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RIS формат служит для представления библиографических ссылок, и часто используется в научной периодике. Например, все ссылки на статьи с сайта www.sciencedirect.com, представлены в RIS формате. Просматривать RISфайлы не очень удобно, и мне захотелось упростить этот процесс, написав конвертер в удобный и понятный XML формат, с возможностью генерации HTML списков.
На сегодняшний день, существует две версии программыконвертера, консольная и Winforms версия. Обе были написаны мной для собственных нужд, и активно используются.

Исходный RIS файл (фрагмент):
TY  JOUR
T1  Memory capacity in neural network models: Rigorous lower bounds
JO  Neural Networks
VL  1
IS  3
SP  223
EP  238
PY  1988
AU  Newman, Charles M.
UR  http://www.sciencedirect.com/science/article/B6T08482R8NT4/2/666a550edeb8f860f5965e02fe0075a9
AB  We consider certain simple neural network models of associative memory with N binary neurons and symmetric lth order synaptic connections, in which m randomly chosen Nbit patterns are to be stored and retrieved with a small fraction [delta] of bit errors allowed. Rigorous proofs of the following are presented both for l = 2 and l >= 3: 1. 1. m can grow as fast as [alpha]Nl1.2. 2. [alpha] can be as large as Bl/ln(1/[delta]) as [delta] > 0.3. 3. Retrieved memories overlapping with several initial patterns persist for (very) small [alpha].These phenomena were previously supported by numerical simulations or nonrigorous calculations. The quantity (l!) [middle dot] [alpha] represents the number of stored bits per distinct synapse. The constant (l!) [middle dot] Bl is determined explicitly; it decreases monotonically with l and tends to zero exponentially fast as l > [infinity]. We obtain rigorous lower bounds for the threshold value (l!) [middle dot] [alpha]c (the maximum possible value of (l!) [middle dot] [alpha] with [delta] unconstrained): 0.11 for l = 2 (compared to the actual value between 0.28 and 0.30 as estimated by Hopfield and by Amit, Gutfreund, and Sompolinsky), 0.22 for l = 3 and 0.16 for l = 4; as l > [infinity], the bound tends to zero as fast as (l!) [middle dot] Bl.
ER 
Полученный XML файл (фрагмент):
<?xml version="1.0"?>
<Records>
<Record>
<TypeOfReference>JOUR</TypeOfReference>
<TitlePrimary>Memory capacity in neural network models: Rigorous lower bounds</TitlePrimary>
<Journal>Neural Networks</Journal>
<Volume>1</Volume>
<Issue>3</Issue>
<StartPage>223</StartPage>
<EndPage>238</EndPage>
<PublicationYear>1988</PublicationYear>
<Authors>Newman, Charles M.</Authors>
<Url>http://www.sciencedirect.com/science/article/B6T08482R8NT4/2/666a550edeb8f860f5965e02fe0075a9</Url>
<Abstract>We consider certain simple neural network models of associative memory with N binary neurons and symmetric lth order synaptic connections, in which m randomly chosen Nbit patterns are to be stored and retrieved with a small fraction [delta] of bit errors allowed. Rigorous proofs of the following are presented both for l = 2 and l >= 3: 1. 1. m can grow as fast as [alpha]Nl1.2. 2. [alpha] can be as large as Bl/ln(1/[delta]) as [delta] > 0.3. 3. Retrieved memories overlapping with several initial patterns persist for (very) small [alpha].These phenomena were previously supported by numerical simulations or nonrigorous calculations. The quantity (l!) [middle dot] [alpha] represents the number of stored bits per distinct synapse. The constant (l!) [middle dot] Bl is determined explicitly; it decreases monotonically with l and tends to zero exponentially fast as l > [infinity]. We obtain rigorous lower bounds for the threshold value (l!) [middle dot] [alpha]c (the maximum possible value of (l!) [middle dot] [alpha] with [delta] unconstrained): 0.11 for l = 2 (compared to the actual value between 0.28 and 0.30 as estimated by Hopfield and by Amit, Gutfreund, and Sompolinsky), 0.22 for l = 3 and 0.16 for l = 4; as l > [infinity], the bound tends to zero as fast as (l!) [middle dot] Bl. </Abstract>
</Record>
</Records>
Полученный HTML файл (фрагмент):
Newman, Charles M.Memory capacity in neural network models: Rigorous lower bounds Vol.
1, Issue #
3,
1988, pp.
223
238
URL: http://www.sciencedirect.com/science/article/B6T08482R8NT4/2/666a550edeb8f860f5965e02fe0075a9
Abstract:
We consider certain simple neural network models of associative memory with N binary neurons and symmetric lth order synaptic connections, in which m randomly chosen Nbit patterns are to be stored and retrieved with a small fraction [delta] of bit errors allowed. Rigorous proofs of the following are presented both for l = 2 and l >= 3: 1. 1. m can grow as fast as [alpha]Nl1.2. 2. [alpha] can be as large as Bl/ln(1/[delta]) as [delta] > 0.3. 3. Retrieved memories overlapping with several initial patterns persist for (very) small [alpha].These phenomena were previously supported by numerical simulations or nonrigorous calculations. The quantity (l!) [middle dot] [alpha] represents the number of stored bits per distinct synapse. The constant (l!) [middle dot] Bl is determined explicitly; it decreases monotonically with l and tends to zero exponentially fast as l > [infinity]. We obtain rigorous lower bounds for the threshold value (l!) [middle dot] [alpha]c (the maximum possible value of (l!) [middle dot] [alpha] with [delta] unconstrained): 0.11 for l = 2 (compared to the actual value between 0.28 and 0.30 as estimated by Hopfield and by Amit, Gutfreund, and Sompolinsky), 0.22 for l = 3 and 0.16 for l = 4; as l > [infinity], the bound tends to zero as fast as (l!) [middle dot] Bl.
