Indian Institute of Management Ahmedabad

About: Indian Institute of Management Ahmedabad is a education organization based out in Ahmedabad, India. It is known for research contribution in the topics: Emerging markets & Population. The organization has 1828 authors who have published 4011 publications receiving 59269 citations. The organization is also known as: IIMA & IIM Ahmedabad.

Taking stock of national climate policies to evaluate implementation of the Paris Agreement

Abstract: Many countries have implemented national climate policies to accomplish pledged Nationally Determined Contributions and to contribute to the temperature objectives of the Paris Agreement on climate change. In 2023, the global stocktake will assess the combined effort of countries. Here, based on a public policy database and a multi-model scenario analysis, we show that implementation of current policies leaves a median emission gap of 22.4 to 28.2 GtCO2eq by 2030 with the optimal pathways to implement the well below 2 °C and 1.5 °C Paris goals. If Nationally Determined Contributions would be fully implemented, this gap would be reduced by a third. Interestingly, the countries evaluated were found to not achieve their pledged contributions with implemented policies (implementation gap), or to have an ambition gap with optimal pathways towards well below 2 °C. This shows that all countries would need to accelerate the implementation of policies for renewable technologies, while efficiency improvements are especially important in emerging countries and fossil-fuel-dependent countries.

ALGORITHM 643: FEXACT: a FORTRAN subroutine for Fisher's exact test on unordered r×c contingency tables

Abstract: The computer code for Mehta and Patel's (1983) network algorithm for Fisher's exact test on unordered r×c contingency tables is provided. The code is written in double precision FORTRAN 77. This code provides the fastest currently available method for executing Fisher's exact test, and is shown to be orders of magnitude superior to any other available algorithm. Many important details of data structures and implementation that have contributed crucially to the success of the network algorithm are recorded here.

Pruning strategies for mining high utility itemsets

Abstract: Presents an efficient high utility mining method.Employs novel pruning strategies to limit the search space of utility mining.Compares the proposed method against a state-of-the-art utility mining method.Experimentally evaluates the system on eight real and synthetic benchmark datasets.Empirical results are found to be quite promising, especially for sparse transactional databases. High utility itemset mining problem involves the use of internal and external utilities of items (such as profits, margins) to discover interesting patterns from a given transactional database. It is an extension of the basic frequent itemset mining problem and is proven to be considerably hard and intractable. This is due to the lack of inherent structural properties of high utility itemsets that can be exploited. Several heuristic methods have been suggested in the literature to limit the large search space. This paper aims to improve the state-of-the-art and proposes a high utility mining method that employs novel pruning strategies. The utility of the proposed method is demonstrated through rigorous experimentation on several real and synthetic benchmark sparse and dense datasets. A comparative evaluation of the method against a state-of-the-art method is also presented. Our experimental results reveal that the proposed method is very effective in pruning unpromising candidates, especially for sparse transactional databases.