the range of the function that are forbidden.

Learning Structural Descriptions from Examples

The research here described centers on how a machine can recognize concepts and learn concepts to be recognized. Explanations are found in computer programs that build and manipulate abstract descriptions of scenes such as those children construct from toy blocks. One program uses sample scenes to create models of simple configurations like the three-brick arch. Another uses the resulting models in making identifications. Throughout emphasis is given to the importance of using good descriptions when exploring how machines can come to perceive and understand the visual environment.

Justified Generalization: Acquiring Procedures from Examples

This thesis describes an implemented system called NODDY for acquiring procedures from examples presented by a teacher. Acquiring procedures form examples involves several different generalization tasks. Generalization is an underconstrained task, and the main issue of machine learning is how to deal with this underconstraint. The thesis presents two principles for constraining generalization on which NODDY is based. The first principle is to exploit domain based constraints. NODDY demonstrated how such constraints can be used both to reduce the space of possible generalizations to manageable size, and how to generate negative examples out of positive examples to further constrain the generalization. The second principle is to avoid spurious generalizations by requiring justification before adopting a generalization. NODDY demonstrates several different ways of justifying a generalization and proposes a way of ordering and searching a space of candidate generalizations based on how much evidence would be required to justify each generalization. Acquiring procedures also involves three types of constructive generalizations: inferring loops (a kind of group), inferring complex relations and state variables, and inferring predicates. NODDY demonstrates three constructive generalization methods for these kinds of generalization.

Nine examples of innovative assessment.

Examples submitted by invitation to a website intended as a genera; resource in Australasia I'm afraid I do not have a pre-publication copy

Teaching from Examples in Assembly and Manipulation of Snack Food Ingredients by Robot

T. G. Williams, J.J. Rowland, and Lee M.H., Teaching from Examples in Assembly and Manipulation of Snack Food Ingredients by Robot, Proc. IEEE/RSJ Int. Conf. on Robots and Systems (IROS 2001), Nov., 2001, pp2300-2305.

Search by Shape Examples: Modeling Nonrigid Deformation

We describe our work on shape-based image database search using the technique of modal matching. Modal matching employs a deformable shape decomposition that allows users to select example objects and have the computer efficiently sort the set of objects based on the similarity of their shape. Shapes are compared in terms of the types of nonrigid deformations (differences) that relate them. The modal decomposition provides deformation "control knobs" for flexible matching and thus allows for selecting weighted subsets of shape parameters that are deemed significant for a particular category or context. We demonstrate the utility of this approach for shape comparison in 2-D image databases; however, the general formulation is applicable to signals of any dimensionality.

Programming Examples Needing Polymorphic Recursion

Inferring types for polymorphic recursive function definitions (abbreviated to polymorphic recursion) is a recurring topic on the mailing lists of popular typed programming languages. This is despite the fact that type inference for polymorphic recursion using for all-types has been proved undecidable. This report presents several programming examples involving polymorphic recursion and determines their typability under various type systems, including the Hindley-Milner system, an intersection-type system, and extensions of these two. The goal of this report is to show that many of these examples are typable using a system of intersection types as an alternative form of polymorphism. By accomplishing this, we hope to lay the foundation for future research into a decidable intersection-type inference algorithm. We do not provide a comprehensive survey of type systems appropriate for polymorphic recursion, with or without type annotations inserted in the source language. Rather, we focus on examples for which types may be inferred without type annotations.

Examples of Network Flow Verification Using TRAFFIC(X)

In our previous work, we developed TRAFFIC(X), a specification language for modeling bi-directional network flows featuring a type system with constrained polymorphism. In this paper, we present two ways to customize the constraint system: (1) when using linear inequality constraints for the constraint system, TRAFFIC(X) can describe flows with numeric properties such as MTU (maximum transmission unit), RTT (round trip time), traversal order, and bandwidth allocation over parallel paths; (2) when using Boolean predicate constraints for the constraint system, TRAFFIC(X) can describe routing policies of an IP network. These examples illustrate how to use the customized type system.