Role of plant functional traits in determining vegetation composition of abandoned grazing land in north-eastern Victoria, Australia

Question: In the Northern Hemisphere, species with dispersal limitations are typically absent from secondary forests. In Australia, little is known about dispersal mechanisms and other traits that drive species composition within post-agricultural, secondary forest. We asked whether mode of seed dispersal, nutrient uptake strategy, fire response, and life form in extant vegetation differ according to land-use history. We also asked whether functional traits of Australian species that confer tolerance to grazing and re-colonisation potential differ from those in the Northern Hemisphere. Location: Delatite Peninsula, NE Victoria, Australia. Methods: The vegetation of primary and secondary forests was surveyed using a paired-plot design. Eight traits were measured for all species recorded. ANOSIM tests and Non-metric Multi-dimensional Scaling were used to test differences in the abundance of plant attributes between land-use types. Results: Land-use history had a significant effect on vegetation composition. Specific leaf area (SLA) proved to be the best predictor of response to land-use change. Primary forest species were typically myrmecochorous phanerophytes with low SLA. In the secondary forest, species were typically therophytes with epizoochorous dispersal and high SLA. Conclusions: The attributes of species in secondary forests provide tolerance to grazing suggesting that disturbance caused by past grazing activity determined the composition of these forests. Myrmecochores were rare in secondary forests, suggesting that species had failed to re-colonise due to dispersal limitations. Functional traits that resulted in species loss through disturbance and prevented re-colonisation were different to those in the Northern Hemisphere and were attributable to the sclerophyllous nature of the primary forest

PDDL: A language with a purpose?

In order to make planning technology more accessible and usable the planning community may have to adopt standard notations for embodying symbolic models of planning domains. In this paper it is argued that before we design such languages for planning we must be able to evaluate their quality. In other words, we must clear for what purpose the languages are to be used, and by what criteria the languages’ effectiveness are to be judged. Here some criteria are set down for languages used for theoretical and practical purposes respectively. PDDL is evaluated with respect to them, with differing results depending on whether PDDL’s purpose is to be a theoretical or practical language. From the results of these evaluations some conclusions are drawn for the development of standard languages for AI planning

On Weight Matrix and Free Energy Models for Sequence Motif Detection

The problem of motif detection can be formulated as the construction of a discriminant function to separate sequences of a specific pattern from background. In computational biology, motif detection is used to predict DNA binding sites of a transcription factor (TF), mostly based on the weight matrix (WM) model or the Gibbs free energy (FE) model. However, despite the wide applications, theoretical analysis of these two models and their predictions is still lacking. We derive asymptotic error rates of prediction procedures based on these models under different data generation assumptions. This allows a theoretical comparison between the WM-based and the FE-based predictions in terms of asymptotic efficiency. Applications of the theoretical results are demonstrated with empirical studies on ChIP-seq data and protein binding microarray data. We find that, irrespective of underlying data generation mechanisms, the FE approach shows higher or comparable predictive power relative to the WM approach when the number of observed binding sites used for constructing a discriminant decision is not too small.Comment: 23 pages, 1 figure and 4 table

Predicting Phishing Websites using Neural Network trained with Back-Propagation

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates

On the Online Generation of Effective Macro-operators

Macro-operator (“macro”, for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training

Some empirical evidences on ASEAN 5 fiscal policy regime and monetary and fiscal policy interactions

The interest of common currency among Asian countries have spurred many events happening for the past few years, notably the declaration of Asian Currency Unit in 2006 by Asia Development Bank (ADB). Hence, research papers examining on the integration of monetary policies are abundance. However, paper on examining fiscal policy regime and interaction between monetary and fiscal policy on ASEAN countries, is lacking. The success of monetary union relies on the price stability of member nations. However, joining a monetary union means the lost of monetary policy sovereignty. Therefore, fiscal policy turns to be the next important tool to maintain price stability. This is reflected from the EMU countries after year 1999, where national monetary policies are completely centralized to the European Central Bank (ECB). The European System of Central Banks (ESCB) combines unity of decisions with participation of national central banks in the decision making process and implementation. Nevertheless, national fiscal policies of the member countries are still in the hands of the national governments. This paper intents to examine the type of fiscal policy regime practiced by ASEAN 5 countries. Using macro-economic data for Indonesia, Malaysia, Philippines, Singapore and Thailand, the interrelationship of government surplus/deficits and liabilities is analyzed using Correlation test, Vector Auto-regression (VAR) and Impulse response (IR) function to determine whether a Ricardian or Non-Ricardian fiscal policy has been implemented. Also, comparison of monetary and fiscal policy interactions between some EMU countries and ASEAN 5 are made. The results indicate interactions among inter EMU countries and inter ASEAN countries are generally comparable

A numerical study of radial basis function based methods for option pricing under one dimension jump-diffusion model

The aim of this paper is to show how option prices in the Jump-diffusion model can be computed using meshless methods based on Radial Basis Function (RBF) interpolation. The RBF technique is demonstrated by solving the partial integro-differential equation (PIDE) in one-dimension for the Ameri- can put and the European vanilla call/put options on dividend-paying stocks in the Merton and Kou Jump-diffusion models. The radial basis function we select is the Cubic Spline. We also propose a simple numerical algorithm for finding a finite computational range of a global integral term in the PIDE so that the accuracy of approximation of the integral can be improved. Moreover, the solution functions of the PIDE are approximated explicitly by RBFs which have exact forms so we can easily compute the global intergal by any kind of numerical quadrature. Finally, we will also show numerically that our scheme is second order accurate in spatial variables in both American and European cases

On the Effective Configuration of Planning Domain Models

The development of domain-independent planners within the AI Planning community is leading to “off the shelf” technology that can be used in a wide range of applications. Moreover, it allows a modular approach – in which planners and domain knowledge are modules of larger software applications – that facilitates substitutions or improvements of individual modules without changing the rest of the system. This approach also supports the use of reformulation and configuration techniques, which transform how a model is represented in order to improve the efficiency of plan generation. In this paper, we investigate how the performance of planners is affected by domain model configuration. We introduce a fully automated method for this configuration task, and show in an extensive experimental analysis with six planners and seven domains that this process (which can, in principle, be combined with other forms of reformulation and configuration) can have a remarkable impact on performance across planners. Furthermore, studying the obtained domain model configurations can provide useful information to effectively engineer planning domain models