Computational immunology for anomaly detection

The thesis makes a significant contribution to the issue of anomaly detection by introducing a computational immunology approach. Immunity-based anomaly detection in high dimensional space is systematically investigated and the proposed hybrid method (combining data mining techniques and computational immunology) improves both accuracy and efficiency.

Computational investigations on periodic trends in Group 14 chemistry

Describes the use of computer-aided molecular modelling to investigate trends in the chemistry of the Group 14 elements, namely carbon, silicon, germanium, tin and lead. The chemical behaviour of two classes of molecules containing Group 14 elements was related to trends in the fundamental properties of these elements.

Dots versus antidots : computational exploration of structure, magnetism, and half-metallicity in boron-nitride nanostructures

Triangle-shaped nanohole, nanodot, and lattice antidot structures in hexagonal boron-nitride (h-BN) monolayer sheets are characterized with density functional theory calculations utilizing the local spin density approximation. We find that such structures may exhibit very large magnetic moments and associated spin splitting. N-terminated nanodots and antidots show strong spin anisotropy around the Fermi level, that is, half-metallicity. While B-terminated nanodots are shown to lack magnetism due to edge reconstruction, B-terminated nanoholes can retain magnetic character due to the enhanced structural stability of the surrounding two-dimensional matrix. In spite of significant lattice contraction due to the presence of multiple holes, antidot super lattices are predicted to be stable, exhibiting amplified magnetism as well as greatly enhanced half-metallicity. Collectively, the results indicate new opportunities for designing h-BN-based nanoscale devices with potential applications in the areas of spintronics, light emission, and photocatalysis. © 2009 American Chemical Society.

A brief review of recent research trends on applications of computational and statistical techniques in financial and business intelligence

Artificial neural networks and statistical techniques like decision trees, discriminant analysis, logistic regression and survival analysis play a crucial role in Business Intelligence. These predictive analytical tools exploit patterns found in historical data to make predictions about future events. In this paper we have shown some recent developments of a few of these techniques in financial and business intelligence applications like fraud detection, bankruptcy prediction and credit rating scoring.

Computational exploration of the biological basis of Black-Scholes expected utility function

It has often been argued that there exists an underlying biological basis of utility functions. Taking this line of argument a step further in this paper, we have aimed to computationally demonstrate the biological basis of the Black-Scholes functional form as applied to classical option pricing and hedging theory. The evolutionary optimality of the classical Black-Scholes function has been computationally established by means of a haploid genetic algorithm model. The objective was to minimize the dynamic hedging error for a portfolio of assets that is built to replicate the payoff from a European multi-asset option. The functional form that is seen to evolve over successive generations which best attains this optimization objective is the classical Black-Scholes function extended to a multiasset scenario.

Computational modeling aspects of photon transport equations in biological media

This paper presents an overview of modeling light propagation through biological media by solving the photon transport equation. Different variants of the photon transport equation (PTE) are discussed. Several methods for modeling static distributions and the transient response are presented. A discussion on how to mix and match electromagnetic problems with the PTE is also provided.

Computational media aesthetics: Finding meaning beautiful

Innovative media management, annotation, delivery, and navigation services will enrich online shopping, help-desk services, and anytime-anywhere training over wireless devices. However, the semantic gap between the rich meaning that users want when they query and browse media and the shallowness of the content descriptions that one can actually compute is weakening today's automatic content-annotation systems. To address such problems, an approach that markedly departs from existing methods based on detecting and annotating low-level audio-visual features is advocated.

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

This paper concerns social learning modes and their effects on team performance. Social learning, such as by observing others' actions and their outcomes, allows members of a team to learn what other members know. Knowing what other members know can reduce task communication and co-ordination overhead, which helps the team to perform faster since members can devote their attention to their tasks. This paper describes agent-based simulation studies using a computational model that implements different social learning modes as parameters that can be controlled in the simulations. The results show that social learning from both direct and indirect observations positively contributes to learning about what others know, but the value of social learning is sensitive to prior familiarity such that minimum thresholds of team familiarity are needed to realise the benefits of social learning. This threshold increases with task complexity. These findings clarify the level of influence that sociality has on social learning and sets up a formal framework by which to conduct studies on how social context influences learning and group performance.

Fast cross-validation of kernel fisher discriminant classifiers

Given n training examples, the training of a Kernel Fisher Discriminant (KFD) classifier corresponds to solving a linear system of dimension n. In cross-validating KFD, the training examples are split into 2 distinct subsets for a number of times (L) wherein a subset of m examples is used for validation and the other subset of(n - m) examples is used for training the classifier. In this case L linear systems of dimension (n - m) need to be solved. We propose a novel method for cross-validation of KFD in which instead of solving L linear systems of dimension (n - m), we compute the inverse of an n × n matrix and solve L linear systems of dimension 2m, thereby reducing the complexity when L is large and/or m is small. For typical 10-fold and leave-one-out cross-validations, the proposed algorithm is approximately 4 and (^4/9n) times respectively as efficient as the naive implementations. Simulations are provided to demonstrate the efficiency of the proposed algorithms.

Application of computational media aesthetics methodology to extracting color semantics in film

Using film grammar as the underpinning, we study the extraction of structures in video based on color using a wide configuration of clustering methods combined with existing and new similarity measures. We study the visualisation of these structures, which we call Scene-Cluster Temporal Charts and show how it can bring out the interweaving of different themes and settings in a film. We also extract color events that filmmakers use to draw/force a viewer's attention to a shot/scene. This is done by first extracting a set of colors used rarely in film, and then building a probabilistic model for color event detection. We demonstrate with experimental results from ten movies that our algorithms are effective in the extraction of both scene-cluster temporal charts and color events.