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Find out how your PSAT/NMSQT and PSAT10 scores are calculated and what those scores mean.

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Score Report Video Guide

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Because there’s no penalty for guessing, your raw score is the number of questions you answered correctly. Raw scores are converted to scores on a scale of 160 to 760 using a process called equating . Equating adjusts for slight differences in difficulty between various versions of the test (such as versions taken on different days).

The College Board uses equating to make sure there’s no advantage in taking the test on a particular day. A score of 400, for instance, on one day’s test means the same thing as a 400 on a test taken on a different day—even though the questions are different.

Score ranges, mean (average) scores, benchmarks, and percentiles can be used to see if you’re on track for college readiness.

For the next few years, norm groups for the score ranges, mean scores, and percentiles described below will be derived from research data, not the prior year’s test-taking populations. A norm group, also called a reference population, is the group whose data your results are compared to.

Tests can't measure exactly what you know, and many factors can affect your score. After all, no two days are the same, and if you were to take the PSAT/NMSQT or PSAT 10 three times in a week or once a week for a month, your scores would vary.

That’s why it’s helpful to think of each score as a range that extends from a few points below to a few points above the score earned. Score ranges show how much your score might change with repeated testing, assuming that your skill level remains the same.

Your score report will show you the mean, or average, scores earned by typical U.S. test-takers per grade.Unless your score is much lower than average, you’re probably developing the kinds of reading, writing and language, and math skills you’ll need in college.

You’ll see a benchmark for each section of the PSAT/NMSQT and PSAT 10. Benchmarks are the scores that represent college readiness. In other words, if you score at or above the benchmark, you’re on track to be ready for college when you graduate high school.

If you score below the benchmark, you still have time to work on your skills. Use the detailed feedback in your online score report to see which skills need the most improvement.

These objects have modes "call" , "expression" , and "name" , respectively.

They can be created directly from expressions using the quote mechanism and converted to and from lists by the as.list and as.call functions. Components of the parse tree can be extracted using the standard indexing operations.

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Symbols refer to R objects. The name of any R object is usually a symbol. Symbols can be created through the functions as.name and quote .

Symbols have mode "name" , storage mode "symbol" , and type "symbol" . They can be coerced to and from character strings using as.character and as.name . They naturally appear as atoms of parsed expressions, try e.g. as.list(quote(x + y)) .

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In R one can have objects of type "expression" . An expression contains one or more statements. A statement is a syntactically correct collection of tokens. Expression objects are special language objects which contain parsed but unevaluated R statements. The main difference is that an expression object can contain several such expressions. Another more subtle difference is that objects of type "expression" are only evaluated when explicitly passed to eval , whereas other language objects may get evaluated in some unexpected cases.

An expression object behaves much like a list and its components should be accessed in the same way as the components of a list.

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In R functions are objects and can be manipulated in much the same way as any other object. Functions (or more precisely, function closures) have three basic components: a formal argument list, a body and an environment. The argument list is a comma-separated list of arguments. An argument can be a symbol, or a ‘ symbol = default ’ construct, or the special argument ‘ ... ’. The second form of argument is used to specify a default value for an argument. This value will be used if the function is called without any value specified for that argument. The ‘ ... ’ argument is special and can contain any number of arguments. It is generally used if the number of arguments is unknown or in cases where the arguments will be passed on to another function.

Specifying #pragma unroll without a parameter directs the loop unroller to attempt to fully unroll the loop if the trip count is known at compile time and attempt to partially unroll the loop if the trip count is not known at compile time:

Specifying the optional parameter, #pragma unroll _value_ , directs the unroller to unroll the loop _value_ times. The parameter may optionally be enclosed in parentheses:

Specifying #pragma nounroll indicates that the loop should not be unrolled:

#pragma unroll and #pragma unroll _value_ have identical semantics to #pragma clang loop unroll(full) and #pragma clang loop unroll_count(_value_) respectively. #pragma nounroll is equivalent to #pragma clang loop unroll(disable) . See satin pajama shorts Black Fleur du Mal Best Place For Sale Sale Cheap Outlet Clearance Store Discount Inexpensive 60sTq4vB
for further details including limitations of the unroll hints.

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The optional attribute intel_reqd_sub_group_size can be used to indicate that the kernel must be compiled and executed with the specified subgroup size. When this attribute is present, get_max_sub_group_size() is guaranteed to return the specified integer value. This is important for the correctness of many subgroup algorithms, and in some cases may be used by the compiler to generate more optimal code. See for details.

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The opencl_unroll_hint attribute qualifier can be used to specify that a loop (for, while and do loops) can be unrolled. This attribute qualifier can be used to specify full unrolling or partial unrolling by a specified amount. This is a compiler hint and the compiler may ignore this directive. See OpenCL v2.0 s6.11.5 for details.

The access qualifiers must be used with image object arguments or pipe arguments to declare if they are being read or written by a kernel or function.

The read_only/__read_only, write_only/__write_only and read_write/__read_write names are reserved for use as access qualifiers and shall not be used otherwise.

In the above example imageA is a read-only 2D image object, and imageB is a write-only 2D image object.

The read_write (or __read_write) qualifier can not be used with pipe.

More details can be found in the OpenCL C language Spec v2.0, Section 6.6.

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  • Deepak Gupta

    Co-founded couple of startups(IOT, AI based). Here to write about code, technology, design, finance and my life experience.

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