Mortality predictor score in hospitalised patients HIV-associated TB in Africa dataset

This dataset contains details of hospital patients with HIV-associated tuberculosis in sub-Saharan Africa from the STAMP clinical trial. It was generated to derive predictor score for mortality including urine lipoarabinomannan detection. Variables include: age, sex, weight, living status, number of day in follow-up, ART treatment, WHO danger sign, Haemoglobin level, ability to walk, and urine LAM positivity

Inclusion is for Everybody : International Day of Disabled Persons

Guest Comment on International Day of Disabled Persons by Dr. Ankur Gupta, MDS (Orthodontics

Virtual Construction Simulator

In the construction sector computer simulation has been extensively incorporated to support complex decisions including automation of several different processes and also to design the novel machines or buildings The changes inhabited in the work zone configurations are reflected in the animations as the work progresses This also provides an opportunity to the construction workers as well as agency personnel and general public to visually present the complicated information This projected work presents an overview of how simulation modeling can help in learning effective decision making while performing construction activities The Virtual Construction Simulator provides a user interaction gadget through which user can feed in the inputs that addresses the system to implement those sequences of tasks The tasks that do not violate certain specified constraints operate concurrently and the operation of these tasks can be viewed on the virtual construction environment as well as the intermediate status of all the elements is updated at the backend A comparative analysis of various available alternatives can be done so as to determine the most optimal and most efficient sequence of operations that can be implemented Here in the cost of translocation of the various vehicles is taken into consideration for efficiency deterministic Some predefined constraints are accustomed to the system like the limit on the number of vehicles that can be used also the parameters involved in evaluation of the efficiency of a plan is subject to some assumptions they are an approximate to the real world attributes but they are subject to change and can be updated on demand as per the requirements of the syste

Approximation algorithms for variants of the traveling salesman problem

The traveling salesman problem, hereafter abbreviated and referred to as TSP, is a very well known NP-optimization problem and is one of the most widely researched problems in computer science. Classical TSP is one of the original NP - hard problems [1]. It is also known to be NP - hard to approximate within any factor and thus there is no approximation algorithm for TSP for general graphs, unless P = NP. However, given the added constraint that edges of the graph observe triangle inequality, it has been shown that it is possible achieve a good approximation to the optimal solution [2]. TSP has a number of variants that have been deeply researched over the years. Approximations of varying degrees have been achieved depending on the complexity presented by the problem setup. An obvious variant is that of finding a maximum weight hamiltonian tour, also informally known as the taxicab ripoff problem . The problem is not equivalent to the minimization problem when the edge weights are non-negative and does allow good approximations. Also important is the problem when the graph is not symmetric. The problem in this case, as should be expected, is slightly tougher to approximate. Another very well researched problem is when weights of edges are drawn from the set { 1, 2}. This study was focused on gaining an understanding of these algorithms keeping in mind the primary endeavor of improving them. This thesis presents approximation algorithms for the aforementioned and other variants of the TSP, and is focused on the techniques and methods used for developing these algorithms

Online Sorting via Searching and Selection

In this paper, we present a framework based on a simple data structure and parameterized algorithms for the problems of finding items in an unsorted list of linearly ordered items based on their rank (selection) or value (search). As a side-effect of answering these online selection and search queries, we progressively sort the list. Our algorithms are based on Hoare's Quickselect, and are parameterized based on the pivot selection method. For example, if we choose the pivot as the last item in a subinterval, our framework yields algorithms that will answer q<=n unique selection and/or search queries in a total of O(n log q) average time. After q=\Omega(n) queries the list is sorted. Each repeated selection query takes constant time, and each repeated search query takes O(log n) time. The two query types can be interleaved freely. By plugging different pivot selection methods into our framework, these results can, for example, become randomized expected time or deterministic worst-case time. Our methods are easy to implement, and we show they perform well in practice

Counterfactual Estimation and Optimization of Click Metrics for Search Engines

Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different search result page for the same query, but we normally cannot infer reliably from search log how users would react to the new result page. Consequently, it appears impossible to accurately estimate online metrics that depend on user feedback, unless the new engine is run to serve users and compared with a baseline in an A/B test. This approach, while valid and successful, is unfortunately expensive and time-consuming. In this paper, we propose to address this problem using causal inference techniques, under the contextual-bandit framework. This approach effectively allows one to run (potentially infinitely) many A/B tests offline from search log, making it possible to estimate and optimize online metrics quickly and inexpensively. Focusing on an important component in a commercial search engine, we show how these ideas can be instantiated and applied, and obtain very promising results that suggest the wide applicability of these techniques