MEASURING THE EFFICIENCY OF INDEX FUNDS: EVIDENCE FROM INDIA

The purpose of this study is to analyse the technical efficiency of Index funds using data envelopment analysis (DEA) and to assess the reasons of inefficiency. Based on secondary data collected from the annual reports of the Association of Mutual Funds in India, this study examined the efficiency performance of the top Index funds available to Indian investors from the year 2018 to 2022 using radial measurers (BCC) of data envelopment analysis. The results show that the average efficiency of Index funds was 83.04 percent during the study period, and the average efficiency of index funds was almost stable during the study period. Only 10 percent of the index funds operated efficiently during the study period. The least amount of slack was found in the input "expense ratio". This reiterates that investment risk is the cause of the funds' inefficiency and not the associated expenses.  This study is first of its kind that has assessed the of Indian index funds and therefore holds important insights for regulators, policy makers and practitioners

TAKING YOU THROUGH THE BISCUIT INDUSTRY

Perhaps one of the most underrated, least talked about and low thrill industry, the biscuit business has been making waves for the last few years!Biscuits eating habit had been inculcated into the Pakistani frame work with ‘sipping of tea’ post British Raj. The biscuit, a natural complement during evening tea or in large receptions and parties has been a necessity at least in the upper income tier. The lower tier though not as heavy in consumption could not abstain (being Pakistanis’ with a sweet tooth) and with baking being a relatively easy concept (eating fresh biscuits being a necessity), the industry as expected started growing at a fast pace. This led to the sprouting of the unbranded neighborhood bakeries that made small quantities of large variety of irresistible snacks

Investigating the Nature of 3D Generalization in Deep Neural Networks

Visual object recognition systems need to generalize from a set of 2D training views to novel views. The question of how the human visual system can generalize to novel views has been studied and modeled in psychology, computer vision, and neuroscience. Modern deep learning architectures for object recognition generalize well to novel views, but the mechanisms are not well understood. In this paper, we characterize the ability of common deep learning architectures to generalize to novel views. We formulate this as a supervised classification task where labels correspond to unique 3D objects and examples correspond to 2D views of the objects at different 3D orientations. We consider three common models of generalization to novel views: (i) full 3D generalization, (ii) pure 2D matching, and (iii) matching based on a linear combination of views. We find that deep models generalize well to novel views, but they do so in a way that differs from all these existing models. Extrapolation to views beyond the range covered by views in the training set is limited, and extrapolation to novel rotation axes is even more limited, implying that the networks do not infer full 3D structure, nor use linear interpolation. Yet, generalization is far superior to pure 2D matching. These findings help with designing datasets with 2D views required to achieve 3D generalization. Code to reproduce our experiments is publicly available: https://github.com/shoaibahmed/investigating_3d_generalization.gitComment: 15 pages, 15 figures, CVPR forma

Determination of Yearly Wind Energy Potential and Extraction of Wind Energy Using Wind Turbine for Coastal Cities of Baluchistan, Pakistan

04 March, 2019 Accepted: 24 April, 2019Abstract: Wind energy assessment of Ormara, Gwadar and Lasbela wind sites which are located in provinceBaluchistan is presented. The daily averaged wind speed data for the three sites is recorded for a period of four yearsfrom 2010-2013 at mast heights 7 m, 9.6 m and 23 m. Measured wind data are extrapolated to heights 60 m (Ormara),80 m (Gwadar) and 60 m (Lasbela). Yearly averaged wind speeds are modeled using a two parameters Weibullfunction whose shape (k) and scale (c) parameters are computed using seven well known numerical iterative methods.Reliability of the fitting process is assessed by employing three goodness-of-fit test statistics, namely, RMSE, R2 and χ2tests. Tests indicate that MLE, MLM and EPFM outperformed other Weibull parameter estimation methods for a betterfit behavior. Yearly Weibull pdf and cdf are obtained and Weibull wind characteristics are determined. Wind turbinesEcotecnia 60/1.67 MW and Nordex S77 1500 kW are used to extract wind energy on yearly basis. Estimated yearlyWeibull power densities are in the range 623.00 - 700.13 W/m2, 276.04 – 307.55 W/m2 and 66.85 – 75.93 W/m2 forOrmara, Gwadar and Lasbela respectively. Extracted wind energy values for Ormara and Gwadar using wind turbinesare reported as ca. 8623 kWh and ca. 4622 kWh, respectively

Interpreting Deep Models through the Lens of Data

Identification of input data points relevant for the classifier (i.e. serve as the support vector) has recently spurred the interest of researchers for both interpretability as well as dataset debugging. This paper presents an in-depth analysis of the methods which attempt to identify the influence of these data points on the resulting classifier. To quantify the quality of the influence, we curated a set of experiments where we debugged and pruned the dataset based on the influence information obtained from different methods. To do so, we provided the classifier with mislabeled examples that hampered the overall performance. Since the classifier is a combination of both the data and the model, therefore, it is essential to also analyze these influences for the interpretability of deep learning models. Analysis of the results shows that some interpretability methods can detect mislabels better than using a random approach, however, contrary to the claim of these methods, the sample selection based on the training loss showed a superior performance.Comment: 8 pages, 11 figures, Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202