A Radial Basis Function Approach to Financial Time Series Analysis

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.

Measurements of the heat capacity of an azeotropic mixture of water and n-butanol from 78 to 320 K

Molar heat capacities of n-butanol and the azeotropic mixture in the binary system [water (x=0.716) plus n-butanol (x=0.284)] were measured with an adiabatic calorimeter in a temperature range from 78 to 320 K. The functions of the heat capacity with respect to thermodynamic temperature were established for the azeotropic mixture. A glass transition was observed at (111.9 +/- 1.1) K. The phase transitions took place at (179.26 +/- 0.77) and (269.69 +/- 0.14) K corresponding to the solid-liquid phase transitions of. n-butanol and water, respectively. The phase-transition enthalpy and entropy of water were calculated. A thermodynamic function of excess molar heat capacity with respect to temperature was established, which took account of physical mixing, destructions of self-association and cross-association for n-butanol and water, respectively. The thermodynamic functions and the excess thermodynamic ones of the binary systems relative to 298.15 K were derived based on the relationships of the thermodynamic functions and the function of the measured heat capacity and the calculated excess heat capacity with respect to temperature.

Volumetric consequences of particle loss by grading entropy.

Chemical and biological processes, such as dissolution in gypsiferous sands and biodegradation in waste refuse, result in mass or particle loss, which in turn lead to changes in solid and void phase volumes and grading. Data on phase volume and grading changes have been obtained from oedometric dissolution tests on sand–salt mixtures. Phase volume changes are defined by a (dissolution-induced) void volume change parameter (Λ). Grading changes are interpreted using grading entropy coordinates, which allow a grading curve to be depicted as a single data point and changes in grading as a vector quantity rather than a family of distribution curves. By combining Λ contours with pre- to post-dissolution grading entropy coordinate paths, an innovative interpretation of the volumetric consequences of particle loss is obtained. Paths associated with small soluble particles, the loss of which triggers relatively little settlement but large increase in void ratio, track parallel to the Λ contours. Paths associated with the loss of larger particles, which can destabilise the sand skeleton, tend to track across the Λ contours.

Pancreatic and adrenal development and function in an ovine model of polycystic ovary syndrome.

Polycystic Ovary Syndrome (PCOS) is a complex disorder encompassing reproductive and metabolic dysfunction. Ovarian hyperandrogenism is an endocrine hallmark of human PCOS. In animal models, PCOS-like abnormalities can be recreated by in utero over-exposure to androgenic steroid hormones. This thesis investigated pancreatic and adrenal development and function in a unique model of PCOS. Fetal sheep were directly exposed (day 62 and day 82 of gestation) to steroidal excesses - androgen excess (testosterone propionate - TP), estrogen excess (diethylstilbestrol - DES) or glucocorticoid excess (dexamethasone - DEX). At d90 gestation there was elevated expression of genes involved in β- cell development and function: PDX-1 (P<0.001), and INS (P<0.05), INSR (P<0.05) driven by androgenic excess only in the female fetal pancreas. β- cell numbers (P<0.001) and in vitro insulin secretion (P<0.05) were also elevated in androgen exposed female fetuses. There was a significant increase in insulin secreting β-cell numbers (P<0.001) and in vivo insulin secretion (glucose stimulated) (P<0.01) in adult female offspring, specifically associated with prenatal androgen excess. At d90 gestation, female fetal adrenal gene expression was perturbed by fetal estrogenic exposure. Male fetal adrenal gene expression was altered more dramatically by fetal glucocorticoid exposure. In female adult offspring from androgen exposed pregnancies there was increased adrenal steroidogenic gene expression and in vivo testosterone secretion (P<0.01). This highlights that the adrenal glands may contribute towards excess androgen secretion in PCOS, but such effects might be secondary to other metabolic alterations driven by prenatal androgen exposure, such as excess insulin secretion Thus there may be dialogue between the pancreas and adrenal gland, programmed during early life, with implications for adult health Given both hyperinsulinaemia and hyperandrogenism are common features in PCOS, we suggest that their origins may be at least partially due to altered fetal steroidal environments, specifically excess androgenic stimulation

Domain 4: The Education Function.

This chapter examines the role of the advanced nurse practitioner (ANP) within the domains of practice identified by the Royal College of Nursing (2002) as the teaching and coaching function. (Note that this is referred to by the NMC as the education function. It approaches the analysis against the backdrop of three policy documents: The Expert Patient: a new approach to chronic disease management for the 21st century(DoH 2001), Choosing Health: making healthy choices easier (DoH 2004), Our health, our care, our say (DoH 2006). It draws into the frame the experiences of ANP students as they work with patients, clients and carers, with the intention of enabling health and managing illness. It uses examples from a range of everyday practice setting to illustrate the inherent challenges of the teaching and coaching function of the ANP, at the same time as recognising its significance if patients, clients and carers are to be enabled to make choices that might optimize their well-being. Before this, however, some statistics are presented to focus thinking on why education is an invaluable component of advanced nursing practice.

Muscle function and body composition profile in adolescents withrestrictive anorexia nervosa: Does resistance training help?

The aim of this study was to analyze the effects of short-term resistance training on the body composition profile and muscle function in a group of Anorexia Nervosa restricting type (AN-R) patients. The sample consisted of AN-R female adolescents (12.8 ± 0.6 years) allocated into the control and intervention groups (n¼18 each). Body composition and relative strength were assessed at baseline, after 8 weeks and 4 weeks following the intervention. Body mass index (BMI) increased throughout the study (p = 0.011). Significant skeletal muscle mass (SMM) gains were found in the intervention group (p = 0.045, d = 0.6) that correlated to the change in BMI (r = 0.51, p < 0.031). Meanwhile, fat mass (FM) gains were significant in the control group (p = 0.047, d = 0.6) and correlated (r > 0.60) with change in BMI in both the groups. Significant relative strength increases (p < 0.001) were found in the intervention group and were sustained over time.

Confirmation of Data Mining Based Predictions of Protein Function

King, R. D. and Wise, P. H. and Clare, A. (2004) Confirmation of Data Mining Based Predictions of Protein Function. Bioinformatics 20(7), 1110-1118

Predicting gene function in Saccharomyces cerevisiae

Clare, A. and King R.D. (2003) Predicting gene function in Saccharomyces cerevisiae. 2nd European Conference on Computational Biology (ECCB '03). (published as a journal supplement in Bioinformatics 19: ii42-ii49)

Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

Q. Meng and M. H Lee, Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks, Connection Science, 19(1), pp 25-52, 2007.

Error-driven active learning in growing radial basis function networks for early robot learning

Q. Meng and M.H. Lee, 'Error-driven active learning in growing radial basis function networks for early robot learning', 2006 IEEE International Conference on Robotics and Automation (IEEE ICRA 2006), 2984-90, Orlando, Florida, USA.

Limits of ventricular function: from athlete's heart to a failing heart

The interest in the study of ventricular function has grown considerably in the last decades. In this review, we analyse the extreme values of ventricular function as obtained with Doppler echocardiography. We mainly focus on the parameters that have been used throughout the history of Doppler echocardiography to assess left ventricular (LV) systolic and diastolic function. The ‘athlete's heart’ would be the highest expression of ventricular function whereas its lowest expression is represented by the failing heart, independently from the original aetiology leading to this condition. There are, however, morphological similarities (dilation and hypertrophy) between the athlete's and the failing heart, which emerge as physiological and pathophysiological adaptations, respectively. The introduction of new assessment techniques, specifically speckle tracking, may provide new insight into the properties that determine ventricular filling, specifically left ventricular twisting. The concept of ventricular function must be always considered, although it may not be always possible to distinguish the normal heart of sedentary individuals from that of highly trained hearts based solely on echocardiographic or basic studies.

The function of the self-attention network

This commentary links Humphrey and Sui’s proposed Self-attention Network (SAN) to the memory advantage associated with self-relevant information (i.e., the self-reference effect). Articulating this link elucidates the functional quality of the SAN in ensuring that information of potential importance to self is not lost. This adaptive system for self-processing mirrors the cognitive response to threat stimuli, which also elicit attentional biases and produce characteristically enhanced, episodic representations in memory. Understanding the link between the SAN and memory is key to comprehending more broadly the operation of the self in cognition.

The effects of ageing and exercise on skeletal muscle structure and function

Musculoskeletal ageing is associated with profound morphological and functional changes that increase fall risk and disease incidence and is characterised by age-related reductions in motor unit number and atrophy of muscle fibres, particularly type II fibres. Decrements in functional strength and power are relatively modest until the 6th decade, after which the rate of loss exponentially accelerates, particularly beyond the 8th decade of life. Physical activity is a therapeutic modality that can significantly attenuate age-related decline. The underlying signature of ageing, as manifested by perturbed redox homeostasis, leads to a blunting of acute and chronic redox regulated exercise adaptations. Impaired redox regulated exercise adaptations are mechanistically related to altered exercise-induced reactive oxygen and nitrogen species generation and a resultant failure to properly activate redox regulated signaling cascades. Despite the aforementioned specific impairment in redox signaling, exercise induces a plethora of beneficial effects, irrespective of age. There is, therefore, strong evidence for promoting regular physical exercise, especially progressive resistance training as a lifelong habitual practice.

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Entropy Loss is Maximal for Uniform Inputs

A secure sketch (defined by Dodis et al.) is an algorithm that on an input w produces an output s such that w can be reconstructed given its noisy version w' and s. Security is defined in terms of two parameters m and m˜ : if w comes from a distribution of entropy m, then a secure sketch guarantees that the distribution of w conditioned on s has entropy m˜ , where λ = m−m˜ is called the entropy loss. In this note we show that the entropy loss of any secure sketch (or, more generally, any randomized algorithm) on any distribution is no more than it is on the uniform distribution.