Handbook Supported Employment

[Excerpt] Supported employment has brought about improvements in the quality of life of women and men with a disability by enabling them to become active participants in society. It has a positive impact on families – and on employers who benefit from the contribution which disabled people can make at work. The strength of supported employment is that it enables people with disabilities to enter the real world of work by focusing on individual abilities and by providing varying levels of individualized support, depending on needs. Support and advice is also provided to the employer

Twenty-Five Comparators is Optimal when Sorting Nine Inputs (and Twenty-Nine for Ten)

This paper describes a computer-assisted non-existence proof of nine-input sorting networks consisting of 24 comparators, hence showing that the 25-comparator sorting network found by Floyd in 1964 is optimal. As a corollary, we obtain that the 29-comparator network found by Waksman in 1969 is optimal when sorting ten inputs. This closes the two smallest open instances of the optimal size sorting network problem, which have been open since the results of Floyd and Knuth from 1966 proving optimality for sorting networks of up to eight inputs. The proof involves a combination of two methodologies: one based on exploiting the abundance of symmetries in sorting networks, and the other, based on an encoding of the problem to that of satisfiability of propositional logic. We illustrate that, while each of these can single handed solve smaller instances of the problem, it is their combination which leads to an efficient solution for nine inputs.Comment: 18 page

The Quest for Optimal Sorting Networks: Efficient Generation of Two-Layer Prefixes

Previous work identifying depth-optimal $n$-channel sorting networks for $9\leq n \leq 16$ is based on exploiting symmetries of the first two layers. However, the naive generate-and-test approach typically applied does not scale. This paper revisits the problem of generating two-layer prefixes modulo symmetries. An improved notion of symmetry is provided and a novel technique based on regular languages and graph isomorphism is shown to generate the set of non-symmetric representations. An empirical evaluation demonstrates that the new method outperforms the generate-and-test approach by orders of magnitude and easily scales until $n=40$

Sorting Networks: the End Game

This paper studies properties of the back end of a sorting network and illustrates the utility of these in the search for networks of optimal size or depth. All previous works focus on properties of the front end of networks and on how to apply these to break symmetries in the search. The new properties help shed understanding on how sorting networks sort and speed-up solvers for both optimal size and depth by an order of magnitude

Cenozoic sedimentary and volcanic rocks of New Zealand: A reference volume of lithology, age and paleoenvironments with maps (PMAPs) and database.

This volume presents descriptive geological data and text about each Cenozoic sedimentary and volcanic geological unit to formation and member level (in some cases) exposed on land in New Zealand, including their lithology, stratigraphic age and inferred environment of deposition or emplacement. These data are illustrated as two types of PMAPS: a present-day paleoenvironment map of New Zealand; and as restored paleoenvironment maps, one for each million years from 65 Ma to the present. These information and data underpin the development of a new Cenozoic paleogeographical model of New Zealand

Development and Testing of a Self-Contained, Portable Instrumentation System for a Fighter Pilot Helmet

A self-contained, portable, inertial and positional measurement system was developed and tested for an HGU-55 model fighter pilot helmet. The system, designated the Portable Helmet Instrumentation System (PHIS), demonstrated the recording of accelerations and rotational rates experienced by the human head in a flight environment. A compact, self-contained, “knee-board” sized computer recorded these accelerations and rotational rates during flight. The present research presents the results of a limited evaluation of this helmet-mounted instrumentation system flown in an Extra 300 fully aerobatic aircraft. The accuracy of the helmet-mounted, inertial head tracker system was compared to the aircraft-mounted referenced system. The ability of the Portable Helmet Instrumentation System to record position, orientation and inertial information in ground and flight conditions was evaluated. The capability of the Portable Helmet Instrumentation System to provide position, orientation and inertial information with sufficient fidelity was evaluated. The concepts demonstrated in this system are: 1) calibration of the inertial sensing element without external equipment 2) the use of differential inertial sensing equipment to remove the accelerations and rotational rates of a moving vehicle from the pilot’s head-tracking measurements 3) the determination of three-dimensional position and orientation from three corresponding points using a range sensor. The range sensor did not operate as planned. The helmet only managed to remain within the range sensor’s field of view for 37% of flight time. Vertical accelerations showed the greatest correlation when comparing helmet measurements to aircraft measurements. The PHIS operated well during level flight

Black-Box Parallelization for Machine Learning

The landscape of machine learning applications is changing rapidly: large centralized datasets are replaced by high volume, high velocity data streams generated by a vast number of geographically distributed, loosely connected devices, such as mobile phones, smart sensors, autonomous vehicles or industrial machines. Current learning approaches centralize the data and process it in parallel in a cluster or computing center. This has three major disadvantages: (i) it does not scale well with the number of data-generating devices since their growth exceeds that of computing centers, (ii) the communication costs for centralizing the data are prohibitive in many applications, and (iii) it requires sharing potentially privacy-sensitive data. Pushing computation towards the data-generating devices alleviates these problems and allows to employ their otherwise unused computing power. However, current parallel learning approaches are designed for tightly integrated systems with low latency and high bandwidth, not for loosely connected distributed devices. Therefore, I propose a new paradigm for parallelization that treats the learning algorithm as a black box, training local models on distributed devices and aggregating them into a single strong one. Since this requires only exchanging models instead of actual data, the approach is highly scalable, communication-efficient, and privacy-preserving. Following this paradigm, this thesis develops black-box parallelizations for two broad classes of learning algorithms. One approach can be applied to incremental learning algorithms, i.e., those that improve a model in iterations. Based on the utility of aggregations it schedules communication dynamically, adapting it to the hardness of the learning problem. In practice, this leads to a reduction in communication by orders of magnitude. It is analyzed for (i) online learning, in particular in the context of in-stream learning, which allows to guarantee optimal regret and for (ii) batch learning based on empirical risk minimization where optimal convergence can be guaranteed. The other approach is applicable to non-incremental algorithms as well. It uses a novel aggregation method based on the Radon point that allows to achieve provably high model quality with only a single aggregation. This is achieved in polylogarithmic runtime on quasi-polynomially many processors. This relates parallel machine learning to Nick's class of parallel decision problems and is a step towards answering a fundamental open problem about the abilities and limitations of efficient parallel learning algorithms. An empirical study on real distributed systems confirms the potential of the approaches in realistic application scenarios

SAT Solving for Argument Filterings

This paper introduces a propositional encoding for lexicographic path orders in connection with dependency pairs. This facilitates the application of SAT solvers for termination analysis of term rewrite systems based on the dependency pair method. We address two main inter-related issues and encode them as satisfiability problems of propositional formulas that can be efficiently handled by SAT solving: (1) the combined search for a lexicographic path order together with an \emph{argument filtering} to orient a set of inequalities; and (2) how the choice of the argument filtering influences the set of inequalities that have to be oriented. We have implemented our contributions in the termination prover AProVE. Extensive experiments show that by our encoding and the application of SAT solvers one obtains speedups in orders of magnitude as well as increased termination proving power

Carbon emissions pinch analysis (CEPA) for emissions reduction in the New Zealand electricity sector

Carbon Emissions Pinch Analysis (CEPA) is a recent extension of traditional thermal and mass pinch analysis to the area of emissions targeting and planning on a macroscale (i.e. economy wide). This paper presents a carbon pinch analysis of the New Zealand electricity industry and illustrates some of the issues with realising meaningful emissions reductions. The current large proportion of renewable generation sources (~67% in 2007) complicates wholesale emissions reductions. The biggest growth in renewable generation is expected to come from geothermal energy followed by wind and hydro. A four fold increase in geothermal generation capacity is needed in addition to large amounts of new wind generation to reduce emissions to around 1990 levels and also meet projected demand. The expected expansion of geothermal generation in New Zealand raises issues of GHG emissions from the geothermal fields. The emissions factors between fields can vary by almost two orders of magnitude making predictions of total emissions highly site specific