Optimal portfolio and spending rules for endowment funds

We investigate the role of different spending rules in a dynamic asset allocation model for university endowment funds. In particular, we consider the fixed consumption-wealth ratio (CW) rule and the hybrid rule which smoothes spending over time. We derive the optimal portfolios under these two strategies and compare them with a theoretically optimal (Merton) strategy. We show that the optimal portfolio with habit is less risky compared to the optimal portfolio without habit. A calibrated numerical analysis on U.S. data shows, similarly, that the optimal portfolio under the hybrid strategy is less risky than the optimal portfolios under both the CW and the classical Merton strategies, in typical market conditions. Our numerical analysis also shows that spending under the hybrid strategy is less volatile than the other strategies. Thus, endowments following the hybrid spending rule use asset allocation to protect spending. However, in terms of the endowment’s wealth, the hybrid strategy comparatively outperforms the conventional Merton and CW strategies when the market is highly volatile but under-performs them when there is strong stock market growth and low volatility. Overall, the hybrid strategy is effective in terms of stability of spending and intergenerational equity because, even if it allows short-term fluctuation in spending, it ensures greater stability in the long run

Wavelet based segmentation of hyperspectral colon tissue imagery

Segmentation is an early stage for the automated classification of tissue cells between normal and malignant types. We present an algorithm for unsupervised segmentation of images of hyperspectral human colon tissue cells into their constituent parts by exploiting the spatial relationship between these constituent parts. This is done by employing a modification of the conventional wavelet based texture analysis, on the projection of hyperspectral image data in the first principal component direction. Results show that our algorithm is comparable to other more computationally intensive methods which exploit the spectral characteristics of the hyperspectral imagery data

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Feature detection from echocardiography images using local phase information

Ultrasound images are characterized by their special speckle appearance, low contrast, and low signal-to-noise ratio. It is always challenging to extract important clinical information from these images. An important step before formal analysis is to transform the image to significant features of interest. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant and thus suitable for ultrasound images. We extend the previous local phase-based method to detect features using the local phase computed from monogenic signal which is an isotropic extension of the analytic signal. We apply our method of multiscale feature-asymmetry measurement and local phase-gradient computation to cardiac ultrasound (echocardiography) images for the detection of endocardial, epicardial and myocardial centerline

Factors Influencing Students' Learning at KASB Institute of Technology

The research article looks into the psychological and other characteristics that play a role in students’ learning ability. In all the observations we have found some students performing better than the others, this display of performance in their studies implies the presence of certain factors which are different from others or play a role in their better learning capabilities. These factors may be present in students, teachers, institutions and others. This article is an attempt to highlight those factors which may be required on part of the students, teachers, institutions and others that may or may not play a significant role in enhancing students’ learning capabilities, the sample of 103 is used to infer the significance of these factors. Through research we were able to answer as per students, punctuality of the teacher is somewhat important in enhancing learning. Clarity of speech was considered an insignificant feature. The most preferred quality of the teacher which is responsible for ranking a teacher as the best teacher is cooperativeness. Another finding was the relationship between CGPA obtained and consulting teacher outside class, which we concluded that there is a strong relationship between consulting teacher and obtaining good CGPA. Lastly we found that time spend in library has no significant association with understanding of topic when taught.Students’ Learning, Students and Teacher Characteristics

Advertising Styles’ Impact on Attention in Pakistan

The topic was selected after giving consideration to the modern environment and the use of media by advertisers for attention purposes of their products. It was also observed that the number of channels especially in the electronic media have also geometrically increased over the last two decades. It is now becoming difficult for advertisers to get the attention of their products in the minds of their viewers. The methodology used in the research was focus group and ads of different products were shown to them which included humorous and serious appeals. As the literature review revealed that these two types of appeals have significant difference when measuring attention between humorous and serious advertisements. At the end of the research it was established that there is a significant difference between the attention of humorous and serious appeals. Initially the idea was taken form a research conducted in Sweden. Same parameters were analyzed in Pakistan. We concluded that the reaction of two different societies have almost the same response for humorous and serious advertising appeals.Humorous Appeals, Serious Appeals, Attention

Some Quadratic Transformations and Reduction Formulas associated with Hypergeometric Functions

In this paper, we construct four summation formulas for terminating Gauss’ hypergeometric series having argument “two and with the help of our summation formulas. We establish two quadratic transformations for Gauss’ hypergeometric function in terms of finite summation of combination of two Clausen hypergeometric functions. Further, we have generalized our quadratic transformations in terms of general double series identities as well as in terms of reduction formulas for Kampé de Fériet’s double hypergeometric function. Some results of Rathie-Nagar, Kim et al. and Choi-Rathie are also obtained as special cases of our findings