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2. Gram Nyayalayas Act, 2008 - Wikipedia

   en.wikipedia.org/wiki/Gram_Nyayalayas_Act,_2008

   Gram Nyayalayas Act, 2008 is an Act of Parliament of India enacted for establishment of Gram Nyayalayas or village courts for speedy and easy access to justice system in the rural areas of India. The Act came into force from 2 October 2009. [ 1 ]

3. Imane Khelif - Wikipedia

   en.wikipedia.org/wiki/Imane_Khelif

   In the second round, held on 1 August, she defeated Angela Carini of Italy 46 seconds after the match commenced, when Carini withdrew after receiving two blows, citing intense pain in her nose. Carini reportedly exclaimed, "It's not fair!" after a punch from Khelif. Because of this, Khelif received online backlash from those who questioned her ...

4. AOL Mail

   mail.aol.com/?rp=webmail-std/en-us/basic

   Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

5. Deaths in 2024 - Wikipedia

   en.wikipedia.org/wiki/Deaths_in_2024

   Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence: Name, age, country of citizenship at birth, subsequent nationality (if applicable), what subject was noted for, cause of death (if known), and reference.

6. Gram - Wikipedia

   en.wikipedia.org/wiki/Gram

   The gram (originally gramme; [1] SI unit symbol g) is a unit of mass in the International System of Units (SI) equal to one thousandth of a kilogram.. Originally defined as of 1795 as "the absolute weight of a volume of pure water equal to the cube of the hundredth part of a metre [1 cm 3], and at the temperature of melting ice", [2] the defining temperature (≈0 °C) was later changed to 4 ...

7. Katz's back-off model - Wikipedia

   en.wikipedia.org/wiki/Katz's_back-off_model

   The equation for Katz's back-off model is: [2] (+) = {+ (+) (+) (+) > + (+)where C(x) = number of times x appears in training w i = ith word in the given context. Essentially, this means that if the n-gram has been seen more than k times in training, the conditional probability of a word given its history is proportional to the maximum likelihood estimate of that n-gram.