³ ÁÒØÖÓÙØÓÒ ½º ÐÙ ØÖ ÜÔÒ ÓÒ Ò ÌÒ ÓÖ ÓÖ ¾º ÌÛÓ¹ÓÝ ÈÖÓÔÖØ Ó ÓÑÔÐÜ ÆÙÐ º ËÙÑÑÖÝ Ò ÓÒÐÙ ÓÒ º ² ± ÇÆÌÆÌË Åº ÐÚÓÐ ¾ Ëʼ
|
|
- Kimberly York
- 6 years ago
- Views:
Transcription
1 È Ò È Æ ÇÊÊÄÌÁÇÆË È ÅÁÍŹÏÁÀÌ ÆÍÄÁ ÁÆ Åº ÐÚÓÐ ÂÄ ÇØÓÖ ¾¼¼ ½
2 ³ ÁÒØÖÓÙØÓÒ ½º ÐÙ ØÖ ÜÔÒ ÓÒ Ò ÌÒ ÓÖ ÓÖ ¾º ÌÛÓ¹ÓÝ ÈÖÓÔÖØ Ó ÓÑÔÐÜ ÆÙÐ º ËÙÑÑÖÝ Ò ÓÒÐÙ ÓÒ º ² ± ÇÆÌÆÌË Åº ÐÚÓÐ ¾ Ëʼ
3 À Ò Ò Ò ÛØ À Ç Òµ Ú Ò µ Ç Òµ Ú Òµ Ö Ë Ä Ëµ ½ Ë Ó Ó Ë ¾ ½ Ö ¾ µ Ç Òµ Òµ Ö Ú ½º ÁÒØÖÓÙØÓÒ ¾ Ñ ¾ ÛÖ Å ½ Ì Ñ ÓÔÖØÓÖÐ ÔÒÒ Ø ÓÒØÓ Ó ÛÖ Ó Ø ÑÒ¹ Ð ÛÚ ÙÒØÓÒ Ò Ò ÓÖÖÐØÓÒ ÓÔÖØÓÖº ź ÐÚÓÐ Ëʼ
4 ¾µ Òµ Ö ½ Ö¾µ ¾ ½µ ¾ ½µ Ö Ö ¼ µ Ò ¾µ Òµ Ö ½ Ö¾µ Ö ÐÙ ØÖ ÜÔÒ ÜÔÒ ÓÒ ØÖÙÒØ Ø ½ Ø ÓÖÖ Ò ¾ Ö½Ö¾ ¾µ Òµ Ö ½ Ö¾µ Ú Òµ Ý Ó Ö½ Ö µ Ç Òµ ½¾ Ó Ö½ Ö µ Ö ¾º ÖÓÙÒ ËØØ ÈÖÓÔÖØ ÐÙ ØÖ ÜÔÒ ÓÒ Ì ÖÓÙÒ ØØ ÒÖÝ ¼ ÚÒ Ý ¾ Ñ ¾ Ö Ö ¾ ½µ Ö Ö ¼ µ ÖÖ ¼ Ó Ò ½µ Ö Ö ¼ µ Ö Ý Ó Ö Ö¾ Ö µ Ó Ö ¼ Ö¾ Ö µ ½ ÅÒ Ð ÖÓÚÖ Ø ¼Ø ÓÖÖ ÒÓÖÑÐÞØÓÒ ÓÒ ÖÚµ Ø ÛÚ ÙÒØÓÒ Ò ÓÖÖÐØÓÒ ÙÒØÓÒ Û ÑÒÑÞ Ø ÖÓÙÒ¹ ÒÖÝ Ù ÓÖ Ø ÜÔØØÓÒ ÚÐÙ Ó ÒÝ ÓÔÖØÓÖ Ø Ñ ÓÖÖ ØØ Åº ÐÚÓÐ Ëʼ
5 Ø Ö Ø ÓÖÖ Ó Ø ÜÔÒ ÓÒ Ø ÙÐÐ ÓÖÖÐØ ÓÒ¹ÓÝ ÑÜ ÑØÖÜ ÜÔÖ ÓÒ ÓÐÐÓÛ Ò ØÝ Ö ¼ ½ µ ½µ Ó Ö½ Ö ¼ ½ µ ½µ À Ö ½ Ö ¼ ½ µ ½µ Ë Ö ½ Ö ¼ ½ µ ½µ Ö½ ½µ Ö ½ Ö ¼ ½ µ À ½µ Ö ½ Ö ¼ ½ µ Ë µ Õµ Ö Ð µ ÔÕµ µ Ö Ö Ð µ Ôµ Ö ½ Ö ¾ µ ½µ Ö ¾ Ö ¼ ½ µ Ö ¾ µ ½µ Ö Ö ¼ ½ µ Ö Ö µ Ö Ö ½½µ µ Ð ÔÕµ µ Ö Ö Ð µ ÔÖÓÔÖ ÙÒØÓÒ Ö Ò ÖÓÑ ÔÒ¹ Ó ÔÒ ØÖ ÛØ ÛØ Ö ¾ À Ö ½¾ Ö ½¼ ¾µ ½µ Ó Ö ½ Ö ¼ ½ µ Ó Ö ¾ µ À Ö ½¾ Ö ½¼ ¾µ ½µ Ó Ó ¾ ½µ Ó Ö ½ Ö ¾ µ À µ ½µ Ö ¾ Ó Ö ¾ Ö ¼ ½ µ Ó Ö µ À µ ½µ Ö ¾ Ö Ö Ó Ó Ò Ø ÙÒØÓÒ À Ò À Ö Ò À µ Ö Ö Ð µ ÔÕ½ ÐÚÓÐ Ó Ð ØØ ÅÓÖØ Èʾ ¾¼¼µµ ź ÐÚÓÐ Ëʼ
6 ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ ËÅ Ö ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ ¾ Ö ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ Ö ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ Ö ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ Ö ËÅ Ö ½ Ö ¾µ Ö ¼ ½ Ö¼ ¾ µ Ó Ö ½ Ö ¼ ½ µ Ó Ö ¾ Ö ¼ ¾ µ Ó Ö ½ Ö ¼ ¾ µ Ó Ö ¾ Ö ¼ ½ µ ¾ ¾ Ö ½ Ö ¾µ Ö ¼ ½ Ö¼ ¾ µ ½ ¾ Ö ½¾ Ö ½¼ ¾ ¼µ Ó Ö ½ Ö ¼ ½ µ Ó Ö ¾ Ö ¼ ¾ µ ½ ¾ Ö ½ Ö ¾µ Ö ¼ ½ Ö¼ ¾ µ ¾ ¾ Ö ½¾ Ö ½¼ ¾ ¼µ Ó Ö ½ Ö ¼ ¾ µ Ó Ö ¾ Ö ¼ ½ µ Ø Ö Ø ÓÖÖ Ó Ø ÜÔÒ ÓÒ Ø ÙÐÐ ÓÖÖÐØ ØÛÓ¹ÓÝ ÑÜ ÑØÖÜ ÜÔÖ ÓÒ ÓÐÐÓÛ Ò ØÝ ÛØ Ö Ö ½ Ö ½¼ µ Ó Ö ½ Ö ¼ ½ µ Ó Ö ¾ Ö ¼ ¾ µ Ó Ö Ö µ Ó Ö ½ Ö ¼ ½ µ Ó Ö ¾ Ö µ Ó Ö Ö ¼ ¾ µ Ó Ö ½ Ö µ Ó Ö ¾ Ö ¼ ½ µ Ó Ö Ö ¼ ¾ µ Ó Ö ½ Ö µ Ó Ö ¾ Ö ¼ ¾ µ Ó Ö Ö ¼ ½ µ Ó Ö ½ Ö ¼ ¾ µ Ó Ö ¾ Ö µ Ó Ö Ö ¼ ½ µ Ó Ö ½ Ö ¼ ¾ µ Ó Ö ¾ Ö ¼ ½ µ Ó Ö Ö µ Ö ½ Ö ¾µ Ö ¼ ½ Ö¼ ¾ µ ½ ¾ Ö Ö Ö µ ½µ È Ó Ö ½ Ö È½ ¼µ Ó Ö ¾ Ö È¾ ¼µ Ó Ö Ö È µ Ó Ö Ö È µ Ⱦ Ó Ð ØØ ÅÓÖØ Èʾ ¾¼¼µµ ÐÚÓÐ Ó Ð ØØ ÅÓÖØ ÖÚ¼¼º ÒÙÐ¹Ø µ ÐÚÓРź ÐÚÓÐ Ëʼ
7 ź ÐÚÓÐ a ÒÓÒ¹ÓÒÐ ÓÒ¹ÓÝ Ö½ Ö ¼ ½ µ ÖÑ ÓÒÐ ØÛÓ¹ÓÝ Ö¾µ ÖÑ Ö½
8 ÖÓÙÒ ØØ ÒÖÝ ½ Ç ¹ ÖÓÒÒ Î ¼ Î Î Î Î Î Ë Î Ë Î Ì» Å Î ÜÔ ¼º½ ¹ º ¹º ¹½½º ¾ ¹¼º¼¼ ¹½¾º ¹ º¼ ¾ º¼ ¹º¼ ¹º½¾ ÀÆ ¼º ¹¼º½ ¹½¼º½ ¹½¼º¼¼ ¹¼º¼ ¹½¼º ¾ ¹ ¼º ¼ ¾º½ ¹º½¾ ¹º¼ ÙÒØÓÒ ËÔÒ¹Á Ó ÔÒ ÓÖÖÐØÓÒ ÌÒ ÓÖ ÒØÖÐ 0.10 f(r) f c (/10) S S O - AV8' r [fm] ź ÐÚÓÐ Ëʼ
9 ³ ² ÌÏÇ¹Ç ÆËÁÌÁË ² º ÅÇÅÆÌÍÅ ÁËÌÊÁÍÌÁÇÆË ÌÏÇ¹Ç Ó ÆÍÄÁ ÇÅÈÄ ± ź ÐÚÓÐ Ëʼ
10 ¾µ Ê ½ Ê Ö Ê ¾ ½ Ö Ê ½ ¾ Ö Ê ¾ ÌÛÓ¹ÓÝ Ò Ø ½ Ö ¾ (r) [fm -3 ] (2) ¾µ Öµ 12 C Total p-p p-n O Total p-p p-n r [fm] r [fm] r [fm] Ca Total p-p p-n ÒÓÖÑÐÞØÓÒ ÒÙÑÖ Ó ÔÖ µ ÓÒ ÖÚ Ý Ø ÜÔÒ ÓÒ Ó ÔÒ ÔÖØÓÒ Ð ÐÓ ¹ ÐÐ ÒÙÐ ÒÐÙ Ò Ø ÓÖÑÐ Ñ Åº ÐÚÓÐ ½¼ Ëʼ
11 ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ ËÅ Ö ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ ¾ Ö ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ Ö ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ Ö ½ Ö ¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ Ö Ö¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ Ö½ ÔÔ ½ Ö¾ Ö ¼ ½ Ö¼ ¾ µ ÔÒ ¾µ Ö ½ Ö¾ Ö ¼ ½ Ö¼ ¾ µ ÒÒ ¾µ Ö ½ Ö¾ Ö ¼ ½ Ö¼ ¾ µ ¾µ Ö ÌÛÓ¹ÓÝ Ò Ø Ó ÔÒ ÔÖØÓÒ Ò Ó Ø ØÖÑ Ó ÓÙÖ ÐÙ ØÖ¹ÜÔÒ ÓÒ ÜÔÖ ÓÒ Ó ØÛÓ ÓÝ Ò ØÝ ÓÒØÖÙØÓÒ ÖÓÑ ÔÖÓØÓÒ¹ÔÖÓØÓÒ ÔÖÓØÓÒ¹ÒÙØÖÓÒ Ò ÒÙØÖÓÒ¹ÒÙØÖÓÒ Ø ÔÖØ Ò Ò ÖØÒ Ø ÔÖÓÔÖ Ó ÔÒ ÔÖÓØÓÒ ÓÔÖØÓÖ Ò Ø ÐÙ ØÖ ÜÔÒ ÓÒ Ý ÔÖØÐ ½ Ò ¾ ÓÖ ÓÒ ÕÙÒ Ø Ñ ÓÐ ÓÖ Ø ØÛÓ ÓÝ ÑÓÑÒØÙÑ ØÖÙØÓÒ Ò ¾µ ½ ¾µ Ò ÔÔ ½ ¾µ Ò ÔÒ ½ ¾µ Ò ÒÒ ½ ¾µ Û Ò Ò Ø ÒÜØ Ðº ź ÐÚÓÐ ½½ Ëʼ
12 ÖÐ ½ ¾ ½ ¾ µ Ö Ö½ Ö¾ Ö ¼ Ö ¼ ½ Ö¼ ¾ à ŠÌÛÓ¹ÓÝ ÅÓÑÒØÙÑ ØÖÙØÓÒ Ã Å Ã ½ ¾ Ê ½ ¾ Ö ½ Ö¾ µ Ê ¼ ½ ¾ Ö¼ ½ Ö¼ ¾ µ Û Ú ½ ¾µ Ò Ãµ ÖÖ ¼ ÊÊ ¼ Ã Ê Ê¼ µ Ö Ö ¼ µ ¾µ Ö Ö ¼ Ê Ê ¼ µ Ò ½ ¾µ Ò µ Ã Ò Ãµ ÖÖ ¼ Ê Ö Ö¼ µ ¾µ Ö Ö ¼ Ê Êµ ½ ¾µ Ò Ãµ Ò Ãµ ÖÊÊ ¼ Ã Ê Ê¼ µ ¾µ Ö Ö Ê Ê ¼ µ ¼ ÓÖÖ ÔÓÒ ØÓ ¾ ½ ºº ¹ØÓ¹ ÒÙÐÓÒ Åº ÐÚÓÐ ½¾ Ëʼ
13 À ÓÑÔÖ ÓÒ ÛØ ÎÅ ÔÆ µ Ê Å ÔÆ Å Ò ÖÐ Ã Å ¼µ Ò ÖÐ Ã Ò ÖÐ Ã µ ÆÆ He He 10 n(k 2 rel ) p-p p-n n( ) [fm 3 ] AV18 p-p p-n ÓÓ ÖÑÒØ ÛØ ÎÅ ÐÙÐØÓÒ n(,k CM =0) [fm 6 ] Ò ÔÒ ÖÐ ¼µÒ ÔÔ ÖÐ ¼µ Ô ÐÓØÓÒ Ó Î½ ËÚÐÐ Ø Ðº ÈÊÄ ¾¼¼µµ Total p - p p - n AV n pn ( ) / n pp ( ) He present work AV k 4 5 rel ź ÐÚÓÐ ½ Ëʼ
14 ØÖ Ò ÓÙÖ¹ÓÝ ÖÑ ÒØÐ Ò ÆÆ ÖÐ µ ÓÖ ÓÑÔÐÜ ÆÙÐ Ò ÆÆ ÖÐ µ Ã Å Ò ÆÆ ÖÐ Ã Å µ n( ) [fm 3 ] 10 4 Total 10 3 SM+2bd 3bd bd C O Total 4 SM+2bd bd bd Total Ca SM+2bd 3bd - 4bd ÒÓÖÑÐÞØÓÒ ÒÙÑÖ Ó ÔÖ µ ÓÒ ÖÚ Ý Ø ÜÔÒ ÓÒ Ó ÔÒ ÔÖØÓÒ Ð ÐÓ ¹ ÐÐ ÒÙÐ ÒÐÙ Ò Ø ÓÖÑÐ Ñ Åº ÐÚÓÐ ½ Ëʼ
15 ¼ ÖÐ Ö Ö¼ µ ÔÆ ¾µ Ö Ö ¼ Ê Êµ Ê ÖÖ ½µ n pn ( ) / n pp ( ) O full central Z 2 / Z(Z-1)/2 = n pn ( ) / n pp ( ) Ca full central Z 2 / Z(Z-1)/2 = ÒÔÒ ÖÐ µ ÒÔÔ ÖÐ µ ÒØÖÐ Ú º ÙÐÐ ÓÖÖÐØÓÒ ÛÖ Ã Å Ò ÔÆ ÖÐ Ã Å µ Ò ÔÆ ÖÐ µ ½ ¾µ Ø ÐÙ ÐÒ Ø ÒÙÑÖ Ó ÔÒ ØÓ ÔÔ ÔÖ ÖØÓ Æ ¾µ Ò ¾ ¾ ź ÐÚÓÐ ½ Ëʼ
16 Ò ÆÆ ÖÐ µ ÓÖ ÓÑÔÐÜ ÆÙÐ Ã Å ¼ n(,k CM =0) [fm 6 ] C Total p - p p - n Ò ÆÆ ÖÐ Ã Å ¼µ O Total p - p p - n Ca Total p - p p - n ½¾ ØÖ Ò ÓÙÖ¹ÓÝ ÖÑ ÚÐÙØÓÒ ØÐÐ ÔÖÐÑÒÖÝ ¹ ÖÐÚÒØ ÖÐ ÖÓÙÒ ¾ Ñ ½ ÓÖ ÔÒ ØÓ ÔÔ ÖØÓ Ö ÛØ ÒÖ Ò ¹ Ù ØÓ Ö Ò ÒÙÑÖ Ó ÖØÓ ÔÖ Åº ÐÚÓÐ ½ Ëʼ
17 Ò ÆÆ ÖÐ µ ÓÖ ÓÑÔÐÜ ÆÙÐ Ã Å ¼ Ò ÔÒ ÖÐ ¼µÒ ÔÔ ÖÐ ¼µ Ñ ÙÖ Ó ÖÐØÚ ÔÒ ØÓ ÔÔ ÓÖÖÐØÓÒ ØÖÒØ n pn (,K CM =0) / n pp (,K CM =0) He 12 C 16 O 40 Ca ÔÒ ØÓ ÔÔ ÖØÓ Ö ÛØ ÒÖ Ò ¹ Ù ØÓ Ö Ò ÒÙÑÖ Ó ÖØÓ ÔÖ Ô Ø ÐÐ ÛØ ÒÖ Ò Åº ÐÚÓÐ ½ Ëʼ
18 ÓÒÐÙ ÓÒ Ï Ú ÐÙÐØ ÓÒ¹ Ò ØÛÓ¹ÓÝ ÖÓÙÒ ØØ ÔÖÓÔÖØ Ó ÓÑÔÐÜ Ò Ø ÖÑÛÓÖ Ó ÐÙ ØÖ ÜÔÒ ÓÒ ÒÙÐ Ì ÐÙ ØÖ ÜÔÒ ÓÒ ÑØÓ ÔÖÓÚ ØÓ ÖÐØÚÐÝ Ý ØÓ Ù Ò Óѹ «ÓÖÐ ÓÑÔÖ ÓÒ ÛØ ÙÖØ ÑÒݹÓÝ ÐÙÐØÓÒ ÔÙØØÓÒÐÐÝ ÚÖÝ Ø ØÓÖÝ Ï Ú ÓÙÖ ØÛÓ¹ÓÝ Ò ÔÆ ÖÐ Ã Å µ Ò Ø Ø ÔÖØÓÒ Ó ÓÖÖÐØÓÒ ÑÓÐ ÌÛÓ¹ÆÙÐÓÒ ÌÒ ÓÖ ÓÖÖÐØÓÒ ÔÔÖ ØÓ Ò ÒØÐ ÒÖÒØ ÓÖ Ø ÓÖÖØ Ó ÓÒ¹ Ò ØÛÓ¹Óݵ ¹ÑÓÑÒØÙÑ ØÖÙØÓÒ ÖÔØÓÒ Åº ÐÚÓÐ ½ Ëʼ
19 ³ ² ± ØÓÒÐ ËРź ÐÚÓÐ ½ Ëʼ
20 ÇÒ¹ÓÝ ÅÓÑÒØÙÑ ØÖÙØÓÒ n(k) [fm 3 ] ½ ¾µ 10-1 Ò µ Mean Field Central Full C FHNC k ¼ ½ Ö Ö ¼ ½µ ½µ Ö ½ Ö ¼ ½ µ Ö½Ö½ Mean Field Central Full 16 O VMC FHNC k Mean Field Central Full 40 Ca FHNC k ź ÐÚÓÐ ¾¼ Ëʼ
21 n(k) [fm 3 ] O Mean Field First order First + Second order ÐÙ ØÖ ÜÔÒ ÓÒ Ò VMC k ½ Ñ ½ n(k) [fm 3 ] ź ÐÚÓÐ ¾½ O VMC Mean Field Central Central + Tensor k
22 À ÓÑÔÖ ÓÒ ÛØ ÎÅ ÔÆ µ Ê Å ÔÆ Å Ò ÖÐ Ã Å ¼µ Ò ÖÐ Ã Ò ÖÐ Ã µ ÆÆ He He 10 n(k 2 rel ) p-p p-n n( ) [fm 3 ] AV18 p-p p-n ÓÓ ÖÑÒØ ÛØ ÎÅ ÐÙÐØÓÒ n(,k CM =0) [fm 6 ] Ò ÔÒ ÖÐ ¼µÒ ÔÔ ÖÐ ¼µ Ô ÐÓØÓÒ Ó Î½ ËÚÐÐ Ø Ðº ÈÊÄ ¾¼¼µµ ź ÐÚÓÐ ¾¾ Total p - p p - n AV n pn ( ) / n pp ( ) He present work AV k 4 5 rel
23 ËÔØÖÐ ÙÒØÓÒ ÔÖÓÔÖØ Ø ÐÓÛ Ã Å Ò ÖÐ ¾ È ½ µ È Ñ Ò ÖÐ È Ñ¾µ Ò Ñ È Ñµ ¾µ ¾µ ØÖ ½µ ½µÈ Ñ ¾µ n pn (,K CM ) [fm 6 ] 10 4 MB CS 10 3 K CM = 0.0 fm -1 K CM = 0.5 fm K CM = 1.0 fm O Å Ò ÖÐ Ã Å µ Ó ËÑÙÐ Ë ½µ ÈÊ Ò ¾ À Öе Ò Ë Ã Å µ Æ ¾Å ź ÐÚÓÐ ¾ Ëʼ
24 ËÔØÖÐ ÙÒØÓÒ ÔÖÓÔÖØ Ø Ã Å ¼ Ò ÖÐ ¾ È ½ µ È Ñ Ò ÖÐ È Ñ¾µ Ò Ñ È Ñµ ¾µ ¾µ ØÖ ½µ ½µÈ Ñ ¾µ Æ n pn (,K CM =0) [fm 6 ] C pp A n D (k)n CM (0) C pn A n D (k)n CM (0) p-p p-n ¾Å 2 3 k 4 5 rel ÔÔ Ò ÔÒ Å Ò ÖÐ ¼µ Ó ËÑÙÐ Ë ½µ ÈÊ Ò ¾ À Öе Ò Å ¼µ ź ÐÚÓÐ ¾ Ëʼ
25 Ò ÖÐ Ã Å µ ØÓÖÞØÓÒ Ò Ø ÌÛÓ¹ÆÙÐÓÒ ÓÖÖÐØÓÒ ÑÓÐ ËÔØÖÐ ÖÕÙÖ Ò ÖÐ Ã Å µ» Ò ÖÐ Ã ¼ Å µ ÙÒØÓÒ n(,k CM, ) / n(,k ' CM ) K CM - K ' CM O - kk =90 o ÖÐ Ò ÐÓÛ Ã Å ØÓÖÞØÓÒ ÚÖ Ý ÑÒݹÓÝ ÐÙ¹ ÐØÓÒ Åº ÐÚÓÐ ¾ Ëʼ
Models, Notation, Goals
Scope Ë ÕÙ Ò Ð Ò ÐÝ Ó ÝÒ Ñ ÑÓ Ð Ü Ô Ö Ñ Ö ² Ñ ¹Ú ÖÝ Ò Ú Ö Ð Ö ÒÙÑ Ö Ð ÔÓ Ö ÓÖ ÔÔÖÓÜ Ñ ÓÒ ß À ÓÖ Ð Ô Ö Ô Ú ß Ë ÑÙÐ ÓÒ Ñ Ó ß ËÑÓÓ Ò ² Ö Ò Ö Ò Ô Ö Ñ Ö ÑÔÐ ß Ã ÖÒ Ð Ñ Ó ÚÓÐÙ ÓÒ Ñ Ó ÓÑ Ò Ô Ö Ð Ð Ö Ò Ð ÓÖ Ñ
More informationAncillary Software Development at GSI. Michael Reese. Outline: Motivation Old Software New Software
Ancillary Software Development at GSI Michael Reese Outline: Motivation Old Software New Software Work supported by BMBF NuSTAR.DA - TP 6, FKZ: BMBF 05P12RDFN8 (TP 6). March 20, 2013 AGATA week 2013 at
More informationMesh Smoothing via Mean and Median Filtering Applied to Face Normals
Mesh Smoothing via Mean and ing Applied to Face Normals Ý Hirokazu Yagou Yutaka Ohtake Ý Alexander G. Belyaev Ý Shape Modeling Lab, University of Aizu, Aizu-Wakamatsu 965-8580 Japan Computer Graphics Group,
More informationUsing USB Hot-Plug For UMTS Short Message Service. Technical Brief from Missing Link Electronics:
Technical Brief 20100507 from Missing Link Electronics: Using USB Hot-Plug For UMTS Short Message Service This Technical Brief describes how the USB hot-plug capabilities of the MLE Soft Hardware Platform
More informationControl-Flow Graph and. Local Optimizations
Control-Flow Graph and - Part 2 Department of Computer Science and Automation Indian Institute of Science Bangalore 560 012 NPTEL Course on Principles of Compiler Design Outline of the Lecture What is
More informationLecture 20: Classification and Regression Trees
Fall, 2017 Outline Basic Ideas Basic Ideas Tree Construction Algorithm Parameter Tuning Choice of Impurity Measure Missing Values Characteristics of Classification Trees Main Characteristics: very flexible,
More informationHow to Implement DOTGO Engines. CMRL Version 1.0
How to Implement DOTGO Engines CMRL Version 1.0 Copyright c 2009 DOTGO. All rights reserved. Contents 1 Introduction 3 2 A Simple Example 3 2.1 The CMRL Document................................ 3 2.2 The
More informationA Comparison of Mesh Smoothing Methods
A Comparison of Mesh Smoothing Methods Alexander Belyaev Yutaka Ohtake Computer Graphics Group, Max-Planck-Institut für Informatik, 66123 Saarbrücken, Germany Phone: [+49](681)9325-408 Fax: [+49](681)9325-499
More informationConcurrent Architectures - Unix: Sockets, Select & Signals
Concurrent Architectures - Unix: Sockets, Select & Signals Assignment 1: Drop In Labs reminder check compiles in CS labs & you have submitted all your files in StReAMS! formatting your work: why to 80
More informationGraphs (MTAT , 4 AP / 6 ECTS) Lectures: Fri 12-14, hall 405 Exercises: Mon 14-16, hall 315 või N 12-14, aud. 405
Graphs (MTAT.05.080, 4 AP / 6 ECTS) Lectures: Fri 12-14, hall 405 Exercises: Mon 14-16, hall 315 või N 12-14, aud. 405 homepage: http://www.ut.ee/~peeter_l/teaching/graafid08s (contains slides) For grade:
More informationProbabilistic analysis of algorithms: What s it good for?
Probabilistic analysis of algorithms: What s it good for? Conrado Martínez Univ. Politècnica de Catalunya, Spain February 2008 The goal Given some algorithm taking inputs from some set Á, we would like
More informationIntroduction to Scientific Typesetting Lesson 11: Foreign Languages, Columns, and Section Titles
Introduction to Scientific Typesetting Lesson 11: Foreign Languages,, and Ryan Higginbottom January 19, 2012 1 Ð The Package 2 Without Ð What s the Problem? With Ð Using Another Language Typing in Spanish
More informationUsing SmartXplorer to achieve timing closure
Using SmartXplorer to achieve timing closure The main purpose of Xilinx SmartXplorer is to achieve timing closure where the default place-and-route (PAR) strategy results in a near miss. It can be much
More informationA Study of Smoothing Methods for Language Models Applied to Information Retrieval
A Study of Smoothing Methods for Language Models Applied to Information Retrieval CHENGXIANG ZHAI and JOHN LAFFERTY Carnegie Mellon University ÄÒÙ ÑÓÐÒ ÔÔÖÓ ØÓ ÒÓÖÑØÓÒ ÖØÖÚÐ Ö ØØÖØÚ Ò ÔÖÓÑ Ò Ù ØÝ ÓÒÒØ
More informationConcurrent Execution
Concurrent Execution Overview: concepts and definitions modelling: parallel composition action interleaving algebraic laws shared actions composite processes process labelling, action relabeling and hiding
More informationFrom Clarity to Efficiency for Distributed Algorithms
From Clarity to Efficiency for Distributed Algorithms Yanhong A. Liu Scott D. Stoller Bo Lin Michael Gorbovitski Computer Science Department, State University of New York at Stony Brook, Stony Brook, NY
More informationTHE AUSTRALIAN NATIONAL UNIVERSITY Practice Final Examination, October 2012
THE AUSTRALIAN NATIONAL UNIVERSITY Practice Final Examination, October 2012 COMP2310 / COMP6310 (Concurrent and Distributed Systems ) Writing Period: 3 hours duration Study Period: 15 minutes duration
More informationComponent Adaptation and Assembly Using Interface Relations
Component Adaptation and Assembly Using Interface Relations Stephen Kell Computer Laboratory, University of Cambridge 15 JJ Thomson Avenue Cambridge CB3 0FD United Kingdom Ö ØÒ Ñ ºÐ ØÒ Ñ Ðº Ѻ ºÙ Abstract
More informationAdministrivia. Lab 1 will be up by tomorrow, Due Oct. 11
p.1/45 Administrivia Lab 1 will be up by tomorrow, Due Oct. 11 - Due at start of lecture 4:15pm - Free extension to midnight if you come to lecture - Or for SCPD students only if you watch lecture live
More informationDSPTricks A Set of Macros for Digital Signal Processing Plots
DSPTricks A Set of Macros for Digital Signal Processing Plots Paolo Prandoni The package DSPTricks is a set of L A TEX macros for plotting the kind of graphs and figures that are usually employed in digital
More informationConstraint Logic Programming (CLP): a short tutorial
Constraint Logic Programming (CLP): a short tutorial What is CLP? the use of a rich and powerful language to model optimization problems modelling based on variables, domains and constraints DCC/FCUP Inês
More informationFormal Specification of an Asynchronous On-Chip Bus
Formal Specification of an Asynchronous On-Chip Bus Juha Plosila Tiberiu Seceleanu Turku Centre for Computer Science TUCS Technical Reports No 461, 14th June 2002 Formal Specification of an Asynchronous
More informationGlobal abstraction-safe marshalling with hash types
Global abstraction-safe marshalling with hash types James J. Leifer Ý Gilles Peskine Ý Peter Sewell Þ Keith Wansbrough Þ Ý INRIA Rocquencourt ßÖ ØºÄ ØÐÒÖºÖ Þ University of Cambridge ßÖ ØºÄ ØÐкѺºÙ ØÖØ
More informationThis document has been prepared by Sunder Kidambi with the blessings of
Ôß ò ÉßÔß ßÔß ò ÆÐÐß ßÔß Ôß» Ôò Æß Ð ÐÑß Æß ÐÑ ýæßæòþøñ Ôò Ð ÐÌÐÑßÔßÑú Ôò ÞØ ß Ð ÞØ Ð ÞÚ Ôß ÔÐÛß Ôß Ôß ÉßÛ Ñß Ì Ðß Þ òõß Ñß ßÔß õó This document has been prepared by Sunder Kidambi with the blessings of
More informationText and Image Metasearch on the Web
Appears in International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA 99, CSREA Press, pp. 89 835, 1999. Copyright c CSREA Press. Text and Image Metasearch on the
More informationThis file contains an excerpt from the character code tables and list of character names for The Unicode Standard, Version 3.0.
Range: This file contains an excerpt from the character code tables and list of character names for The Unicode Standard, Version.. isclaimer The shapes of the reference glyphs used in these code charts
More informationOn the Complexity of List Scheduling Algorithms for Distributed-Memory Systems.
On the Complexity of List Scheduling Algorithms for Distributed-Memory Systems. Andrei Rădulescu Arjan J.C. van Gemund Faculty of Information Technology and Systems Delft University of Technology P.O.Box
More informationTime-Space Tradeoffs, Multiparty Communication Complexity, and Nearest-Neighbor Problems
Time-Space Tradeoffs, Multiparty Communication Complexity, and Nearest-Neighbor Problems Paul Beame Computer Science and Engineering University of Washington Seattle, WA 98195-2350 beame@cs.washington.edu
More informationRSA (Rivest Shamir Adleman) public key cryptosystem: Key generation: Pick two large prime Ô Õ ¾ numbers È.
RSA (Rivest Shamir Adleman) public key cryptosystem: Key generation: Pick two large prime Ô Õ ¾ numbers È. Let Ò Ô Õ. Pick ¾ ½ ³ Òµ ½ so, that ³ Òµµ ½. Let ½ ÑÓ ³ Òµµ. Public key: Ò µ. Secret key Ò µ.
More informationRSA (Rivest Shamir Adleman) public key cryptosystem: Key generation: Pick two large prime Ô Õ ¾ numbers È.
RSA (Rivest Shamir Adleman) public key cryptosystem: Key generation: Pick two large prime Ô Õ ¾ numbers È. Let Ò Ô Õ. Pick ¾ ½ ³ Òµ ½ so, that ³ Òµµ ½. Let ½ ÑÓ ³ Òµµ. Public key: Ò µ. Secret key Ò µ.
More informationContents. Bibliography 43. Index 44
Contents 3 Basic Data Types page 2 3.1 Stacks and Queues 2 3.2 Lists 5 3.3 Arrays 17 3.4 Compressed Boolean Arrays (Type int set) 21 3.5 Random Sources 23 3.6 Pairs, Triples, and such 38 3.7 Strings 39
More informationT E C H N I C A L R E P O R T. Disjunctive Logic Programs with Inheritance INSTITUT FÜR INFORMATIONSSYSTEME DBAI-TR-99-30
T E C H N I C A L R E P O R T INSTITUT FÜR INFORMATIONSSYSTEME ABTEILUNG DATENBANKEN UND ARTIFICIAL INTELLIGENCE Disjunctive Logic Programs with Inheritance DBAI-TR-99-30 Francesco Buccafurri 1 Wolfgang
More informationAn Esterel Virtual Machine
An Esterel Virtual Machine Stephen A. Edwards Columbia University Octopi Workshop Chalmers University of Technology Gothenburg, Sweden December 28 An Esterel Virtual Machine Goal: Run big Esterel programs
More informationat MODELS 2008 (Proceedings) Edited by
3 rd Workshop on Models@run.time at MODELS 2008 (Proceedings) Edited by Nelly Bencomo Gordon Blair Lancaster University Robert France Colorado State University Freddy Muñoz INRIA, France Cedric Jeanneret
More informationAn Object-Oriented Metamodel for Bunge-Wand-Weber Ontology
An Object-Oriented Metamodel for Bunge-Wand-Weber Ontology Arvind W. Kiwelekar, Rushikesh K. Joshi Department of Computer Science and Engineering Indian Institute of Technology Bombay Powai, Mumbai-400
More informationTeam Practice October 2012: 1:00 6:00 PM Contest Problem Set
Team Practice 1 14 October 2012: 1:00 6:00 PM Contest Problem Set The ten problems on this contest are referred to, in order, by the following names: stones, birdtree, money, duke1, dull, maze, howbig,
More informationExtending Conceptual Logic Programs with Arbitrary Rules
Extending Conceptual Logic Programs with Arbitrary Rules Stijn Heymans, Davy Van Nieuwenborgh, and Dirk Vermeir Dept. of Computer Science Vrije Universiteit Brussel, VUB Pleinlaan 2, B1050 Brussels, Belgium
More informationPointers & Arrays. CS2023 Winter 2004
Pointers & Arrays CS2023 Winter 2004 Outcomes: Pointers & Arrays C for Java Programmers, Chapter 8, section 8.12, and Chapter 10, section 10.2 Other textbooks on C on reserve After the conclusion of this
More informationThe CImg Library and G MIC
The CImg Library and G MIC Open-Source Toolboxes for Image Processing at Different Levels David Tschumperlé { Image Team - GREYC Laboratory (CNRS UMR 6072) - Caen / France} Séminaire LRDE, Paris / France,
More informationOverview: Concurrent Architectures - Unix: Forks and Pipes
Overview: Concurrent Architectures - Unix: Forks and Pipes Other Matters: TuteLab-5 solutions and the proof of Peterson s Algorithm Ref: [Coulouris&al Ch 4] history architecture: monolithic vs microkernels,
More informationÔ ÖØÑ ÒØ Ó ÔÔÐ Å Ø Ñ Ø Ò ÆÙÑ Ö Ð Ò ÐÝ Î ÒÒ ÍÒ Ú Ö ØÝ Ó Ì ÒÓÐÓ Ý Î ÒÒ Ê ÔÓÖØ ÆÖº ½¾» Ì ÔÔÐ Ø ÓÒ Ó Ë ÓÓØ Ò ØÓ Ë Ò ÙÐ Ö ÓÙÒ ÖÝ Î ÐÙ ÈÖÓ Ð Ñ Ïº ÙÞ Ò Ö Çº
ÔÖØÑÒØ ÓÔÔÐ ÅØÑØ Ò ÆÙÑÖÐ ÒÐÝ ÎÒÒ ÍÒÚÖ ØÝ Ó ÌÒÓÐÓÝ ÎÒÒ ÊÔÓÖØ ÆÖº ½» ÌÔÔÐØÓÒ Ó ËÓÓØÒ ØÓ ËÒÙÐÖ ÓÙÒÖÝ ÎÐÙ ÈÖÓÐÑ Ïº ÙÞÒÖ Çº ÃÓ Èº ÃÓ Ö º ÏÒÑĐÙÐÐÖ Ì ÔÖÓØ Û ÙÔÔÓÖØÝ Ø Ù ØÖÒ Ê ÖÙÒ Ïµ ÖÒØ È¹½¼ßÅ̺ ĐÙÖ Ò ÁÒÐØ ÚÖÒØÛÓÖØÐ
More informationBlocking System Calls in KRoC/Linux
Communicating Process Architectures 2 P.H. Welch and A.W.P. Bakkers (Eds.) IOS Press, 2 155 Blocking System Calls in KRoC/Linux Frederick R.M. Barnes Computing Laboratory, University of Kent, Canterbury,
More informationInstruction Scheduling. Software Pipelining - 3
Instruction Scheduling and Software Pipelining - 3 Department of Computer Science and Automation Indian Institute of Science Bangalore 560 012 NPTEL Course on Principles of Compiler Design Instruction
More informationPropagating XML Constraints to Relations
Propagating XML Constraints to Relations Susan Davidson U. of Pennsylvania Wenfei Fan Ý Bell Labs Carmem Hara U. Federal do Parana, Brazil Jing Qin Temple U. Abstract We present a technique for refining
More informationINTERVAL ANALYSIS FOR CERTIFIED NUMERICAL SOLUTION OF PROBLEMS IN ROBOTICS
Int. J. Appl. Math. Comput. Sci., 2008, Vol., No., DOI: INTERVAL ANALYSIS FOR CERTIFIED NUMERICAL SOLUTION OF PROBLEMS IN ROBOTICS J-P. MERLET INRIA,2004 Route des Lucioles, 06902 Sophia-Antipolis,France
More informationPointers. CS2023 Winter 2004
Pointers CS2023 Winter 2004 Outcomes: Introduction to Pointers C for Java Programmers, Chapter 8, sections 8.1-8.8 Other textbooks on C on reserve After the conclusion of this section you should be able
More informationIntelligence Analysis Using Quantitative Preferences
Intelligence Analysis Using Quantitative Preferences Davy Van Nieuwenborgh, Stijn Heymans, and Dirk Vermeir Dept. of Computer Science Vrije Universiteit Brussel, VUB Pleinlaan 2, B1050 Brussels, Belgium
More informationComposable Memory Transactions
Composable Memory Transactions December 20, 2004 Tim Harris, Simon Marlow, Simon Peyton Jones, Maurice Herlihy Microsoft Research, Cambridge Abstract Writing concurrent programs is notoriously difficult,
More information1 System Overview Event Link Data Global Time Distribution... 5
ÎÊ Í Ù Å Ð Ú Ú Ö Ñ Ú Ú Ö Òк ÓÚ Ù Ù Øº ¾¼½ Ê Úº ÓÒØ ÒØ 1 System Overview 3 1.1 Event Link Data........................... 4 1.2 Global Time Distribution...................... 5 2 Receiver Functions 6 2.1
More information54 5 Vol.54 No ACTA ASTRONOMICA SINICA Sep., ASIC (Application Specific Integrated Circuit) Á Ü Ö Êº Æ 4 pixel
54 5 Vol.54 No.5 2013 9 ACTA ASTRONOMICA SINICA Sep., 2013 Ó Å Í Â Ú½ 1,2 1,2 1,2,3 1,2,3 (1 Ê Å µ 210008) (2 Ê µï Å ÇÕ µ 210008) (3 Ê µ 100049) ß Ä (CdZnTe) ±ÆÁ Ú ÜÇÌ Ò Ð Å Ð ½ ¾ ÜÜ Ä ³ Ü Æ Ü Æ ¾ Ñ Ä
More informationAn Experimental CLP Platform for Integrity Constraints and Abduction
An Experimental CLP Platform for Integrity Constraints and Abduction Slim Abdennadher ½ and Henning Christiansen ¾ ½ ¾ Computer Science Department, University of Munich Oettingenstr. 67, 80538 München,
More informationPHANTOM TYPES AND SUBTYPING
PHANTOM TYPES AND SUBTYPING Matthew Fluet and Riccardo Pucella Department of Computer Science Cornell University ßfluet,riccardoÐ@cs.cornell.edu Abstract We investigate a technique from the literature,
More informationFairing Triangular Meshes with Highlight Line Model
Fairing Triangular Meshes with Highlight Line Model Jun-Hai Yong, Bai-Lin Deng, Fuhua (Frank) Cheng and Kun Wu School of Software, Tsinghua University, Beijing 100084, P. R. China Department of Computer
More informationSFU CMPT Lecture: Week 8
SFU CMPT-307 2008-2 1 Lecture: Week 8 SFU CMPT-307 2008-2 Lecture: Week 8 Ján Maňuch E-mail: jmanuch@sfu.ca Lecture on June 24, 2008, 5.30pm-8.20pm SFU CMPT-307 2008-2 2 Lecture: Week 8 Universal hashing
More informationConditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data John Lafferty LAFFERTY@CS.CMU.EDU Andrew McCallum MCCALLUM@WHIZBANG.COM Fernando Pereira Þ FPEREIRA@WHIZBANG.COM
More informationReview: Test-and-set spinlock
p. 1/3 Review: Test-and-set spinlock struct var { int lock; int val; ; void atomic_inc (var *v) { while (test_and_set (&v->lock)) ; v->val++; v->lock = 0; void atomic_dec (var *v) { while (test_and_set
More informationTime-space tradeoff lower bounds for randomized computation of decision problems
Time-space tradeoff lower bounds for randomized computation of decision problems Paul Beame Ý Computer Science and Engineering University of Washington Seattle, WA 98195-2350 beame@cs.washington.edu Xiaodong
More informationComputing optimal linear layouts of trees in linear time
Computing optimal linear layouts of trees in linear time Konstantin Skodinis University of Passau, 94030 Passau, Germany, e-mail: skodinis@fmi.uni-passau.de Abstract. We present a linear time algorithm
More informationModel-driven QoS Provisioning for Distributed Real-time and Embedded Systems
Model-driven QoS Provisioning for Distributed Real-time and Embedded Systems Submitted to the Special issue on Model-driven Embedded System Design  Á Æ ËÀ Ä ËÍ Ê Å ÆÁ Æ ËÍÅ ÆÌ Ì Å ÆÁÊÍ À ÇÃÀ Ä ÇÍ Ä Ë
More informationSeparation Logic: A Logic for Shared Mutable Data Structures
This is a preprint of a paper to appear in the Proceedings of the Seventeenth Annual IEEE Symposium on Logic in Computer Science, to be held July 22-25, 2002 in Copenhagen, Denmark. Copyright 2002 IEEE.
More informationThe role of the parser
The role of the parser source code tokens scanner parser IR errors Parser performs context-free syntax analysis guides context-sensitive analysis constructs an intermediate representation produces meaningful
More informationComputing Gaussian Mixture Models with EM using Equivalence Constraints
Computing Gaussian Mixture Models with EM using Equivalence Constraints Noam Shental, Aharon Bar-Hillel, Tomer Hertz and Daphna Weinshall email: tomboy,fenoam,aharonbh,daphna@cs.huji.ac.il School of Computer
More informationCERIAS Tech Report
CERIAS Tech Report 2002-43 AN EXTENSION OF THE DICKMAN FUNCTION AND ITS APPLICATION by Chaogui Zhang Center for Education and Research in Information Assurance and Security, Purdue University, West Lafayette,
More informationUnified Configuration Knowledge Representation Using Weight Constraint Rules
Unified Configuration Knowledge Representation Using Weight Constraint Rules Timo Soininen ½ and Ilkka Niemelä ¾ and Juha Tiihonen ½ and Reijo Sulonen ½ Abstract. In this paper we present an approach to
More informationtranx86 an Optimising ETC to IA32 Translator
Communicating Process Architectures 2001 Alan Chalmers, Majid Mirmehdi and Henk Muller (Eds.) IOS Press, 2001 265 tranx86 an Optimising ETC to IA32 Translator Frederick R.M. Barnes Computing Laboratory,
More informationTicc: A Tool for Interface Compatibility and Composition
ÒØÖ Ö Ò Î Ö Ø ÓÒ Ì Ò Ð Ê ÔÓÖØ ÒÙÑ Ö ¾¼¼ º Ì ÌÓÓÐ ÓÖ ÁÒØ Ö ÓÑÔ Ø Ð ØÝ Ò ÓÑÔÓ Ø ÓÒº Ð Ö º Ì ÓÑ Å ÖÓ ÐÐ ÄÙ Ð ÖÓ Äº Ë ÐÚ Ü Ð Ä Ý Î Û Ò Ø Ê Ñ Ò Èº Ê ÓÝ Ì ÛÓÖ Û Ô ÖØ ÐÐÝ ÙÔÔÓÖØ Ý Ê Ö ÒØ ¾º ¼º¼¾ ØØÔ»»ÛÛÛºÙÐ º
More informationAnalysis and Optimisation of Active Database Rules Using Abstract Interpretation and Partial Evaluation
Analysis and Optimisation of Active Database Rules Using Abstract Interpretation and Partial Evaluation ¾ ½ James Bailey ½ and Alexandra Poulovassilis ¾ Dept. of Computer Science, King s College London,
More informationAPPLESHARE PC UPDATE INTERNATIONAL SUPPORT IN APPLESHARE PC
APPLESHARE PC UPDATE INTERNATIONAL SUPPORT IN APPLESHARE PC This update to the AppleShare PC User's Guide discusses AppleShare PC support for the use of international character sets, paper sizes, and date
More informationTUGBOAT. Volume19, Number3 / September Annual Meeting Proceedings
TUGBOAT Volume19, Number3 / September 1998 1998 Annual Meeting Proceedings 234 Barbara Beeton / TUG Election Notice 235 Barbara Beeton / Editorial Comments A TUG 98 trip report 237 TUG 98 Attendees Real
More informationWhy? The classical inverse ECG problem. The Bidomain model. Outside the heart
Ú Áion Úµ Ö Å ÖÚµ Ö Å ÖÙ µ Ñ Ø ¾»» The classical inverse ECG problem Is it possible to compute the electrical potential at the surface of the heart from body surface measurements? Why? Improve traditional
More informationGeneration of Interactive Visual Interfaces for Resource Management
Generation of Interactive Visual Interfaces for Resource Management Andreas Dangberg, Wolfgang Mueller C LAB, Fuerstenallee 11, 33102 Paderborn, Germany Abstract This paper introduces the VIVID (Visual
More informationParallel Functional Reactive Programming
Parallel Functional Reactive Programming John Peterson, Valery Trifonov, and Andrei Serjantov Yale University Ô Ø Ö ÓÒ¹ Ó Ò ºÝ Ð º Ù ØÖ ÓÒÓÚ¹Ú Ð ÖÝ ºÝ Ð º Ù Ò Ö º Ö ÒØÓÚÝ Ð º Ù Abstract In this paper,
More informationDirected Single Source Shortest Paths in Linear Average Case Time
Directed Single Source Shortest Paths in inear Average Case Time Ulrich Meyer MPI I 2001 1-002 May 2001 Author s Address ÍÐÖ ÅÝÖ ÅܹÈÐÒ¹ÁÒ ØØÙØ ĐÙÖ ÁÒÓÖÑØ ËØÙÐ ØÞÒÙ Û ½¾ ËÖÖĐÙÒ umeyer@mpi-sb.mpg.de www.uli-meyer.de
More informationDesigning Networks Incrementally
Designing Networks Incrementally Adam Meyerson Kamesh Munagala Ý Serge Plotkin Þ Abstract We consider the problem of incrementally designing a network to route demand to a single sink on an underlying
More informationBanner 8 Using International Characters
College of William and Mary Banner 8 Using International Characters A Reference and Training Guide Banner Support January 23, 2009 Table of Contents Windows XP Keyboard Setup 3 VISTA Keyboard Setup 7 Creating
More informationImplementing Language-Dependent Lexicographic Orders in Scheme
Implementing Language-Dependent Lexicographic Orders in Scheme Jean-Michel HUFFLEN LIFC (EA CNRS 4157) University of Franche-Comté 16, route de Gray 25030 BESANÇON CEDEX FRANCE Ù ÒÐ ºÙÒ Ú¹ ÓÑØ º Ö Abstract
More informationThe Online Median Problem
The Online Median Problem Ramgopal R. Mettu C. Greg Plaxton November 1999 Abstract We introduce a natural variant of the (metric uncapacitated) -median problem that we call the online median problem. Whereas
More informationEfficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms Roni Khardon Tufts University Medford, MA 02155 roni@eecs.tufts.edu Dan Roth University of Illinois Urbana, IL 61801 danr@cs.uiuc.edu
More informationCassandra: Distributed Access Control Policies with Tunable Expressiveness
Cassandra: Distributed Access Control Policies with Tunable Expressiveness p. 1/12 Cassandra: Distributed Access Control Policies with Tunable Expressiveness Moritz Y. Becker and Peter Sewell Computer
More informationA sharp threshold in proof complexity yields lower bounds for satisfiability search
A sharp threshold in proof complexity yields lower bounds for satisfiability search Dimitris Achlioptas Microsoft Research One Microsoft Way Redmond, WA 98052 optas@microsoft.com Michael Molloy Ý Department
More informationNetworks. Other Matters: draft Assignment 2 up (Labs 7 & 8 v. important!!) Ref: [Coulouris&al Ch 3, 4] network performance and principles
Networks Other Matters: draft Assignment 2 up (Labs 7 & 8 v. important!!) Ref: [Coulouris&al Ch 3, 4] network performance and principles OSI protocol; routing TCP/IP layers and packet organization IP addresses
More informationBetter than the Two: Exceeding Private and Shared Caches via Two-Dimensional Page Coloring
Better than the Two: Exceeding Private and Shared Caches via Two-Dimensional Page Coloring Lei Jin Sangyeun Cho Department of Computer Science University of Pittsburgh jinlei,cho@cs.pitt.edu Abstract Private
More informationº
ý ý ¾¼¼ º ý º º º ººº º º º ýº ýº º ý º ý ¾¼¼ ¾¼¼ È ÒÓ À Ð Ö º ÐÐ Ö Ø Ö ÖÚ º º º º º ¹ º ¹ ¹ µ µ º ý ¹ µº ý º ¹ º ÓÒÐ Ò µ ¹ ¹ º ý ¹ º ¹ ½ i i 1 º ô µº ¹ ¹ º º ØÖ Øº Ï ØÙ Ý Ú Ö Ð Ú ÖØ Ü ÓÐÓÖ Ò ÔÖÓ Ð Ñ ÓÖ
More informationOn Clusterings Good, Bad and Spectral
On Clusterings Good, Bad and Spectral Ravi Kannan Computer Science, Yale University. kannan@cs.yale.edu Santosh Vempala Ý Mathematics, M.I.T. vempala@math.mit.edu Adrian Vetta Þ Mathematics, M.I.T. avetta@math.mit.edu
More informationFace Detection Using Mixtures of Linear Subspaces
Face Detection Using Mixtures of Linear Subspaces Ming-Hsuan Yang Narendra Ahuja David Kriegman Department of Computer Science and Beckman Institute University of Illinois at Urbana-Champaign, Urbana,
More informationAlgorithms Design for the Parallelization of Nested Loops
NATIONAL TECHNICAL UNIVERSITY OF ATHENS SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT OF INFORMATICS AND COMPUTER TECHNOLOGY COMPUTING SYSTEMS LABORATORY Algorithms Design for the Parallelization
More informationOnline Aggregation over Trees
Online Aggregation over Trees C. Greg Plaxton, Mitul Tiwari University of Texas at Austin Praveen Yalagandula HP Labs Abstract Consider a distributed network with nodes arranged in a tree, and each node
More informationChap. 3. Chap. 3. Recall and Precision Alternative Measures. TREC Collection CACM and ISI Collections CFC (Cystic Fibrosis Collection)
b!"$#%&'(!) *,+.-0/1-0/2 3547698;:'=?@A8;BC
More informationSFU CMPT Lecture: Week 9
SFU CMPT-307 2008-2 1 Lecture: Week 9 SFU CMPT-307 2008-2 Lecture: Week 9 Ján Maňuch E-mail: jmanuch@sfu.ca Lecture on July 8, 2008, 5.30pm-8.20pm SFU CMPT-307 2008-2 2 Lecture: Week 9 Binary search trees
More information) $ G}] }O H~U. G yhpgxl. Cong
» Þ åî ïî á ë ïý þý ÿ þ ë ú ú F \ Œ Œ Ÿ Ÿ F D D D\ \ F F D F F F D D F D D D F D D D D FD D D D F D D FD F F F F F F F D D F D F F F D D D D F Ÿ Ÿ F D D Œ Ÿ D Ÿ Ÿ FŸ D c ³ ² í ë óô ò ð ¹ í ê ë Œ â ä ã
More information1. Oracle Mobile Agents? 2. client-agent-server client-server
1. Oracle Mobile Agents?!"#$ application software system%. &'( )'*+, -. */0 1 23 45 678 9:; >?, %@ +%. - 6A(mobility) : B? CDE@ F GH8!" * channel #I 1 = / 4%. ()'*, &', LAN) - * application
More informationWeighted Pushdown Systems and their Application to Interprocedural Dataflow Analysis
Weighted Pushdown Systems and their Application to Interprocedural Dataflow Analysis ¾ ½ Thomas Reps ½, Stefan Schwoon ¾, and Somesh Jha ½ Comp. Sci. Dept., University of Wisconsin; reps,jha@cs.wisc.edu
More informationMechanical Verification of Transaction Processing Systems
Mechanical Verification of Transaction Processing Systems Dmitri Chkliaev Ý Jozef Hooman Þ Ý Dept. of Computing Science Eindhoven University of Technology The Netherlands e-mail: dmitri,wsstok @win.tue.nl
More informationGraph Traversal. 1 Breadth First Search. Correctness. find all nodes reachable from some source node s
1 Graph Traversal 1 Breadth First Search visit all nodes and edges in a graph systematically gathering global information find all nodes reachable from some source node s Prove this by giving a minimum
More informationOn the Analysis of Interacting Pushdown Systems
On the Analysis of Interacting Pushdown Systems Vineet Kahlon NEC Labs America, Princeton, NJ 08540, USA. ÐÓÒҹРºÓÑ Aarti Gupta NEC Labs America, Princeton, NJ 08540, USA. ÙÔØҹРºÓÑ Abstract Pushdown
More informationOptimal Static Range Reporting in One Dimension
of Optimal Static Range Reporting in One Dimension Stephen Alstrup Gerth Stølting Brodal Theis Rauhe ITU Technical Report Series 2000-3 ISSN 1600 6100 November 2000 Copyright c 2000, Stephen Alstrup Gerth
More informationMonotonicity testing over general poset domains
Monotonicity testing over general poset domains [Extended Abstract] Eldar Fischer Technion Haifa, Israel eldar@cs.technion.ac.il Sofya Raskhodnikova Ý LCS, MIT Cambridge, MA 02139 sofya@mit.edu Eric Lehman
More informationLecture 5 C Programming Language
Lecture 5 C Programming Language Summary of Lecture 5 Pointers Pointers and Arrays Function arguments Dynamic memory allocation Pointers to functions 2D arrays Addresses and Pointers Every object in the
More informationViewpoint-Invariant Learning and Detection of Human Heads
Viewpoint-Invariant Learning and Detection of Human Heads M. Weber Ý W. Einhäuser Ü M. Welling Þ P. Perona ÝÞ California Institute of Technology Ý Dept. of Computation and Neural Systems Þ Dept. of Electrical
More informationDesign and Implementation of Generics for the.net Common Language Runtime
Design and Implementation of Generics for the NET Common Language Runtime Andrew Kennedy Don Syme Microsoft Research, Cambridge, UK ÒÒ ÝÑ Ñ ÖÓ Ó ØºÓÑ Abstract The Microsoft NET Common Language Runtime
More information