We develop a facile methodology using dynamic programming for goals-based wealth management over long horizons where rebalancing uses the standard securities and also derivative securities. A kernel density estimation approach is developed to accommodate any number of derivative assets, solving a high dimensional problem with fast computation. The approach easily accommodates skewed and fat-tailed distributions. Portfolio performance is much better with the use of options, especially for investors with aggressive goals.

Using a big data set of venture capital financing and related startup firms from Crunchbase, this paper develops a machine-learning model called CapitalVX (for "Capital Venture eXchange") to predict the outcomes for startups, i.e., whether they will exit successfully through an IPO or acquisition, fail, or remain private. Using a large feature set, the out-of-sample accuracy of predictions on startup outcomes and follow-on funding is 80–89%. This research suggests that VC/PE firms may be able to benefit from using machine learning to screen potential investments using publicly available information, diverting this time instead into mentoring and monitoring the investments they make.

Abstract Production of oil and gas in Indonesia has fallen steadily during the last decade, owing to a combination of reduced exploration efforts and natural decline in currently producing fields. To counter-balance declining reserves in mature oil fields and a lack of expansion of known natural gas reserves, policy makers are now pursuing exploration initiatives more energetically. However, producers have expressed deep concern about the prospects for fair cost-recovery rules, and about the legal and regulatory environment more generally. Three issues are particularly relevant to the investment climate: resource nationalism; the anti-corruption drive; and decentralisation. Steps that the government could take include stemming the erosion of fiscal terms in contracts and ensuring contract sanctity. And although economic nationalists will find it hard to accept, maximising the benefits to the Indonesian people of exploiting the nation's hydrocarbon resources will require the presence of the world's most efficient operators.

To help understand multidimensional data, information visualisation techniques are often applied to take advantage of human visual perception in exposing latent structure. A popular means of presenting such data is via two-dimensional scatterplots where the inter-point proximities reflect some notion of similarity between the entities represented. This can result in potentially interesting structure becoming almost immediately apparent. Traditional algorithms for carrying out this dimension reduction tend to have different strengths and weaknesses in terms of run times and layout quality. However, it has been found that the combination of algorithms can produce hybrid variants that exhibit significantly lower run times while maintaining accurate depictions of high-dimensional structure. The author's initial contribution in the creation of such algorithms led to the design and implementation of a software system (HIVE) for the development and investigation of new hybrid variants and the subsequent analysis of the data they transform. This development was motivated by the fact that there are potentially many hybrid algorithmic combinations to explore and therefore an environment that is conductive to their development, analysis and use is beneficial not only in exploring the data they transform but also in exploring the growing number of visualisation tools that these algorithms beget. This thesis descries three areas of the author's contribution to the field of information visualisation. Firstly, work on hybrid algorithms for dimension reduction is presented and their analysis shows their effectiveness. Secondly, the development of a framework for the creation of tailored hybrid algorithms is illustrated. Thirdly, a system embodying the framework, providing an environment conductive to the development, evaluation and use of the algorithms is described. Case studies are provided to demonstrate how the author and others have used and found value in the system across areas as diverse as environmental science, social science and investigative psychology, where multidimensional data are in abundance.

Visualisation systems consisting of a set of components through which data and interaction commands flow have been explored by a number of researchers. Such hybrid and multistage algorithms can be used to reduce overall computation time, and to provide views of the data that show intermediate results and the outputs of complementary algorithms. In this paper we present work on expanding the range and variety of such components, with two new techniques for analysing and controlling the performance of visualisation processes. While the techniques presented are quite different, they are unified within HIVE: a visualisation system based upon a data-flow model and visual programming. Embodied within this system is a framework for weaving together our visualisation components to better afford insight into data and also deepen understanding of the process of the data's visualisation. We describe the new components and offer short case studies of their application. We demonstrate that both analysts and visualisation designers can benefit from a rich set of components and integrated tools for profiling performance.

A number of researchers have put forward approaches to the development and use of visualisation systems consisting of a number of components, through which data and interaction commands flow. Systems based on hybrid and multistage algorithms can be used to reduce algorithmic complexity, and to open up intermediate stages of the algorithm for inspection and steering. In this paper we present work on aiding the developer and the user of such algorithms, applying interactive visualisation techniques to the process of designing, evaluating and using visualisation systems. We present a set of tools designed to show and control the performance of other visualisation components, and we offer case studies of their application to a number of data sets. Through this work we are exploring ways in which techniques traditionally used to prepare for visualisation runs, and to retrospectively analyse them, can find new uses within the context of a multi– component visualisation system. We aim to demonstrate that when such systems use flexible structures for data flow and cross–component interaction, developers and users can gain valuable understanding and control of the processes and parameters of visualisation, and hence insight into the information being visualised. Keywords--Visual programming, data-flow model, multiple views, coordinated views, hybrid algorithms, dimension reduction, algorithmic profiling.

Endothelin receptor blockade is an emerging therapy for pulmonary hypertension. However, hemodynamic and structural effects and potential changes in endogenous nitric oxide (NO)-cGMP and endothelin-1 signaling of chronic endothelin A receptor blockade in pulmonary hypertension secondary to congenital heart disease are unknown. Therefore, the objectives of this study were to determine hemodynamic and structural effects and potential changes in endogenous NO-cGMP and endothelin-1 signaling of chronic endothelin A receptor blockade in a lamb model of increased pulmonary blood flow following in utero placement of an aortopulmonary shunt. Immediately after spontaneous birth, shunt lambs were treated lifelong with either an endothelin A receptor antagonist (PD-156707) or placebo. At 4 wk of age, PD-156707-treated shunt lambs (n = 6) had lower pulmonary vascular resistance and right atrial pressure than placebo-treated shunt lambs ( n = 8, P < 0.05). Smooth muscle thickness or arterial number per unit area was not different between the two groups. However, the number of alveolar profiles per unit area was increased in the PD-156707-treated shunt lambs (190.7 +/- 5.6 vs. 132.9 +/- 10.0, P < 0.05). Plasma endothelin-1 and cGMP levels and lung NOS activity, cGMP, eNOS, preproendothelin-1, endothelin-converting enzyme-1, endothelin A, and endothelin B receptor protein levels were similar in both groups. We conclude that chronic endothelin A receptor blockade attenuates the progression of pulmonary hypertension and augments alveolar growth in lambs with increased pulmonary blood flow.

A number of researchers have designed visualisation systems that consist of multiple components, through which data and interaction commands flow. Such multistage (hybrid) models can be used to reduce algorithmic complexity, and to open up intermediate stages of algorithms for inspection and steering. In this paper we present work on aiding the developer and the user of such algorithms through the application of interactive visualisation techniques. We present a set of tools designed to profile the performance of other visualisation components, and provide further functionality for the exploration of high dimensional data sets. Case studies are provided, illustrating the application of the profiling modules to a number of data sets. Through this work we are exploring ways in which techniques traditionally used to prepare for visualisation runs, and to retrospectively analyse them, can find new uses within the context of a multi-component visualisation system.

Data can be distinguished according to volume, vari able types and distribution, and each of these characteristics imp oses constraints upon the choice of applicable algorithms for their visualisation. This has led to an abundance of often disparate alg orithmic techniques. Previous work has shown that a hybrid a lgorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scali ng (MDS). This paper presents a system and framework in which a user can easily explore algorithms as well as their hybrid c onjunctions and the data flowing through them. Visual programming a d a novel algorithmic architecture let the user semi–automati cally define data flows and the co-ordination of multiple views of algorithmic and visualisation components. We propose that our a pp o ch has two main benefits: significant improvements in run times of MDS algorithms can be achieved, and intermediate views of the data and the visualisation program structure can provide greater insight and control over the visualisation process. CR Categories: I.5.3 [Pattern recognition]: Clustering – Algorithms; E.1 [Data Structures]: Graphs and netwo rks; D.1.7 [Programming Techniques]: Visual Programming; I.3.6 [Computer Graphics]: Methodology and Techniques – I nteraction techniques;

The term ‘proximity data’ refers to data sets within which it is possible to assess the similarity of pairs of objects. Multidimensional scaling (MDS) is applied to such data and attempts to map high-dimensional objects onto low-dimensional space through the preservation of these similarity relations. Standard MDS techniques have in the past suffered from high computational complexity and, as such, could not feasibly be applied to data sets over a few thousand objects in size. Through a novel hybrid approach based upon stochastic sampling, interpolation and spring models, we have designed an algorithm running in O( N√N). Using Chalmers’ 1996 O( N2) spring model as a benchmark for the evaluation of our technique, we compare layout quality and run times using sets of synthetic and real data. Our algorithm executes significantly faster than Chalmers’ 1996 algorithm, while producing superior layouts. In reducing complexity and run time, we allow the visualisation of data sets of previously infeasible size. Our results indicate that our method is a solid foundation for interactive and visual exploration of data.

In visualising multidimensional data, it is well known that different types of algorithms to process them. Data sets might be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualization. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This suggests that hybrid combinations of appropriate algorithms might also successfully address other characteristics of data. This paper presents a system and framework in which a user can easily explore hybrid algorithms and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views.

Many clustering and layout techniques have been used for structuring and visualising complex data. This paper is inspired by a number of such contemporary techniques and presents a novel hybrid approach based upon stochastic sampling, interpolation and spring models. We use Chalmers' 1996 O(N/sup 2/) spring model as a benchmark when evaluating our technique, comparing layout quality and run times using data sets of synthetic and real data. Our algorithm runs in O(N/spl radic/N) and executes significantly faster than Chalmers' 1996 algorithm, whilst producing superior layouts. In reducing complexity and run time, we allow the visualisation of data sets of previously infeasible size. Our results indicate that our method is a solid foundation for interactive and visual exploration of data.

Many clustering and layout techniques have been used for structuring and visualising complex data. This paper explores a number of combinations and variants of sampling, K-means clustering and spring models in making such layouts, using Chalmers’ 1996 linear iteration time spring model as a benchmark. This algorithm runs in O(N) time overall, but the run times for the new algorithms we describe reach O(N√N). We compare their layout quality and run times in laying out two collections of synthetic data, drawing samples from each collection of sizes ranging from 1000 to 20000. Based on these comparisons, we outline a number of avenues for future work that may further reduce time complexity and improve layout quality. ____________________________________________________________________________________________________