Securities Pricing with Information-Sensitive Discounting

In this paper incomplete-information models are developed for the pricing of securities in a stochastic interest rate setting. In particu- lar we consider credit-risky assets that may include random recovery upon default. The market filtration is generated by a collection of information processes associated with economic factors, on which in- terest rates depend, and information processes associated with mar- ket factors used to model the cash flows of the securities. We use information-sensitive pricing kernels to give rise to stochastic interest rates. Semi-analytical expressions for the price of credit-risky bonds are derived, and a number of recovery models are constructed which take into account the perceived state of the economy at the time of default. The price of European-style call bond options is deduced, and it is shown how examples of hybrid securities, like inflation-linked credit-risky bonds, can be valued. Finally, a cumulative information process is employed to develop pricing kernels that respond to the amount of aggregate debt of an economy.Asset pricing, incomplete information, stochastic interest rates, credit risk, recovery models, credit-inflation hybrid securities, information-sensitive pricing kernels

An investigation into the use of smart grid technologies for the Northern Cape Province of South Africa, where extensive solar generation is planned in a constrained network

Large scale distributed generation is expected to be implemented in South Africa for the first time due to government initiated programmes aimed at increasing the utilization of renewable energy resources for power generation. Solar photovoltaic distributed generation is proposed in the vast majority of renewable energy power generation independent power producer applications received in the Northern Cape of South Africa; therefore the integration of these units into the distribution networks presents a great challenge. The current distribution network was not originally designed for integration of distributed generation at such high penetration levels. Expensive and lengthy network reinforcement and strengthening projects will need to be implemented in the distribution network to facilitate safe and reliable interconnection of distributed generation at higher penetration levels. This report gathers and documents the major challenges such as: voltage regulation, equipment thermal ratings, power quality, and protection coordination related to solar photovoltaic distributed generation interconnection to constrained distribution networks. Smart grid technologies which enable higher penetration of distributed generation and provide a cost-effective alternative to network strengthening are investigated. Based on the South African context, a Smart Grid technology approach is proposed and the Smart Grid technologies which offer tangible and direct benefits in the short term are prioritised for implementation

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using intuitive toy examples as well as medical tasks for treating sepsis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than simpler L1 or L2 penalties without sacrificing predictive power.Comment: To appear in AAAI 2018. Contains 9-page main paper and appendix with supplementary materia

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects. Generative approaches to computer vision allow us to overcome this difficulty by explicitly modeling the physical image formation process. Using generative object models, the analysis of an observed image is performed via Bayesian inference of the posterior distribution. This conceptually simple approach tends to fail in practice because of several difficulties stemming from sampling the posterior distribution: high-dimensionality and multi-modality of the posterior distribution as well as expensive simulation of the rendering process. The main difficulty of sampling approaches in a computer vision context is choosing the proposal distribution accurately so that maxima of the posterior are explored early and the algorithm quickly converges to a valid image interpretation. In this work, we propose to use a Bayesian Neural Network for estimating an image dependent proposal distribution. Compared to a standard Gaussian random walk proposal, this accelerates the sampler in finding regions of the posterior with high value. In this way, we can significantly reduce the number of samples needed to perform facial image analysis.Comment: Accepted to the Bayesian Deep Learning Workshop at NeurIPS 201

Combining Kernel and Model Based Learning for HIV Therapy Selection

We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in patient data makes it difficult for one particular model to succeed at providing suitable therapy predictions for all patients. An appropriate means for addressing this heterogeneity is through combining kernel and model-based techniques. These methods capture different kinds of information: kernel-based methods are able to identify clusters of similar patients, and work well when modelling the viral response for these groups. In contrast, model-based methods capture the sequential process of decision making, and are able to find simpler, yet accurate patterns in response for patients outside these groups. We take advantage of this information by proposing a mixture-of-experts model that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual. Overall, we verify that therapy combinations proposed using this approach significantly outperform previous methods

A step in the right direction: an analysis of the viability of a specialised intellectual property court in South Africa.

No abstract available

An audit of biopsy proven minimal change nephrotic syndrome in children at Chris Hani Baragwanath academic hospital

A Research Report submitted to the Faculty of Health Sciences, University of the Witwatersrand, in partial fulfillment of the requirements for the degree Of Master of Medicine Johannesburg, 2016Objective: To evaluate the clinicopathological features, response to treatment and outcomes in children presenting to Chris Hani Baragwanath Academic Hospital with biopsy proven minimal change nephrotic syndrome. Methods: A retrospective record review was conducted. Available records of children, between the ages of 1 and 14 years, who had nephrotic syndrome clinically and who were proven to be minimal change nephrotic syndrome on renal biopsy, were studied. Children who presented from January 1996 to December 2010 were included. Their demographics, clinical features on presentation, biopsy results, management and outcomes were studied. Results: In the 15 year period there were 129 (29% of all NS) children with minimal change nephrotic syndrome. Seventeen patients were excluded because 4 were not biopsied and 13 patients’ records could not be traced. The remaining 112 patients were included in the study. Ages ranged from 1 year to 13.6 years with a median age of 3.8 years (IQR 2.6-5.9). There was a male predominance, with 72 males and 40 females (1.8:1). The majority of the children studied were Black African (89.3%). On presentation 68.8% had microscopic haematuria. Although 59.8% had a blood pressure at presentation which was above the 95th centile for gender, height and age, only 33.9% had sustained hypertension. On initial biopsy, 34% were found to have the mesangial hypercellular variant of minimal change disease and 6% had the IgM variant of minimal change disease. Two patients went into spontaneous remission. The remainder, were treated with oral corticosteroids. Of those treated, 58.9% were steroid responsive, 19.6% were steroid resistant and 8.9% were initially responsive but subsequently became steroid resistant. Of the sample, 22.3% were steroid dependent and 16.1% were frequent relapsers. Second line immunosuppressive therapy was needed in 38 (33.9%) patients. The three second line immunosuppressant agents used were intravenous pulsed cyclophosphamide (28.5%), intravenous pulsed methyl prednisolone (9.8%) and mycophenolate mofetil (MMF) (7.1%). Repeat biopsies were performed on 22 children (19.6%). Four of the 22 repeat biopsies showed focal segmental glomerular sclerosis (FSGS).The average length of follow up was 4.86 years (median 3.58). At the last visit, 75.9% of the study group was in remission. During the course of follow up, 41.1% were admitted to hospital for a suspected bacterial infection. A high proportion of patients were lost to follow up (62%). The mortality rate was 1.8%. Conclusion: At Chris Hani Baragwanath Academic Hospital, all children with nephrotic syndrome are biopsied prior to initiating steroid therapy due to the high prevalence of tuberculosis infection and poor compliance in our population. This practice has highlighted differences between the children in our population with minimal change disease compared to that reported by the International Study of Kidney Disease in Children (ISKDC). In our study there were a higher proportion of children with initial hypertension and haematuria, and fewer children that responded to steroid therapy. This differs from the ISKDC findings in 1978. Their study had predominantly Caucasian children, and our study had predominantly Black African children. These differences in ethnicity may account for the differences.MT201

The Veiled Lady Fungus

This semester I studied the Stinkhorn mushroom Phallus Indusiatus. The plan of this research is to develop a protocol for growing this fungus and using it in collaboration with research on it’s web-like properties of the unique veil produced by the fruiting body. This will be distributed to teams of engineers as well as NASA for Dr. Penick’s research. Due to logistics of receiving spores from across the world, we still have not been able to begin growing these mushrooms. However, I have described a protocol that we will follow in order to grow. The protocol contains detailed descriptions on how and when to add certain spawn materials or substrates. It also describes when it is necessary to supplement FAE (fresh air exchange) as well as optimal temperature and humidity settings for best possible growth. I developed this protocol through research online as well as personal experience with cultivating oyster mushrooms, Pleurotus ostreatus. I have also written a presentation regarding this species which Dr. Penick will present to his NASA collaborators. This presentation includes general information like background information, where this species is found in nature, the history of its use in humans (medicinal as well as culinary), speculation for its web-like veil, or insidium, how it has been previously cultivated as well as how it is currently cultivated, and the history of it’s phylogeny