A hybrid approach to combining CART and logistic regression for stock ranking

The benefits of applying tree-based methods to the purpose of modelling financial assets as opposed to linear factor analysis are increasingly being understood by market practitioners. Tree-based models such as CART (classification and regression trees) are particularly well suited to analysing stock market data which is noisy and often contains non-linear relationships and high-order interactions. CART was originally developed in the 1980s by medical researchers disheartened by the stringent assumptions applied by traditional regression analysis (Brieman et al. [1984]). In the intervening years, CART has been successfully applied to many areas of finance such as the classification of financial distress of firms (see Frydman, Altman and Kao [1985]), asset allocation (see Sorensen, Mezrich and Miller [1996]), equity style timing (see Kao and Shumaker [1999]) and stock selection (see Sorensen, Miller and Ooi [2000])...

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Supporting risk-informed decisions during business process execution

This paper proposes a technique that supports process participants in making risk-informed decisions, with the aim to reduce the process risks. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we prompt the participant with the expected risk that a given fault will occur given the particular input. These risks are predicted by traversing decision trees generated from the logs of past process executions and considering process data, involved resources, task durations and contextual information like task frequencies. The approach has been implemented in the YAWL system and its effectiveness evaluated. The results show that the process instances executed in the tests complete with substantially fewer faults and with lower fault severities, when taking into account the recommendations provided by our technique.

Learning hierarchical prototypes of motion time series for interactive systems

Abstract. For interactive systems, recognition, reproduction, and generalization of observed motion data are crucial for successful interaction. In this paper, we present a novel method for analysis of motion data that we refer to as K-OMM-trees. K-OMM-trees combine Ordered Means Models (OMMs) a model-based machine learning approach for time series with an hierarchical analysis technique for very large data sets, the K-tree algorithm. The proposed K-OMM-trees enable unsupervised prototype extraction of motion time series data with hierarchical data representation. After introducing the algorithmic details, we apply the proposed method to a gesture data set that includes substantial inter-class variations. Results from our studies show that K-OMM-trees are able to substantially increase the recognition performance and to learn an inherent data hierarchy with meaningful gesture abstractions.

Modeling and detection of complex attacks

A complex attack is a sequence of temporally and spatially separated legal and illegal actions each of which can be detected by various IDS but as a whole they constitute a powerful attack. IDS fall short of detecting and modeling complex attacks therefore new methods are required. This paper presents a formal methodology for modeling and detection of complex attacks in three phases: (1) we extend basic attack tree (AT) approach to capture temporal dependencies between components and expiration of an attack, (2) using enhanced AT we build a tree automaton which accepts a sequence of actions from input message streams from various sources if there is a traversal of an AT from leaves to root, and (3) we show how to construct an enhanced parallel automaton that has each tree automaton as a subroutine. We use simulation to test our methods, and provide a case study of representing attacks in WLANs.

Systematics and biology of the iconic Australian scribbly gum moths Ogmograptis Meyrick (Lepidoptera: Bucculatricidae) and their unique insect-plant interaction

The larvae of particular Ogmograptis spp. produce distinctive scribbles on some smooth-barked Eucalyptus spp. which are a common feature on many ornamental and forest trees in Australia. However, although they are conspicuous in the environment the systematics and biology of the genus has been poorly studied. This has been addressed through detailed field and laboratory studies of their biology of three species (O. racemosa Horak sp. nov., O. fraxinoides Horak sp. nov., O. scribula Meyrick), in conjunction with a comprehensive taxonomic revision support by a molecular phylogeny utilising the mitochondrial Cox1 and nuclear 18S genes. In brief, eggs are laid in bark depressions and the first instar larvae bore into the bark to the level where the future cork cambium forms (the phellegen). Early instar larvae bore wide, arcing tracks in this layer before forming a tighter zig-zag shaped pattern. The second last instar turns and bores either closely parallel to the initial mine or doubles its width, along the zig-zag shaped mine. The final instar possesses legs and a spinneret (unlike the earlier instars) and feeds exclusively on callus tissue which forms within the zig-zag shaped mine formed by the previous instar, before emerging from the bark to pupate at the base of the tree. The scars of mines them become visible scribble following the shedding of bark. Sequence data confirm the placement of Ogmograptis within the Bucculatricidae, suggest that the larvae responsible for the ‘ghost scribbles’ (unpigmented, raised scars found on smooth-barked eucalypts) are members of the genus Tritymba, and support the morphology-based species groups proposed for Ogmograptis. The formerly monotypic genus Ogmograptis Meyrick is revised and divided into three species groups. Eleven new species are described: Ogmograptis fraxinoides Horak sp. nov., Ogmograptis racemosa Horak sp. nov. and Ogmograptis pilularis Horak sp. nov. forming the scribula group with Ogmograptis scribula Meyrick; Ogmograptis maxdayi Horak sp. nov., Ogmograptis barloworum Horak sp. nov., Ogmograptis paucidentatus Horak sp. nov., Ogmograptis rodens Horak sp. nov., Ogmograptis bignathifer Horak sp. nov. and Ogmograptis inornatus Horak sp. nov. as the maxdayi group; Ogmograptis bipunctatus Horak sp. nov., Ogmograptis pulcher Horak sp. nov., Ogmograptis triradiata (Turner) comb. nov. and Ogmograptis centrospila (Turner) comb. nov. as the triradiata group. Ogmograptis notosema (Meyrick) cannot be assigned to a species group as the holotype has not been located. Three unique synapomorphies, all derived from immatures, redefine the family Bucculatricidae, uniting Ogmograptis, Tritymba Meyrick (both Australian) and Leucoedemia Scoble & Scholtz (African) with Bucculatrix Zeller, which is the sister group of the southern hemisphere genera. The systematic history of Ogmograptis and the Bucculatricidae is discussed.

Design and modeling of collaboration architecture for security

Threats against computer networks evolve very fast and require more and more complex measures. We argue that teams respectively groups with a common purpose for intrusion detection and prevention improve the measures against rapid propagating attacks similar to the concept of teams solving complex tasks known from field of work sociology. Collaboration in this sense is not easy task especially for heterarchical environments. We propose CIMD (collaborative intrusion and malware detection) as a security overlay framework to enable cooperative intrusion detection approaches. Objectives and associated interests are used to create detection groups for exchange of security-related data. In this work, we contribute a tree-oriented data model for device representation in the scope of security. We introduce an algorithm for the formation of detection groups, show realization strategies for the system and conduct vulnerability analysis. We evaluate the benefit of CIMD by simulation and probabilistic analysis.

A formal method for attack modelling and detection

This paper presents a formal methodology for attack modeling and detection for networks. Our approach has three phases. First, we extend the basic attack tree approach 1 to capture (i) the temporal dependencies between components, and (ii) the expiration of an attack. Second, using the enhanced attack trees (EAT) we build a tree automaton that accepts a sequence of actions from input stream if there is a traverse of an attack tree from leaves to the root node. Finally, we show how to construct an enhanced parallel automaton (EPA) that has each tree automaton as a subroutine and can process the input stream by considering multiple trees simultaneously. As a case study, we show how to represent the attacks in IEEE 802.11 and construct an EPA for it.

Fast exact nearest neighbour matching in high dimensions using d-D Sort

Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k nearest neighbour matching, but are only marginally more effective than linear search when performing exact matching in high-dimensional image descriptor data. This paper presents several improvements to linear search that allows it to outperform existing methods and recommends two approaches to exact matching. The first method reduces the number of operations by evaluating the distance measure in order of significance of the query dimensions and terminating when the partial distance exceeds the search threshold. This method does not require preprocessing and significantly outperforms existing methods. The second method improves query speed further by presorting the data using a data structure called d-D sort. The order information is used as a priority queue to reduce the time taken to find the exact match and to restrict the range of data searched. Construction of the d-D sort structure is very simple to implement, does not require any parameter tuning, and requires significantly less time than the best-performing tree structure, and data can be added to the structure relatively efficiently.

Issues in negotiating a Carbon Sequestration Agreement for a Biosequestration Offsets Project

Biosequestration of carbon in trees, forests and vegetation is a key method for offsetting greenhouse gas emissions. To facilitate it, the Commonwealth has introduced the Carbon Farming Initiative, a scheme whereby carbon credits can be earned for biosequestration offsets projects. The project proponent must acquire under state law a ‘carbon sequestration right’ which confers the benefit of the sequestered carbon on the land. Each State provides for an agreement associated with the carbon sequestration right between the landowner and the holder of the right (‘carbon sequestration agreement’). This article identifies some key risks and issues that must be considered in the drafting of a carbon sequestration agreement to support the successful operation of a biosequestration offsets project.

Adaptive dynamic path re-planning RRT algorithms with game theory for UAVs

The main aim of this paper is to describe an adaptive re-planning algorithm based on a RRT and Game Theory to produce an efficient collision free obstacle adaptive Mission Path Planner for Search and Rescue (SAR) missions. This will provide UAV autopilots and flight computers with the capability to autonomously avoid static obstacles and No Fly Zones (NFZs) through dynamic adaptive path replanning. The methods and algorithms produce optimal collision free paths and can be integrated on a decision aid tool and UAV autopilots.

Four new avian mitochondrial genomes help get to basic evolutionary questions in the late cretaceous

Good phylogenetic trees are required to test hypotheses about evolutionary processes. We report four new avian mitochondrial genomes, which together with an improved method of phylogenetic analysis for vertebrate mt genomes give results for three questions in avian evolution. The new mt genomes are: magpie goose (Anseranas semipalmata), an owl (morepork, Ninox novaeseelandiae); a basal passerine (rifleman, or New Zealand wren, Acanthisitta chloris); and a parrot (kakapo or owl-parrot, Strigops habroptilus). The magpie goose provides an important new calibration point for avian evolution because the well-studied Presbyornis fossils are on the lineage to ducks and geese, after the separation of the magpie goose. We find, as with other animal mitochondrial genomes, that RY-coding is helpful in adjusting for biases between pyrimidines and between purines. When RY-coding is used at third positions of the codon, the root occurs between paleognath and neognath birds (as expected from morphological and nuclear data). In addition, passerines form a relatively old group in Neoaves, and many modern avian lineages diverged during the Cretaceous. Although many aspects of the avian tree are stable, additional taxon sampling is required.

Future of the Haze Agreement : is the glass half empty or half full?

"In 1997–98, the ASEAN (Association of Southeast Asian Nations) region suffered an unprecedented health and environmental catastrophe due to choking haze created by a massive forest !re in Indonesia. It is estimated that the total losses from the fire could be US$5–6 billion after taking into account the loss of trees and other natural resources as well as the long-term impact on human health. This unprecedented anthropogenic disaster not only created a severe health and environmental hazard but also raised a question mark about the credibility and effectiveness of the ASEAN regional grouping. Against this background, ASEAN took a number of regional initiatives to try and solve the problem and finally adopted a new treaty for regional cooperation to combat forest fire and haze in 2002. This paper assesses the future success of this agreement from the perspectives of the legal, institutional and geopolitical reality of the region. Since numerous studies have examined state responsibility for transboundary environmental harm under international law and its implications on the ASEAN haze problem, this article will not touch upon that general debate nor the remedies that are possibly available to victim states. Rather, it will focus on the ASEAN regional legal and institutional initiatives to combat the haze pollution and compare them with a similar European regional agreement. Regarding the following analysis, it is important to recognise the uncertainty arising from Indonesia’s status (presently a non-party to the Agreement). A primary indication of the future effectiveness of this agreement can be drawn from an analysis of the principles involved in this agreement, bearing in mind the inherent difficulty of enforcing norms in the international environmental legal system as a whole, and the geopolitical reality of the region."

From rights to responsibilities : reconceptualising carbon sequestration rights in Australia

Biosequestration of carbon in trees, forests and vegetation is a key method for mitigating climate change in Australia. To facilitate this, all States have enacted legislation for carbon sequestration rights, separating commercial rights in carbon from ownership of the land, trees and vegetation in which the carbon is sequestered. Ownership of carbon sequestration rights under state law is a prerequisite for the issue of carbon credits to proponents of ‘eligible sequestration offsets projects’ under the Carbon Credits (Carbon Farming Initiative) Act 2011 (Cth) (‘Carbon Farming Act’). This article examines the extent to which current State carbon sequestration rights support the offsets regime established by the Carbon Farming Act. The Commonwealth Act is concerned with allocating responsibilities to ensure the maintenance of the carbon sequestration, while the State Acts confer commercial rights in the carbon and leave the responsibilities to be allocated by private agreements. The carbon sequestration rights as defined by state laws do not confer the rights of access and management over land that a project proponent needs in order to discharge its responsibilities to maintain the carbon sequestration.

Inventory procedures for smallholder and community woodlots in the Philippines : methods, initial findings and insights

This paper details the processes and challenges involved in collecting inventory data from smallholder and community woodlots on Leyte Island, Philippines. Over the period from 2005 through to 2012, 253 woodlots at 170 sites were sampled as part of a large multidisciplinary project, resulting in a substantial timber inventory database. The inventory was undertaken to provide information for three separate but interrelated studies, namely (1) tree growth, performance and timber availability from private smallholder woodlots on Leyte Island; (2) tree growth and performance of mixed-species plantings of native species; and (3) the assessment of reforestation outcomes from various forms of reforestation. A common procedure for establishing plots within each site was developed and applied in each study, although the basis of site selection varied. A two-stage probability proportion to size sampling framework was developed to select smallholder woodlots for inclusion in the inventory. In contrast, community-based forestry woodlots were selected using stratified random sampling. Challenges encountered in undertaking the inventory were mostly associated with the need to consult widely before the commencement of the inventory and problems in identifying woodlots for inclusion. Most smallholder woodlots were only capable of producing merchantable volumes of less than 44 % of the site potential due to a lack of appropriate silviculture. There was a clear bimodal distribution of proportion that the woodlots comprised of the total smallholding area. This bimodality reflects two major motivations for smallholders to establish woodlots, namely timber production and to secure land tenure.