
The prevalence of subnormality
At the beginning of the 20th century, the prevalence of subnormality was thought to be increasing, because the views of Francis Galton (Hereditary Genius, 1869) and Karl Pearson were uncritically applied to the concept of intelligence. It was supposed, therefore, that because of differential fertility more children would be born to those with less intellectual endowment and that, as a result, the average level of intelligence would decline. Few authorities, with the exception of L. S. Penrose, disagreed with this view, though today few would agree with it. As a result of later studies, the prevalence of severe subnormality is now better understood and we know more about the intelligence level of the children of the mildly subnormal. If we were to define the prevalence of subnormality solely in terms of the level of intelligence, and if all below IQ 70 were assumed to be subnormal, then, further assuming distribution according to Karl Pearson's 'normal' curve, 2.28 per cent of the population would be expected to be subnormal. The great majority of these cases would be between IQs 70 and 55, say 2.14 per cent of the total population. The remaining 0.14 per cent (67,200 in England and Wales) would have IQs in the range of severe subnormality, i.e. lower than IQ 55. When investigations were made, however, fairly firm figures began to emerge for the severely subnormal. These figures were 3.88 per 1,000 (E. O. Lewis), 3.45 per 1,000 (N. Goodman and J. Tizard), for age ranges 7–14 years, and 3.75 per 1,000 (A. Kushlick) for the 15–19 age range. Obviously the actual findings, which for a population of 48 million people would yield a total prevalence of approximately 180,000 severely subnormal cases on the basis of Kushlick's figures, very much exceed the number which would be expected if the definition were based on intelligence level and the normal curve. The difference was accounted for long ago by Pearson and G. A. Jaederholm (On the Continuity of Mental Defect, 1914), the excess of severely subnormal subjects being assumed to be due to the pathological conditions so frequently found at this IQ level.— Neil O'Connor
A state less than normal or that usually encountered.

In computer science, denormal numbers or denormalized numbers (now often called subnormal numbers) fill the underflow gap around zero in floating point arithmetic: any non-zero number which is smaller than the smallest normal number is 'sub-normal'.
In a normal floating point value there are no leading zeros in the significand, instead leading zeros are moved to the exponent. So 0.0123 would be written as 1.23 * 10-2. Denormal numbers are numbers where this representation would result in an exponent that is too small (the exponent usually having a limited range). Such numbers are represented using leading zeros in the significand.
The significand (or mantissa) of an IEEE number is the part of a floating point number that represents the significant digits. For a positive normalised number it can be represented as m0.m1m2m3...mp-2mp-1 (where m represents a significant digit and p is the precision, and m0 is non-zero). Notice that for a binary radix, the leading binary digit is one. In a denormal number, since the exponent is the least that it can be, zero is the lead significand digit (0.m1m2m3...mp-2mp-1) in order to represent numbers closer to zero than the smallest normal number.
By filling the underflow gap like this, significant digits are lost, but not to the extent as when doing flush to zero on underflow (losing all significant digits all through the underflow gap). Hence the production of a denormal number is sometimes called gradual underflow because it allows a calculation to lose precision slowly when the result is small.
In IEEE 754-2008, denormal numbers are renamed subnormal numbers, and are supported in both binary and decimal formats. In binary interchange formats, subnormal numbers are encoded with a biased exponent of 0, but are interpreted with the value of the smallest allowed exponent, which is one greater (i.e., as if it were encoded as a 1). In decimal interchange formats they require no special encoding because the format supports unnormalized numbers directly.
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Denormal numbers provide the guarantee that addition and subtraction of floating-point numbers never underflows; two nearby floating-point numbers always have a representable non-zero difference. Without gradual underflow, the subtraction a−b can underflow and produce zero even though the values are not equal. This can, in turn, lead to division by zero errors that cannot occur when gradual underflow is used.
Denormal numbers were implemented in the Intel 8087 while the IEEE 754 standard was being written. They were by far the most controversial feature in the K-C-S format proposal that was eventually adopted,[1] but this implementation demonstrated that denormals could be supported in a practical implementation. Some implementations of floating point units do not directly support denormal numbers in hardware, but rather trap to some kind of software support. While this may be transparent to the user, it can result in calculations which produce or consume denormal numbers being much slower than similar calculations on normal numbers.
Some systems handle denormal values in hardware, in the same way as normal values. Others leave the handling of denormal values to system software, only handling normal values and zero in hardware. Handling denormal values in software always leads to a significant decrease in performance. But even when denormal values are entirely computed in hardware, the speed of computation is significantly reduced on most modern processors; in extreme cases, instructions involving denormal operands may run as much as 100 times slower.[2][3]
Some applications need to contain code to avoid denormal numbers, either to maintain accuracy, or in order to avoid the performance penalty in some processors. For instance, in audio processing applications, denormal values usually represent a signal so quiet that it is out of the human hearing range. Because of this, a common measure to avoid denormals on processors where there would be a performance penalty is to cut the signal to zero once it reaches denormal levels or mix in an extremely quiet noise signal.[4] Since the SSE2 processor extension, Intel has provided such a functionality in CPU hardware, which rounds denormalized numbers to zero.[5]
See also various papers on William Kahan's web site [1] for examples of where denormal numbers help improve the results of calculations.
This entry is from Wikipedia, the leading user-contributed encyclopedia. It may not have been reviewed by professional editors (see full disclaimer)
Dansk (Danish)
adj. - under normalen
n. - nedfælde den vinkelrette på aksen
Nederlands (Dutch)
achterlijk, minder dan normaal
Français (French)
adj. - arriéré, au-dessous de la normale
n. - arriéré
Deutsch (German)
adj. - unterdurchschnittlich
n. - Minderbegabter
Ελληνική (Greek)
adj. - κάτω του φυσιολογικού ή του κανονικού
n. - (μαθημ.) υποκάθετος
Português (Portuguese)
adj. - subnormal
n. - subnormal (m)
Русский (Russian)
поднормальный, умственно неполноценный человек, дебил, меньше или ниже нормального, умственно отсталый, слабоумный
Español (Spanish)
adj. - subnormal, deficiente
n. - subnormal
Svenska (Swedish)
adj. - under det normala, utvecklingsmässigt under det normala, subnormal, undernormal
n. - det subnormala, det undernormala
中文(简体)(Chinese (Simplified))
正常以下的, 低能的, 普通以下的, 不及常人者, 弱智者
中文(繁體)(Chinese (Traditional))
adj. - 正常以下的, 低能的, 普通以下的
n. - 不及常人者, 弱智者
한국어 (Korean)
adj. - 정상에 못 미치는, 저능의
n. - 정상 이하의 사람, 저능자, 차법선
日本語 (Japanese)
adj. - 普通以下の, 知恵遅れの
العربيه (Arabic)
(صفه) دون المعدل الطبيعي (الاسم) متخلف عقليا
עברית (Hebrew)
adj. - תת-נורמלי (בעיקר מבחינת רמת המשכל)
n. - אדם תת-נורמלי
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