On the discrete time dynamics of a self-stabilizing MCA learning algorithm

The stability of minor component analysis (MCA) learning algorithms is an important problem in many signal processing applications. In this paper, we propose an effective MCA learning algorithm that can offer better stability. The dynamics of the proposed algorithm are analyzed via a corresponding deterministic discrete time (DDT) system. It is proven that if the learning rate satisfies some mild conditions, almost all trajectories of the DDT system starting from points in an invariant set are bounded, and will converge to the minor component of the autocorrelation matrix of the input data. Simulation results will be furnished to illustrate the theoretical results achieved.

Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in practice. While inherently insensitive to visible spectrum illumination changes, IR data introduces specific challenges of its own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, when comparing two thermal IR images of faces, we mutually normalize their poses and facial expressions by using an active appearance model (AAM) to generate synthetic images of the two faces with a neutral facial expression and in the same view (the average of the two input views). This is achieved by piecewise affine warping which follows AAM fitting. A major contribution of our work is the use of an AAM ensemble in which each AAM is specialized to a particular range of poses and a particular region of the thermal IR face space. Combined with the contributions from our previous work which addressed the problem of reliable AAM fitting in the thermal IR spectrum, and the development of a person-specific representation robust to transient changes in the pattern of facial temperature emissions, the proposed ensemble framework accurately matches faces across the full range of yaw from frontal to profile, even in the presence of scale variation (e.g. due to the varying distance of a subject from the camera). The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces and a newly acquired data set of thermal IR motion videos. Our approach achieved perfect recognition performance on both data sets, significantly outperforming the current state of the art methods even when they are trained with multiple images spanning a range of head views.

Face set classification using maximally probable mutual modes

In this paper we consider face recognition from sets of face images and, in particular, recognition invariance to illumination. The main contribution is an algorithm based on the novel concept of maximally probable mutual modes (MMPM). Specifically: (i) we discuss and derive a local manifold illumination invariant and (ii) show how the invariant naturally leads to a formulation of "common modes" of two face appearance distributions. Recognition is then performed by finding the most probable mode, which is shown to be an eigenvalue problem. The effectiveness of the proposed method is demonstrated empirically on a challenging database containing the total of 700 video sequences of 100 individuals

The association between the investment opportunity set and corporate financing and dividend decisions: some Australian evidence

Summarizes previous research on the investment opportunity set (IOS) using price-based and investment-based proxies and variance measures; and develops hypotheses on the relationship between IOS, debt/equity ratios and dividend policies. Tests them on 1990-1998 data from listed Australian companies and explains the methodology, which builds on Gover and Gover (1993) by including more recent proxy variables. Finds no significant results from low growth firms, although some high growth firms show lower debt/equity ratios and dividends. Questions the robustness of existing IOS proxies in the Australian context and calls for further research.

Selected Ranked Set Sampling

This paper proposes a sampling procedure called selected ranked set sampling (SRSS), in which only selected observations from a ranked set sample (RSS) are measured. This paper describes the optimal linear estimation of location and scale parameters based on SRSS, and for some distributions it presents the required tables for optimal selections. For these distributions, the optimal SRSS estimators are compared with the other popular simple random sample (SRS) and RSS estimators. In every situation the estimators based on SRSS are found advantageous at least in some respect, compared to those obtained from SRS or RSS. The SRSS method with errors in ranking is also described. The relative precision of the estimator of the population mean is investigated for different degrees of correlations between the actual and erroneous ranking. The paper reports the minimum value of the correlation coefficient between the actual and the erroneous ranking required for achieving better precision with respect to the usual SRS estimator and with respect to the RSS estimator.

Sensory adaptability of foods with added Lupin (Lupinus angustifolius) kernel fibre using pre-set criteria

Fiber-enriched white bread, muffin. pasta, orange juice, and breakfast bar were prepared with lupin (Lupin us angusti/olius) kernel fiber. Consumer panelists (n = 44) determined that all these fiber-enriched foods, except orange juice, fulfilled pre-set acceptability criteria. Fiber enrichment did not change overall acceptability (p> 0.05) of the bread and pasta, but reduced overall acceptability (p < 0.05) of the muffin, orange juice, and breakfast bar. In all fiber-enriched products, flavor was the attribute most highly correlated with overall acceptability (p < 0.05). The lupin kernel fiber used in this study therefore appears to have potential as a 'nonintrusive' ingredient in some processed cereal-based foods_ For other applications, fiber modification appears worthy of investigation to accomplish 'nonintrusive' fiber enrichment.

A set of patterns for the web-based interfaces of an employee services system

The provision of Human Resource (HR), especially payroll, is a core function in every organization. Previously, providers of HR/payroll have offered their services to their clients via conventional modes of communication, such as telephones, facsimile, and courier services. In recent years, with the advent of the Internet and the emergence of web-based electronic commerce, there has been a rise in the adoption of web-based technology and information
systems by service providers, thereby enabling them to interact with their clients through this medium. This development necessitates the use of web-based user interfaces as workspaces between the HR/payroll providers and their clients, and thus, raises certain concerns that determine the effectiveness of web-based workflow systems. These concerns, related to the use of web interfaces, form the basis of the patterns discussed in this paper

An email classification model based on rough set theory

The communication via email is one of the most popular services of the Internet. Emails have brought us great convenience in our daily work and life. However, unsolicited messages or spam, flood our email boxes, which results in bandwidth, time and money wasting. To this end, this paper presents a rough set based model to classify emails into three categories - spam, no-spam and suspicious, rather than two classes (spam and non-spam) in most currently used approaches. By comparing with popular classification methods like Naive Bayes classification, the error ratio that a non-spam is discriminated to spam can be reduced using our proposed model.

Determination of all syzygies for the dependent polynomial invariants of the Riemann Tensor. III. mixed invariants of arbitrary degree in the Ricci Spinor

In this paper, we rigorously prove that the complete set of Riemann tensor invariants given by Sneddon [J. Math. Phys. 40, 5905 (1999)] is both minimal and complete. Furthermore, we provide a two-stage algorithm for the explicit construction of polynomial syzygies relating any dependent Riemann tensor invariant to members of the complete set.

The Permian-Triassic Boundary Stratigraphic Set: characteristics and correlation

Eased on field observations and compilation of data, a new stratigraphic concept, herein named the Permian-Triassic boundary stratigraphic set (PTBST), is proposed. The PTBST consists of, in ascending order, beds of claystone, limestone (or marl) and claystone. This boundary stratigraphic
set has been recognized at many sections in the Yangtze region of South China, with laterally stable lithological characters, the same or comparable biotas, comparable radiometric ages, and identical or similar magnetostratigraphic and chemostratigraphic signals. Therefore, the PTBST marks an isochronous unit and can serve as an important and effective marker set for regional and global correlations.
The important index fossils for the lowermost Triassic, Hindeodus parvus or Claraia, may be diachronous in their first occurrences with respect to the base of the PTBST and, therefore, should not be used as an exclusive indicator for the beginning of the Triassic. Rather more attention should
be paid to events, succession of events and/or event surfaces, which would potentially provide a more precise tool for high-resolution stratigraphic division and correlation.

Some aspects of photosynthetic characteristics in a set of perennial irrigation reservoirs located in five river basins in Sri Lanka

Phytoplankton primary productivity of eleven irrigation reservoirs located in five river basins in Sri Lanka was determined on a single occasion together with light climate and nutrient concentrations. Although area-based gross primary productivity (1.43–11.65 g O2 m−2 d−1) falls within the range already established for tropical water bodies, net daily rate was negative in three water bodies. Light-saturated optimum rates were found in water bodies, with relatively high algal biomass, but photosynthetic efficiency or specific rates were higher in water bodies with low algal biomass, indicating nutrient limitation or physiological adaptation of phytoplankton. Concentrations of micronutrients and algal biomass in the reservoirs are largely altered by high flushing rate resulting from irrigation release. Underwater light climate and nutrient availability control the rate of photosynthesis and subsequent areabased primary production to a great extent. However, morpho-edephic index or euphotic algal biomass in the most productive stratum of the water column is not a good predictor of photosynthetic capacity or daily rate of primary production of these shallow tropical irrigation reservoirs.

On the size of training set and the benefit from ensemble

In this paper, the impact of the size of the training set on the benefit from ensemble, i.e. the gains obtained by employing ensemble learning paradigms, is empirically studied. Experiments on Bagged/ Boosted J4.8 decision trees with/without pruning show that enlarging the training set tends to improve the benefit from Boosting but does not significantly impact the benefit from Bagging. This phenomenon is then explained from the view of bias-variance reduction. Moreover, it is shown that even for Boosting, the benefit does not always increase consistently along with the increase of the training set size since single learners sometimes may learn relatively more from additional training data that are randomly provided than ensembles do. Furthermore, it is observed that the benefit from ensemble of unpruned decision trees is usually bigger than that from ensemble of pruned decision trees. This phenomenon is then explained from the view of error-ambiguity balance.