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JEE-Main, unlike JEE-Advanced, has a fixed exam structure and is not subject to change every year. Up until 2018, the JEE-Main Paper-I was three hours long and consisted of thirty questions in each of the three subjects (physics, chemistry and maths). 4 marks are awarded for correct answers and 1 mark is deducted for incorrect answers.
The number of students taking the examination increased substantially each year with 506,484 candidates registered for JEE-Advanced- 2012. [47] However, with the two stage JEE-Main + JEE-Advanced structure from 2013, [ 48 ] the number of candidates in JEE-Advanced is fixed at 150 thousand students in 2013 and it is increased in subsequent years ...
The number of attempts which a candidate can avail at the examination is limited to three in consecutive years. As of 2018, the top 2,24,000 rankers of JEE-Main will qualify to take the second and final level of examination: JEE-Advanced. this number of 2.24 lakh is not fixed this may vary as per difficulty level of paper of JEE-Main. [7]
Gopakumar is a string theorist.Earlier on in his career, his research was primarily focused on large N gauge theories with David Gross, noncommutative gauge theories with Andrew Strominger and Shiraz Minwalla, and topological string theory and gauge/geometry correspondence with Cumrun Vafa and is particularly known for proposing the Gopakumar–Vafa duality and Gopakumar–Vafa invariants.
In psychometrics, predictive validity is the extent to which a score on a scale or test predicts scores on some criterion measure. [1] [2]For example, the validity of a cognitive test for job performance is the correlation between test scores and, for example, supervisor performance ratings.
In computer science, the Knuth–Morris–Pratt algorithm (or KMP algorithm) is a string-searching algorithm that searches for occurrences of a "word" W within a main "text string" S by employing the observation that when a mismatch occurs, the word itself embodies sufficient information to determine where the next match could begin, thus bypassing re-examination of previously matched characters.
The basic form of a linear predictor function () for data point i (consisting of p explanatory variables), for i = 1, ..., n, is = + + +,where , for k = 1, ..., p, is the value of the k-th explanatory variable for data point i, and , …, are the coefficients (regression coefficients, weights, etc.) indicating the relative effect of a particular explanatory variable on the outcome.
Typically the parameters are estimated and plugged into the predictor, leading to the empirical best linear unbiased predictor (EBLUP). Notice that by simply plugging in the estimated parameter into the predictor, additional variability is unaccounted for, leading to overly optimistic prediction variances for the EBLUP. [citation needed]