µ(, y) Computing the Möbius fun tion µ(x, x) = 1 The Möbius fun tion is de ned b y and X µ(x, t) = 0 x < y if x6t6y 3

Size: px
Start display at page:

Download "µ(, y) Computing the Möbius fun tion µ(x, x) = 1 The Möbius fun tion is de ned b y and X µ(x, t) = 0 x < y if x6t6y 3"

Transcription

1 ÈÖÑÙØØÓÒ ÔØØÖÒ Ò Ø ÅÙ ÙÒØÓÒ ÙÖ ØÒ ÎØ ÂÐÒ Ú ÂÐÒÓÚ Ò ÐÜ ËØÒÖÑ ÓÒ ÒÖ

2 Ì ØÛÓµ

3 2314 ½¾ ½ ¾ ¾½ ¾ ½ ½¾ ¾½ ½¾ ¾½ ½ Ì ÔÓ Ø Ó ÔÖÑÙØØÓÒ ÛºÖºØº ÔØØÖÒ ÓÒØÒÑÒØ ½

4 2314 ½¾ ½ ¾ ¾½ ¾ ½ ½¾ ¾½ ½¾ ¾½ Ì ÒØÖÚÐ [12,2314] ½ ¾

5 ÓÑÔÙØÒ Ø ÅÙ ÙÒØÓÒ µ(, y) ¹½ ½ ¾ ¼ ¹½ ¹½ ¹½ ½ xty µ(x, t) = 0 x < y Ì ÅÙ ÙÒØÓÒ Ò Ý µ(x, x) = 1 Ò

6 ËÓÑ ÜÑÔÐ ½ ¾ ½ ¾ ¾½ ½¾ ½ ¾ ½ ¾ ½¾ ¾½ ½¾ ¾½ ¾½ ¾ ½ ½¾ ½ ¾ ½ ¾ ½ ½¾ µ(12,2134) = 1 µ(,132) = 0 ½ ¾ µ(132, ) = 2

7 P : ½¾ ½¾ ¾½ ¾½ ½ ¾ ½ ¾ ¾½ ÀÐе Ì ÅÙ ÙÒØÓÒ Ó P ÚÒ ÌÓÖÑ ÈÐÔ i( 1) i C ÛÖ i Ý C i ÒÙÑÖ Ó Ò Ó ÐÒØ i Ø Ò P ØØ ÓÒØÒ ÓØ Ø ÑÒÑÐ Ò ÑÜÑÐ ÐÑÒØ º

8 P : ½¾ ½¾ ¾½ ¾½ Ó Ò ÐÒØ ½ ¾ ½ ¾ ¾½ ÀÐе Ì ÅÙ ÙÒØÓÒ Ó P ÚÒ ÌÓÖÑ ÈÐÔ i( 1) i C ÛÖ i Ý C i ÒÙÑÖ Ó Ò Ó ÐÒØ i Ø Ò P ØØ ÓÒØÒ ÓØ Ø ÑÒÑÐ Ò ÑÜÑÐ ÐÑÒØ º

9 P : ½¾ ½¾ ¾½ ¾½ Ó Ò ¾ ÐÒØ ½ ¾ ½ ¾ ¾½ ÀÐе Ì ÅÙ ÙÒØÓÒ Ó P ÚÒ ÌÓÖÑ ÈÐÔ i( 1) i C ÛÖ i Ý C i ÒÙÑÖ Ó Ò Ó ÐÒØ i Ø Ò P ØØ ÓÒØÒ ÓØ Ø ÑÒÑÐ Ò ÑÜÑÐ ÐÑÒØ º

10 ÔÖÑÙØØÓÒ ÓÑÔÓ Ð Ø Ø ÖØ ÙÑ Ó ÓÖ ÑÓÖ ÒÓÒÑÔØݵ ÔÖÑÙØØÓÒ ØÛÓ ½ ¾

11 ÔÖÑÙØØÓÒ ÓÑÔÓ Ð Ø Ø ÖØ ÙÑ Ó ÓÖ ÑÓÖ ÒÓÒÑÔØݵ ÔÖÑÙØØÓÒ ØÛÓ ½ ¾

12 ÔÖÑÙØØÓÒ ÓÑÔÓ Ð Ø Ø ÖØ ÙÑ Ó ÓÖ ÑÓÖ ÒÓÒÑÔØݵ ÔÖÑÙØØÓÒ ØÛÓ ½ ¾ ½¼

13 ÔÖÑÙØØÓÒ ÓÑÔÓ Ð Ø Ø ÖØ ÙÑ Ó ÓÖ ÑÓÖ ÒÓÒÑÔØݵ ÔÖÑÙØØÓÒ ØÛÓ ½ ¾ ½½

14 ÔÖÑÙØØÓÒ ÓÑÔÓ Ð Ø Ø ÖØ ÙÑ Ó ÓÖ ÑÓÖ ÒÓÒÑÔØݵ ÔÖÑÙØØÓÒ ØÛÓ ½ ¾ = 312 ½¾

15 ÔÖÑÙØØÓÒ ÓÑÔÓ Ð Ø Ø ÖØ ÙÑ Ó ÓÖ ÑÓÖ ÒÓÒÑÔØݵ ÔÖÑÙØØÓÒ ØÛÓ ½ ¾ = ½

16 ÔÖÑÙØØÓÒ ÓÑÔÓ Ð Ø Ø ÖØ ÙÑ Ó ÓÖ ÑÓÖ ÒÓÒÑÔØݵ ÔÖÑÙØØÓÒ ØÛÓ ½ ¾ = ½

17 ÔÖÑÙØØÓÒ ÓÑÔÓ Ð Ø Ø ÖØ ÙÑ Ó ÓÖ ÑÓÖ ÒÓÒÑÔØݵ ÔÖÑÙØØÓÒ ØÛÓ ½ ¾ = ½ ¾ ÁÒÓÑÔÓ Ð ½

18 ÔÖÑÙØØÓÒ ÓÑÔÓ Ð Ø Ø ÖØ ÙÑ Ó ÓÖ ÑÓÖ ÒÓÒÑÔØݵ ÔÖÑÙØØÓÒ ØÛÓ ½ ¾ = ½ ¾ ÁÒÓÑÔÓ Ð Ï ÛÖØ π = π 1 π 2 π n ÓÒÐÝ π i ÒÓÑÔÓ Ð ½

19 ÄØ σ = σ 1 σ 2 σ m Ò π = π 1 π 2 π n ½

20 ÄØ σ = σ 1 σ 2 σ m Ò π = π 1 π 2 π n ÄØ π >i = π i+1 π i+2 π n غ ÓÖ π i, σ >i, σ i ½

21 ÄØ σ = σ 1 σ 2 σ m Ò π = π 1 π 2 π n ÄØ π >i = π i+1 π i+2 π n غ ÓÖ π i, σ >i, σ i ÊÙÖÖÒ ÄØ l 0 Ò k 1 ÑÜÑÐ Ó ØØ Ö Ø σ 1 = σ 2 = = σ l ½ Ò = π 1 = π 2 = = π k ½º ÌÒ = µ(σ, π) = 0 l k 2 µ(σ k, π >k ) l = k 1 µ(σ >k, π >k ) µ(σ k, π >k ) l k ½

22 ÄØ σ = σ 1 σ 2 σ m Ò π = π 1 π 2 π n ÄØ π >i = π i+1 π i+2 π n غ ÓÖ π i, σ >i, σ i ÊÙÖÖÒ ÄØ l 0 Ò k 1 ÑÜÑÐ Ó ØØ Ö Ø σ 1 = σ 2 = = σ l ½ Ò = π 1 = π 2 = = π k ½º ÌÒ = µ(σ, π) = 0 l k 2 µ(σ k, π >k ) l = k 1 µ(σ >k, π >k ) µ(σ k, π >k ) l k ÜÑÔÐ µ(132, ) = 0 µ(132,126453) = µ(21,4231) = 2 µ(132,13524) = µ(21,2413) µ(132,2413) = 3 ( 1) = 4 ¾¼

23 ÌÓÖÑ ËÙÔÔÓ π ÅÒ 1 ½º ÄØ k ÑÜÑÐ 1 ØØ π Ó 1 = π 2 = = π k ÌÒ º µ(σ, π) = m k i=1 j=1 µ(σ i, π 1 )µ(σ >i, π >j ) ¾½

24 ÌÓÖÑ ËÙÔÔÓ π ÅÒ 1 ½º ÄØ k ÑÜÑÐ 1 ØØ π Ó 1 = π 2 = = π k ÌÒ º µ(σ, π) = m k i=1 j=1 µ(σ i, π 1 )µ(σ >i, π >j ) Á σ = a b Ò π = c d ÛÖ c, d ½, c d ÓÖÓÐÐÖÝ ØÒ µ(σ, π) = µ(a, c) µ(b, d) ¾¾

25 ÌÓÖÑ ËÙÔÔÓ π ÅÒ 1 ½º ÄØ k ÑÜÑÐ 1 ØØ π Ó 1 = π 2 = = π k ÌÒ º µ(σ, π) = m k i=1 j=1 µ(σ i, π 1 )µ(σ >i, π >j ) Á σ = a b Ò π = c d ÛÖ c, d ½, c d ÓÖÓÐÐÖÝ ØÒ µ(σ, π) = µ(a, c) µ(b, d) Á σ ÒÓÑÔÓ Ð Ó m = 1µ ØÒ ÓÖÓÐÐÖÝ µ(σ, π) = µ(σ, π 1 ) π = π 1 π 1 π 1 µ(σ, π) = µ(σ, π 1 ) π = π 1 π 1 π 1 ½ π 1 ½µ µ(σ, π) = 0 ÓØÖÛ ¾

26 ¾ ½ ¾

27 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÙÑ Ò Û ÙÑ º ÖØ = = (3124 1) 231 = ¾ ½ ¾

28 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÙÑ Ò Û ÙÑ º ÖØ = = (3124 1) 231 = ¾ ½ ¾

29 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÖØ ÙÑ Ò Û ÙÑ º = = (3124 1) 231 = ¾ ½ ¾

30 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÖØ ÙÑ Ò Û ÙÑ º = = (3124 1) 231 = ¾ ½ ¾

31 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÖØ ÙÑ Ò Û ÙÑ º = = (3124 1) 231 = ¾ ½ ¾

32 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÖØ ÙÑ Ò Û ÙÑ º = = (3124 1) 231 = ¾ ½ ¼

33 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÖØ ÙÑ Ò Û ÙÑ º = = (3124 1) 231 = ¾ ½ ½

34 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÖØ ÙÑ Ò Û ÙÑ º = = (3124 1) 231 = ¾ ½ ËÔÖÐ ¾

35 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÖØ ÙÑ Ò Û ÙÑ º = = (3124 1) 231 = ¾ ½ ËÔÖÐ ÔÖÑÙØØÓÒ ÔÖÐ ÓÒÐÝ Ø ÚÓ Ø Ôع Ò ØÖÒ ¾½ Ò ½¾º

36 ÔÖÑÙØØÓÒ ÔÖÐ Ò ÒÖØ ÖÓÑ ½ Ý Ø ÖØ ÙÑ Ò Û ÙÑ º = = (3124 1) 231 = ¾ ½ ËÔÖÐ ÔÖÑÙØØÓÒ ÔÖÐ ÓÒÐÝ Ø ÚÓ Ø Ôع Ò ØÖÒ ¾½ Ò ½¾º ¾ ½ ÆÓØ ÔÖÐ

37 + + q : p : σ = π = 3,1,2,6,4,5,9,7,8,10,13,11,12 σ Ò π ÔÖе Ì ÔÖØÒ ØÖ Ó σ Ò π

38 ÍÒÔÖ ÓÙÖÖÒ Ó σ = 123 Ò π

39 ÌÓÖÑ Á σ Ò π Ö ÔÖÐ ÔÖÑÙØØÓÒ ØÒ µ(σ, π) = X (1) parity(x) ÛÖ Ø ÙÑ ÓÚÖ ÙÒÔÖ ÓÙÖÖÒ Ó σ Ò πº

40 ÌÓÖÑ Á σ Ò π Ö ÔÖÐ ÔÖÑÙØØÓÒ ØÒ µ(σ, π) = X (1) parity(x) ÛÖ Ø ÙÑ ÓÚÖ ÙÒÔÖ ÓÙÖÖÒ Ó σ Ò πº Ì ÓÑÔÙØ µ(σ, π) Ò ÔÓÐÝÒÓÑÐ ØÑ ÐØÓÙ ÆÓØ Þ Ó Ø ÒØÖÚÐ [σ, π] ÑÝ ÖÓÛ ÜÔÓÒÒØÐÐݺ Ø

41 ÌÓÖÑ Á σ Ò π Ö ÔÖÐ ÔÖÑÙØØÓÒ ØÒ µ(σ, π) = X (1) parity(x) ÛÖ Ø ÙÑ ÓÚÖ ÙÒÔÖ ÓÙÖÖÒ Ó σ Ò πº Ì ÓÑÔÙØ µ(σ, π) Ò ÔÓÐÝÒÓÑÐ ØÑ ÐØÓÙ ÆÓØ Þ Ó Ø ÒØÖÚÐ [σ, π] ÑÝ ÖÓÛ ÜÔÓÒÒØÐÐݺ Ø Á π ÔÖÐ ØÒ ÓÖÓÐÐÖÝ µ(σ, π) σ(π) ÛÖ σ(π) Ø ÒÙÑÖ Ó ÓÙÖÖÒ Ó σ Ò πº

42 ÌÓÖÑ Á σ Ò π Ö ÔÖÐ ÔÖÑÙØØÓÒ ØÒ µ(σ, π) = X (1) parity(x) ÛÖ Ø ÙÑ ÓÚÖ ÙÒÔÖ ÓÙÖÖÒ Ó σ Ò πº Ì ÓÑÔÙØ µ(σ, π) Ò ÔÓÐÝÒÓÑÐ ØÑ ÐØÓÙ ÆÓØ Þ Ó Ø ÒØÖÚÐ [σ, π] ÑÝ ÖÓÛ ÜÔÓÒÒØÐÐݺ Ø Á π ÔÖÐ ØÒ ÓÖÓÐÐÖÝ µ(σ, π) σ(π) ÛÖ σ(π) Ø ÒÙÑÖ Ó ÓÙÖÖÒ Ó σ Ò πº ÁÒ ÔÖØÙÐÖ Á π ÚÓ ½ ¾ ØÒ µ(σ, π) σ(π) ¼

43 ÁÒØÓÒ π[π ÄØ 1,..., π n Ø ÔÖÑÙØØÓÒ ÓØÒ Ý ÖÔÐÒ ] Ò i Ý π π i ÒÖÑÒØÒ Ø ÐØØÖ Ó π ØÖ i ØØ ØÝ Ó ÐÖÖ ØÒ ØÓ ÓÖÖ ÔÓÒÒ ØÓ π Ö i 1 Ø ÒØ Ò Ò ÑÐÐÖ ØÒ ØÓ ÓÖÖ ÔÓÒÒ ØÓ π ÔÖÑÙØØÓÒ i+1 º ½

44 ÁÒØÓÒ π[π ÄØ 1,..., π n Ø ÔÖÑÙØØÓÒ ÓØÒ Ý ÖÔÐÒ ] Ò i Ý π π i ÒÖÑÒØÒ Ø ÐØØÖ Ó π ØÖ i ØØ ØÝ Ó ÐÖÖ ØÒ ØÓ ÓÖÖ ÔÓÒÒ ØÓ π Ö i 1 Ø ÒØ Ò Ò ÑÐÐÖ ØÒ ØÓ ÓÖÖ ÔÓÒÒ ØÓ π ÔÖÑÙØØÓÒ i+1 º ÜÑÔÐ 2413[312,1,21,12] = ¾

45 ÁÒØÓÒ π[π ÄØ 1,..., π n Ø ÔÖÑÙØØÓÒ ÓØÒ Ý ÖÔÐÒ ] Ò i Ý π π i ÒÖÑÒØÒ Ø ÐØØÖ Ó π ØÖ i ØØ ØÝ Ó ÐÖÖ ØÒ ØÓ ÓÖÖ ÔÓÒÒ ØÓ π Ö i 1 Ø ÒØ Ò Ò ÑÐÐÖ ØÒ ØÓ ÓÖÖ ÔÓÒÒ ØÓ π ÔÖÑÙØØÓÒ i+1 º ÜÑÔÐ 2413[312,1,21,12] = Á π ÑÔÐ Ó ÐÒØ n Ò ÒÓÒ Ó π ÌÓÖÑ 1,..., π n π ØÒ ÓÒØÒ µ(π, π[π 1,..., π n ]) = n i=1 µ(1, π i )

46 Ð C Ó ÔÖÑÙØØÓÒ ÙѹÐÓ σ, π C σ π C

47 Ð C Ó ÔÖÑÙØØÓÒ ÙѹÐÓ σ, π C σ π C Û¹ÐÓ σ π C

48 Ð C Ó ÔÖÑÙØØÓÒ ÙѹÐÓ σ, π C σ π C Û¹ÐÓ σ π C ÐÓ ÙÖ Ð(C) Ó C Ø ÑÐÐ Ø ÙѹÐÓ Ò Û¹ Ì Ð ÓÒØÒÒ Cº ÐÓ

49 Ð C Ó ÔÖÑÙØØÓÒ ÙѹÐÓ σ, π C σ π C Û¹ÐÓ σ π C ÐÓ ÙÖ Ð(C) Ó C Ø ÑÐÐ Ø ÙѹÐÓ Ò Û¹ Ì Ð ÓÒØÒÒ Cº ÐÓ Ð({1}) Ø Ø Ó ÔÖÐ ÔÖÑÙØØÓÒ º

50 Ð C Ó ÔÖÑÙØØÓÒ ÙѹÐÓ σ, π C σ π C Û¹ÐÓ σ π C ÐÓ ÙÖ Ð(C) Ó C Ø ÑÐÐ Ø ÙѹÐÓ Ò Û¹ Ì Ð ÓÒØÒÒ Cº ÐÓ Ð({1}) Ø Ø Ó ÔÖÐ ÔÖÑÙØØÓÒ º ËÙÔÔÓ σ ÒØÖ ÓÑÔÓ Ð ÒÓÖ Û¹ ÌÓÖÑ ÄØ C ÒÝ Ø Ó ÔÖÑÙØØÓÒ º ÌÒ ÓÑÔÓ Ðº max{ µ(σ, π) : π Ð(C)} = max{ µ(σ, π) : π C}.

51 Ð C Ó ÔÖÑÙØØÓÒ ÙѹÐÓ σ, π C σ π C Û¹ÐÓ σ π C ÐÓ ÙÖ Ð(C) Ó C Ø ÑÐÐ Ø ÙѹÐÓ Ò Û¹ Ì Ð ÓÒØÒÒ Cº ÐÓ Ð({1}) Ø Ø Ó ÔÖÐ ÔÖÑÙØØÓÒ º ËÙÔÔÓ σ ÒØÖ ÓÑÔÓ Ð ÒÓÖ Û¹ ÌÓÖÑ ÄØ C ÒÝ Ø Ó ÔÖÑÙØØÓÒ º ÌÒ ÓÑÔÓ Ðº max{ µ(σ, π) : π Ð(C)} = max{ µ(σ, π) : π C}. ÓÑÔÙØØÓÒ Ó µ(σ, π) ÓÖ π Ð(C) Ò ÒØÐÝ Ì ØÓ Ø ÓÑÔÙØØÓÒ Ó Ø ÚÐÙ µ(σ, τ) ÓÖ τ Cº ÖÙ

52 ÅÓÖ Ö ÙÐØ ¼

53 µ( k¹1 2k...42, n 1 2n...42) = ( ) n+k 1 n k ÅÓÖ Ö ÙÐØ ½

54 µ( k¹1 2k...42, n 1 2n...42) = ( ) n+k 1 n k ÅÓÖ Ö ÙÐØ ÁÒ ÔÖØÙÐÖ µ(σ, π) ÙÒÓÙÒ ¾

55 µ( k¹1 2k...42, n 1 2n...42) = ( ) n+k 1 n k ÅÓÖ Ö ÙÐØ ÁÒ ÔÖØÙÐÖ µ(σ, π) ÙÒÓÙÒ Á π ÔÖÐ ØÒ µ(½, π) {0,1, 1}

56 µ( k¹1 2k...42, n 1 2n...42) = ( ) n+k 1 n k ÅÓÖ Ö ÙÐØ ÁÒ ÔÖØÙÐÖ µ(σ, π) ÙÒÓÙÒ Á π ÔÖÐ ØÒ µ(½, π) {0,1, 1} Á σ ÒÓÑÔÓ Ð Ò π = π 1 1 π 2 ØÒ µ(σ, π) = 0

57 ÇÔÒ ÔÖÓÐÑ

58 ÇÔÒ ÔÖÓÐÑ ÏÒ µ(σ, π) = 0

59 ÇÔÒ ÔÖÓÐÑ ÏÒ µ(σ, π) = 0 ÏÒ µ(σ, π) = σ(π)

60 ÇÔÒ ÔÖÓÐÑ ÏÒ µ(σ, π) = 0 ÏÒ µ(σ, π) = σ(π) ÏØ max{µ(½, π) : π = n}

61 ÇÔÒ ÔÖÓÐÑ ÏÒ µ(σ, π) 0 = ÏÒ µ(σ, π) σ(π) = ÏØ max{µ(½, π) : π n} = Á Ò σ Ö ÑÔÐ τ µ(σ, τ) 0

62 ÇÔÒ ÔÖÓÐÑ ÏÒ µ(σ, π) 0 = ÏÒ µ(σ, π) σ(π) = ÏØ max{µ(½, π) : π n} = Á Ò σ Ö ÑÔÐ τ µ(σ, τ) 0 Ò Û ÖÐØ µ(σ, ØÓ Ø ÓÑÓÐÓÝ Ó π) π] [σ, ¼

63 ÇÔÒ ÔÖÓÐÑ ÏÒ µ(σ, π) 0 = ÏÒ µ(σ, π) σ(π) = ÏØ max{µ(½, π) : π n} = Á Ò σ Ö ÑÔÐ τ µ(σ, τ) 0 Ò Û ÖÐØ µ(σ, ØÓ Ø ÓÑÓÐÓÝ Ó π) π] [σ, Ï ÒØÖÚÐ [σ, Ö ÐÐÐ π] ½

64 ÇÔÒ ÔÖÓÐÑ ÏÒ µ(σ, π) 0 = ÏÒ µ(σ, π) σ(π) = ÏØ max{µ(½, π) : π n} = Á Ò σ Ö ÑÔÐ τ µ(σ, τ) 0 Ò Û ÖÐØ µ(σ, ØÓ Ø ÓÑÓÐÓÝ Ó π) π] [σ, Ï ÒØÖÚÐ [σ, π] Ö ÐÐÐ ÓÒØÙÖ max π Sn µ(½, π) ÙÒÓÙÒ ÙÒØÓÒ Ó n ¾

65 ÌÓ Ó ØØÖ ÒÖÐ ØÓÖÑ ØØ ÓÐÚ ÓÑ Ó Ø ÓÔÒ Ò Ò ÙÒÝ ÓÑ Ó ØÓ Ò Ø ÓÚ Ö ÙÐØ ÔÖÓÐÑ º

F(jω) = a(jω p 1 )(jω p 2 ) Û Ö p i = b± b 2 4ac. ω c = Y X (jω) = 1. 6R 2 C 2 (jω) 2 +7RCjω+1. 1 (6jωRC+1)(jωRC+1) RC, 1. RC = p 1, p

F(jω) = a(jω p 1 )(jω p 2 ) Û Ö p i = b± b 2 4ac. ω c = Y X (jω) = 1. 6R 2 C 2 (jω) 2 +7RCjω+1. 1 (6jωRC+1)(jωRC+1) RC, 1. RC = p 1, p ÓÖ Ò ÊÄ Ò Ò Û Ò Ò Ö Ý ½¾ Ù Ö ÓÖ ÖÓÑ Ö ÓÒ Ò ÄÈ ÐØ Ö ½¾ ½¾ ½» ½½ ÓÖ Ò ÊÄ Ò Ò Û Ò Ò Ö Ý ¾ Á b 2 < 4ac Û ÒÒÓØ ÓÖ Þ Û Ö Ð Ó ÒØ Ó Û Ð Ú ÕÙ Ö º ËÓÑ Ñ ÐÐ ÕÙ Ö Ö ÓÒ Ò º Ù Ö ÓÖ ½¾ ÓÖ Ù Ö ÕÙ Ö ÓÖ Ò ØÖ Ò Ö ÙÒØ ÓÒ

More information

ÁÒØÖÓÙØÓÒ ÈÖÓÐÑ ØØÑÒØ ÓÚÖÒ¹ ÐØÖ Ò ËÑÙÐØÓÒ Ê ÙÐØ ÓÒÐÙ ÓÒ ÁÒÜ ½ ÁÒØÖÓÙØÓÒ ¾ ÈÖÓÐÑ ØØÑÒØ ÓÚÖÒ¹ ÐØÖ Ò ËÑÙÐØÓÒ Ê ÙÐØ ÓÒÐÙ ÓÒ

ÁÒØÖÓÙØÓÒ ÈÖÓÐÑ ØØÑÒØ ÓÚÖÒ¹ ÐØÖ Ò ËÑÙÐØÓÒ Ê ÙÐØ ÓÒÐÙ ÓÒ ÁÒÜ ½ ÁÒØÖÓÙØÓÒ ¾ ÈÖÓÐÑ ØØÑÒØ ÓÚÖÒ¹ ÐØÖ Ò ËÑÙÐØÓÒ Ê ÙÐØ ÓÒÐÙ ÓÒ ÁÒØÖÓÙØÓÒ ÈÖÓÐÑ ØØÑÒØ ÓÚÖÒ¹ ÐØÖ Ò ËÑÙÐØÓÒ Ê ÙÐØ ÓÒÐÙ ÓÒ ÙÑÔ ÐØÖ ÓÖ ÙÒÖØÒ ÝÒÑ Ý ØÑ ÛØ ÖÓÔÓÙØ º ÓÐÞ ½ º º ÉÙÚÓ ¾ Áº ÈÖÖÓ ½ ʺ ËÒ ½ ½ ÔÖØÑÒØ Ó ÁÒÙ ØÖÐ ËÝ ØÑ ÒÒÖÒ Ò Ò ÍÒÚÖ ØØ ÂÙÑ Á ØÐÐ ËÔÒ ¾ ËÓÓÐ Ó ÐØÖÐ ÒÒÖÒ

More information

ÆÓÒ¹ÒØÖÐ ËÒÐØ ÓÙÒÖÝ

ÆÓÒ¹ÒØÖÐ ËÒÐØ ÓÙÒÖÝ ÁÒØÖÐ ÓÙÒÖ Ò Ë»Ì Î ÊÐ ÔÖØÑÒØ Ó ÅØÑØ ÍÒÚÖ ØÝ Ó ÓÖ Á̳½½ ØÝ ÍÒÚÖ ØÝ ÄÓÒÓÒ ÔÖÐ ½ ¾¼½½ ÆÓÒ¹ÒØÖÐ ËÒÐØ ÓÙÒÖÝ ÇÙØÐÒ ËÙÔÖ ØÖÒ Ò Ë»Ì Ì ØÙÔ ÏÓÖÐ Ø Ë¹ÑØÖÜ ÍÒÖÐÝÒ ÝÑÑØÖ ÁÒØÖÐ ÓÙÒÖ ÁÒØÖÐØÝ Ø Ø ÓÙÒÖÝ» ÖÒ Ò ØÛ Ø ÒÒ Ú»Ú

More information

ÇÙÐ Ò ½º ÅÙÐ ÔÐ ÔÓÐÝÐÓ Ö Ñ Ò Ú Ö Ð Ú Ö Ð ¾º Ä Ò Ö Ö Ù Ð Ý Ó ËÝÑ ÒÞ ÔÓÐÝÒÓÑ Ð º Ì ÛÓ¹ÐÓÓÔ ÙÒÖ Ö Ô Û Ö Ö ÖÝ Ñ ¹ ÝÓÒ ÑÙÐ ÔÐ ÔÓÐÝÐÓ Ö Ñ

ÇÙÐ Ò ½º ÅÙÐ ÔÐ ÔÓÐÝÐÓ Ö Ñ Ò Ú Ö Ð Ú Ö Ð ¾º Ä Ò Ö Ö Ù Ð Ý Ó ËÝÑ ÒÞ ÔÓÐÝÒÓÑ Ð º Ì ÛÓ¹ÐÓÓÔ ÙÒÖ Ö Ô Û Ö Ö ÖÝ Ñ ¹ ÝÓÒ ÑÙÐ ÔÐ ÔÓÐÝÐÓ Ö Ñ ÅÙÐ ÔÐ ÔÓÐÝÐÓ Ö Ñ Ò ÝÒÑ Ò Ò Ö Ð Ö Ò Ó Ò Ö ÀÍ ÖÐ Òµ Ó Ò ÛÓÖ Û Ö Ò ÖÓÛÒ Ö Ú ½ ¼¾º ¾½ Û Åº Ä Ö Ö Ú ½ ¼¾º ¼¼ Û Äº Ñ Ò Ëº Ï ÒÞ ÖÐ Å ÒÞ ½ º¼ º¾¼½ ÇÙÐ Ò ½º ÅÙÐ ÔÐ ÔÓÐÝÐÓ Ö Ñ Ò Ú Ö Ð Ú Ö Ð ¾º Ä Ò Ö Ö Ù Ð Ý Ó ËÝÑ

More information

Radu Alexandru GHERGHESCU, Dorin POENARU and Walter GREINER

Radu Alexandru GHERGHESCU, Dorin POENARU and Walter GREINER È Ö Ò Ò Ù Ò Ò Ò ÖÝ ÒÙÐ Ö Ý Ø Ñ Radu Alexandru GHERGHESCU, Dorin POENARU and Walter GREINER Radu.Gherghescu@nipne.ro IFIN-HH, Bucharest-Magurele, Romania and Frankfurt Institute for Advanced Studies, J

More information

PH Nuclear Physics Laboratory Gamma spectroscopy (NP3)

PH Nuclear Physics Laboratory Gamma spectroscopy (NP3) Physics Department Royal Holloway University of London PH2510 - Nuclear Physics Laboratory Gamma spectroscopy (NP3) 1 Objectives The aim of this experiment is to demonstrate how γ-ray energy spectra may

More information

F(q 2 ) = 1 Q Q = d 3 re i q r ρ(r) d 3 rρ(r),

F(q 2 ) = 1 Q Q = d 3 re i q r ρ(r) d 3 rρ(r), ÁÒØÖÓÙØÓÒ ÖÐ ÕÙÖ ÑÓÐ Ê ÙÐØ ËÙÑÑÖÝ ÌÖÒ ÚÖ ØÝ ØÖÙØÙÖ Ó Ø ÔÓÒ Ò ÖÐ ÕÙÖ ÑÓÐ ÏÓ ÖÓÒÓÛ ÂÒ ÃÓÒÓÛ ÍÒÚÖ ØÝ ÃÐ ÁÒ ØØÙØ Ó ÆÙÐÖ ÈÝ ÈÆ ÖÓÛ ÛØ º ÊÙÞ ÖÖÓÐ Ò º º ÓÖÓÓÚ ÅÒ¹ÏÓÖ ÓÔ Ð ¾¼½½ ÍÒÖ ØÒÒ ÀÖÓÒ ËÔØÖ Ð ËÐÓÚÒµ ¹½¼ ÂÙÐÝ

More information

Lecture 16: Modern Classification (I) - Separating Hyperplanes

Lecture 16: Modern Classification (I) - Separating Hyperplanes Lecture 16: Modern Classification (I) - Separating Hyperplanes Outline 1 2 Separating Hyperplane Binary SVM for Separable Case Bayes Rule for Binary Problems Consider the simplest case: two classes are

More information

Proving observational equivalence with ProVerif

Proving observational equivalence with ProVerif Proving observational equivalence with ProVerif Bruno Blanchet INRIA Paris-Rocquencourt Bruno.Blanchet@inria.fr based on joint work with Martín Abadi and Cédric Fournet and with Vincent Cheval June 2015

More information

j j ( ϕ j ) p (dd c ϕ j ) n < (dd c ϕ j ) n <.

j j ( ϕ j ) p (dd c ϕ j ) n < (dd c ϕ j ) n <. ÆÆÄË ÈÇÄÇÆÁÁ ÅÌÀÅÌÁÁ ½º¾ ¾¼¼µ ÓÒÖÒÒ Ø ÒÖÝ Ð E p ÓÖ 0 < p < 1 Ý ÈÖ ËÙÒ ÚÐе Ê ÞÝ ÃÖÛµ Ò È º Ñ ÀÓÒ À º Ô ÀÒÓµ ØÖغ Ì ÒÖÝ Ð E p ØÙ ÓÖ 0 < p < 1º ÖØÖÞØÓÒ Ó Ö¹ ØÒ ÓÙÒ ÔÐÙÖ ÙÖÑÓÒ ÙÒØÓÒ Ò ØÖÑ Ó F p Ò Ø ÔÐÙÖÓÑÔÐÜ

More information

Arbeitstagung: Gruppen und Topologische Gruppen Vienna July 6 July 7, Abstracts

Arbeitstagung: Gruppen und Topologische Gruppen Vienna July 6 July 7, Abstracts Arbeitstagung: Gruppen und Topologische Gruppen Vienna July 6 July 7, 202 Abstracts ÁÒÚ Ö Ð Ñ Ø Ó Ø¹Ú ÐÙ ÙÒØ ÓÒ ÁÞØÓ Ò ÞØÓ º Ò ÙÒ ¹Ñ º ÙÐØÝ Ó Æ ØÙÖ Ð Ë Ò Ò Å Ø Ñ Ø ÍÒ Ú Ö ØÝ Ó Å Ö ÓÖ ÃÓÖÓ ½ ¼ Å Ö ÓÖ ¾¼¼¼

More information

dz k dz j. ω n = 1. supφ 1.

dz k dz j. ω n = 1. supφ 1. ÆÆÄË ÈÇÄÇÆÁÁ ÅÌÀÅÌÁÁ ½º¾ ¾¼¼µ ÖÐÐ Ð ÓÒ ÓÑÔØ ÃÐÖ ÑÒÓÐ Ý ËÛÓÑÖ ÒÛ ÃÖÛµ ØÖغ Ï ØÙÝ ÖÐÐ Ð ÓÒ ÓÑÔØ ÃÐÖ ÑÒÓÐ º ÇÙÖ Ö ÙÐØ Ò¹ ÖÐÞ ÓÑ ØÓÖÑ Ó Ù Ò Ö ÖÓÑ Ø ØØÒ Ó ÙÖ ØÓ ÖØÖÖÝ ÑÒÓÐ µ Ò Ò ÛÖ ÓÑ ÓÔÒ ÕÙ ØÓÒ ÔÓ Ý ØѺ ½º

More information

«Û +(2 )Û, the total charge of the EH-pair is at most «Û +(2 )Û +(1+ )Û ¼, and thus the charging ratio is at most

«Û +(2 )Û, the total charge of the EH-pair is at most «Û +(2 )Û +(1+ )Û ¼, and thus the charging ratio is at most ÁÑÔÖÓÚ ÇÒÐÒ ÐÓÖØÑ ÓÖ Ù«Ö ÅÒÑÒØ Ò ÉÓË ËÛØ ÅÖ ÖÓ ÏÓ ÂÛÓÖ ÂÖ ËÐÐ Ý ÌÓÑ ÌÝ Ý ØÖØ We consider the following buffer management problem arising in QoS networks: packets with specified weights and deadlines arrive

More information

½ ÅÝ Ò ØØÙØÓÒ ¾ ÁÒØÖÓÙØÓÒ ÓÒ ØØÙØÚ ÑÓÐÐÒ ÆÙÑÖÐ ÑÔÐÑÒØØÓÒ ÓÒÐÙ ÓÒ Ò ÔÖ ÔØÚ ¾»¾

½ ÅÝ Ò ØØÙØÓÒ ¾ ÁÒØÖÓÙØÓÒ ÓÒ ØØÙØÚ ÑÓÐÐÒ ÆÙÑÖÐ ÑÔÐÑÒØØÓÒ ÓÒÐÙ ÓÒ Ò ÔÖ ÔØÚ ¾»¾ ÓÖ ÁÒ ØØÙØ Ó ÌÒÓÐÓÝ ¹ ÇØÓÖ Ø ¾¼½¾ ÓÒ ØØÙØÚ ÑÓÐ ÓÙÔÐÒ Ñ Ò ÔÐ ØØÝ ÓÖ ÙÒ ØÙÖØ ÓÑØÖÐ ËÓÐÒÒ Ä ÈÆË È ËÙÔÖÚ ÓÖ Ñ ÈÇÍ ÖÓÙÞ ÌÅÁÊÁ ½ ÅÝ Ò ØØÙØÓÒ ¾ ÁÒØÖÓÙØÓÒ ÓÒ ØØÙØÚ ÑÓÐÐÒ ÆÙÑÖÐ ÑÔÐÑÒØØÓÒ ÓÒÐÙ ÓÒ Ò ÔÖ ÔØÚ ¾»¾ ½

More information

Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), Institute BW/WI & Institute for Computer Science, University of Hildesheim

Lars Schmidt-Thieme, Information Systems and Machine Learning Lab (ISMLL), Institute BW/WI & Institute for Computer Science, University of Hildesheim Course on Information Systems 2, summer term 2010 0/29 Information Systems 2 Information Systems 2 5. Business Process Modelling I: Models Lars Schmidt-Thieme Information Systems and Machine Learning Lab

More information

2 Hallén s integral equation for the thin wire dipole antenna

2 Hallén s integral equation for the thin wire dipole antenna Ú Ð Ð ÓÒÐ Ò Ø ØØÔ»» Ѻ Ö Ùº º Ö ÁÒغ º ÁÒ Ù ØÖ Ð Å Ø Ñ Ø ÎÓк ÆÓº ¾ ¾¼½½µ ½ ¹½ ¾ ÆÙÑ Ö Ð Ñ Ø Ó ÓÖ Ò ÐÝ Ó Ö Ø ÓÒ ÖÓÑ Ø Ò Û Ö ÔÓÐ ÒØ ÒÒ Ëº À Ø ÑÞ ¹Î ÖÑ ÞÝ Ö Åº Æ Ö¹ÅÓ Êº Ë Þ ¹Ë Ò µ Ô ÖØÑ ÒØ Ó Ð ØÖ Ð Ò Ò

More information

SKMM 3023 Applied Numerical Methods

SKMM 3023 Applied Numerical Methods SKMM 3023 Applied Numerical Methods Solution of Nonlinear Equations ibn Abdullah Faculty of Mechanical Engineering Òº ÙÐÐ ÚºÒÙÐÐ ¾¼½ SKMM 3023 Applied Numerical Methods Solution of Nonlinear Equations

More information

This document has been prepared by Sunder Kidambi with the blessings of

This document has been prepared by Sunder Kidambi with the blessings of Ö À Ö Ñ Ø Ò Ñ ÒØ Ñ Ý Ò Ñ À Ö Ñ Ò Ú º Ò Ì ÝÊ À Å Ú Ø Å Ê ý Ú ÒØ º ÝÊ Ú Ý Ê Ñ º Å º ² ºÅ ý ý ý ý Ö Ð º Ñ ÒÜ Æ Å Ò Ñ Ú «Ä À ý ý This document has been prepared by Sunder Kidambi with the blessings of Ö º

More information

arxiv: v1 [math.dg] 17 Nov 2009

arxiv: v1 [math.dg] 17 Nov 2009 arxiv:0911.3294v1 [math.dg] 17 Nov 2009 ½ ÓÒÓÖÑÐ Ð Ò Ø ØÐØÝ Ó ÐÚ ÛØ ÓÒ ØÒØ Ö ÓÖÖ ÑÒ ÙÖÚØÙÖ ÃÖÞÝ ÞØÓ ÒÖÞÛ Ò ÈÛ º ÏÐÞ ØÖØ ÁÒ Ø ÔÔÖ Û ØÙÝ ÙÑÒÓÐ ÛØ ÓÒ ØÒØ rø ÑÒ ÙÖ¹ ÚØÙÖ S r º Ï ÒÚ ØØ Ø ØÐØÝ Ó Ù ÙÑÒÓÐ Ò Ø

More information

INRIA Sophia Antipolis France. TEITP p.1

INRIA Sophia Antipolis France. TEITP p.1 ÌÖÙ Ø ÜØ Ò ÓÒ Ò ÓÕ Ä ÙÖ ÒØ Ì ÖÝ INRIA Sophia Antipolis France TEITP p.1 ÅÓØ Ú Ø ÓÒ Ï Ý ØÖÙ Ø Ó ÑÔÓÖØ ÒØ Å ÒÐÝ ÈÖÓÚ Ò ÌÖÙØ Ø Ò ÑÔÐ Ã Ô ÈÖÓÚ Ò ÌÖÙ Ø È Ó ÖÖÝ Ò ÈÖÓÓ µ Ò Ö ØÝ ÓÑ Ò ËÔ ÔÔÐ Ø ÓÒ TEITP p.2 ÇÙØÐ

More information

SME 3023 Applied Numerical Methods

SME 3023 Applied Numerical Methods UNIVERSITI TEKNOLOGI MALAYSIA SME 3023 Applied Numerical Methods Solution of Nonlinear Equations Abu Hasan Abdullah Faculty of Mechanical Engineering Sept 2012 Abu Hasan Abdullah (FME) SME 3023 Applied

More information

A projection preconditioner for solving the implicit immersed boundary equations

A projection preconditioner for solving the implicit immersed boundary equations NUMERICAL MATHEMATICS: Theory, Methods and Applications Numer. Math. Theor. Meth. Appl., Vol. xx, No. x, pp. -27 (200x) A projection preconditioner for solving the implicit immersed boundary equations

More information

Lund Institute of Technology Centre for Mathematical Sciences Mathematical Statistics

Lund Institute of Technology Centre for Mathematical Sciences Mathematical Statistics Lund Institute of Technology Centre for Mathematical Sciences Mathematical Statistics STATISTICAL METHODS FOR SAFETY ANALYSIS FMS065 ÓÑÔÙØ Ö Ü Ö Ì ÓÓØ ØÖ Ô Ð ÓÖ Ø Ñ Ò Ý Ò Ò ÐÝ In this exercise we will

More information

x f(t) 1 + t 1 + t 1 + u k e uk du = f(0) k Γ 1

x f(t) 1 + t 1 + t 1 + u k e uk du = f(0) k Γ 1 ½ ÌÇÌÌÆ ËÇÄÍÌÁÇÆË Ì Ö Ø ÓÐÙØÓÒ ØÓ Ø ÔÐ ØÓÒ Ó ÔÖÓÐÑ ÔÔÖÒ Ò Ø ËÔØÑÖ ¾¼¼ Ù Ò Ø ØÓ Ø ÑÑÓÖÝ Ó ÂÑ ÌÓØØÒº ÌÇÌÌÆß¼½º ¾¼¼ ¾¼ ¾¾ ÈÖÓÔÓ Ý Ó ÑÒ ÈÓÓØ ÌÙÓÖ ÎÒÙ ÆØÓÒÐ ÓÐÐ ÙÖ Ø ÊÓÑÒº ÄØ H Ø ÓÖØÓÒØÖ Ó ØÖÒÐ ABC Ò ÐØ P Ø

More information

A Glimpse into the Special Theory of Relativity

A Glimpse into the Special Theory of Relativity A Glimpse into the Special Theory of Relativity Siim Ainsaar January 013 1 Why relativity?...1 4.6 Light-cones, simultaneity and Postulates of special relativity... causality...6 3 Basic thought experiments...

More information

Multi-electron and multi-channel effects on Harmonic Generation

Multi-electron and multi-channel effects on Harmonic Generation Multi-electron and multi-channel effects on Harmonic Generation Contact abrown41@qub.ac.uk A.C. Brown and H.W. van der Hart Centre for Theoretical Atomic, Molecular and Optical Physics, Queen s University

More information

N 1 N 1 + N 2. Pr(I = I 0 ) = ˆπ(A) π(a) Pr(I A Ē) + Pr(E) π(a) Ω + δ A

N 1 N 1 + N 2. Pr(I = I 0 ) = ˆπ(A) π(a) Pr(I A Ē) + Pr(E) π(a) Ω + δ A 8 CHAPTER 1. SAMPLING AND COUNTING Thus Pr(I = ) 2/3 as required. (d) This is clearly true if V =. If V and v = maxv I 0 then, by induction Pr(I = I 0 ) = and similarly Pr(I = I 0 ) = φ if v / I 0. N 1

More information

Mean, Median, Mode, More. Tilmann Gneiting University of Washington

Mean, Median, Mode, More. Tilmann Gneiting University of Washington Mean, Median, Mode, More ÓÖ ÉÙ ÒØ Ð ÇÔØ Ñ Ð ÈÓ ÒØ ÈÖ ØÓÖ Tilmann Gneiting University of Washington Mean, Median, Mode, More or Quantiles as Optimal Point Predictors Tilmann Gneiting University of Washington

More information

! " # $! % & '! , ) ( + - (. ) ( ) * + / 0 1 2 3 0 / 4 5 / 6 0 ; 8 7 < = 7 > 8 7 8 9 : Œ Š ž P P h ˆ Š ˆ Œ ˆ Š ˆ Ž Ž Ý Ü Ý Ü Ý Ž Ý ê ç è ± ¹ ¼ ¹ ä ± ¹ w ç ¹ è ¼ è Œ ¹ ± ¹ è ¹ è ä ç w ¹ ã ¼ ¹ ä ¹ ¼ ¹ ±

More information

Monodic Temporal Resolution

Monodic Temporal Resolution Monodic Temporal Resolution ANATOLY DEGTYAREV Department of Computer Science, King s College London, London, UK. and MICHAEL FISHER and BORIS KONEV Department of Computer Science, University of Liverpool,

More information

Lecture 11: Regression Methods I (Linear Regression)

Lecture 11: Regression Methods I (Linear Regression) Lecture 11: Regression Methods I (Linear Regression) Fall, 2017 1 / 40 Outline Linear Model Introduction 1 Regression: Supervised Learning with Continuous Responses 2 Linear Models and Multiple Linear

More information

Lecture 11: Regression Methods I (Linear Regression)

Lecture 11: Regression Methods I (Linear Regression) Lecture 11: Regression Methods I (Linear Regression) 1 / 43 Outline 1 Regression: Supervised Learning with Continuous Responses 2 Linear Models and Multiple Linear Regression Ordinary Least Squares Statistical

More information

UNIQUE FJORDS AND THE ROYAL CAPITALS UNIQUE FJORDS & THE NORTH CAPE & UNIQUE NORTHERN CAPITALS

UNIQUE FJORDS AND THE ROYAL CAPITALS UNIQUE FJORDS & THE NORTH CAPE & UNIQUE NORTHERN CAPITALS Q J j,. Y j, q.. Q J & j,. & x x. Q x q. ø. 2019 :. q - j Q J & 11 Y j,.. j,, q j q. : 10 x. 3 x - 1..,,. 1-10 ( ). / 2-10. : 02-06.19-12.06.19 23.06.19-03.07.19 30.06.19-10.07.19 07.07.19-17.07.19 14.07.19-24.07.19

More information

Non-Stationary Spatial Modeling

Non-Stationary Spatial Modeling BAYESIAN STATISTICS 6, pp. 000 000 J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (Eds.) Oxford University Press, 1998 Non-Stationary Spatial Modeling D. HIGDON, J. SWALL, and J. KERN Duke

More information

SME 3023 Applied Numerical Methods

SME 3023 Applied Numerical Methods UNIVERSITI TEKNOLOGI MALAYSIA SME 3023 Applied Numerical Methods Ordinary Differential Equations Abu Hasan Abdullah Faculty of Mechanical Engineering Sept 2012 Abu Hasan Abdullah (FME) SME 3023 Applied

More information

On the Stability and Accuracy of the BGK, MRT and RLB Boltzmann Schemes for the Simulation of Turbulent Flows

On the Stability and Accuracy of the BGK, MRT and RLB Boltzmann Schemes for the Simulation of Turbulent Flows Commun. Comput. Phys. doi: 10.4208/cicp.OA-2016-0229 Vol. 23, No. 3, pp. 846-876 March 2018 On the Stability and Accuracy of the BGK, MRT and RLB Boltzmann Schemes for the Simulation of Turbulent Flows

More information

arxiv:hep-ph/ v1 10 May 2001

arxiv:hep-ph/ v1 10 May 2001 New data and the hard pomeron A Donnachie Department of Physics, Manchester University P V Landshoff DAMTP, Cambridge University DAMTP-200-38 M/C-TH-0/03 arxiv:hep-ph/005088v 0 May 200 Abstract New structure-function

More information

SKMM 3023 Applied Numerical Methods

SKMM 3023 Applied Numerical Methods UNIVERSITI TEKNOLOGI MALAYSIA SKMM 3023 Applied Numerical Methods Ordinary Differential Equations ibn Abdullah Faculty of Mechanical Engineering Òº ÙÐÐ ÚºÒÙÐÐ ¾¼½ SKMM 3023 Applied Numerical Methods Ordinary

More information

Chebyshev Spectral Methods and the Lane-Emden Problem

Chebyshev Spectral Methods and the Lane-Emden Problem Numer. Math. Theor. Meth. Appl. Vol. 4, No. 2, pp. 142-157 doi: 10.4208/nmtma.2011.42s.2 May 2011 Chebyshev Spectral Methods and the Lane-Emden Problem John P. Boyd Department of Atmospheric, Oceanic and

More information

An Example file... log.txt

An Example file... log.txt # ' ' Start of fie & %$ " 1 - : 5? ;., B - ( * * B - ( * * F I / 0. )- +, * ( ) 8 8 7 /. 6 )- +, 5 5 3 2( 7 7 +, 6 6 9( 3 5( ) 7-0 +, => - +< ( ) )- +, 7 / +, 5 9 (. 6 )- 0 * D>. C )- +, (A :, C 0 )- +,

More information

x 0, x 1,...,x n f(x) p n (x) = f[x 0, x 1,..., x n, x]w n (x),

x 0, x 1,...,x n f(x) p n (x) = f[x 0, x 1,..., x n, x]w n (x), ÛÜØ Þ ÜÒ Ô ÚÜ Ô Ü Ñ Ü Ô Ð Ñ Ü ÜØ º½ ÞÜ Ò f Ø ÚÜ ÚÛÔ Ø Ü Ö ºÞ ÜÒ Ô ÚÜ Ô Ð Ü Ð Þ Õ Ô ÞØÔ ÛÜØ Ü ÚÛÔ Ø Ü Ö L(f) = f(x)dx ÚÜ Ô Ü ÜØ Þ Ü Ô, b] Ö Û Þ Ü Ô Ñ ÒÖØ k Ü f Ñ Df(x) = f (x) ÐÖ D Ü Ü ÜØ Þ Ü Ô Ñ Ü ÜØ Ñ

More information

Calculation of the van der Waals potential of argon dimer using a modified Tang-Toennies model

Calculation of the van der Waals potential of argon dimer using a modified Tang-Toennies model J. At. Mol. Sci. doi: 10.4208/jams.011511.022111a Vol. x, No. x, pp. 1-5 xxx 2011 ½ ¾ ½¼ Calculation of the van der Waals potential of argon dimer using a modified Tang-Toennies model J. F. Peng a, P.

More information

519.8 ýý ½ ¹¼½¹¾¼¼ ¼º üº üº þ üº º Á ¹ ÇÊž¼½ µ ¾¾ ¾ ¾¼½ º º Á» º º üº üº þ üº º º º ü ¾¼½ º º ÁË Æ þ Á ¹ º ¹ º ºþº ¹ ú û ü ü µ ¹ µ ¹ ü ü µ ¹ µ

519.8 ýý ½ ¹¼½¹¾¼¼ ¼º üº üº þ üº º Á ¹ ÇÊž¼½ µ ¾¾ ¾ ¾¼½ º º Á» º º üº üº þ üº º º º ü ¾¼½ º º ÁË Æ þ Á ¹ º ¹ º ºþº ¹ ú û ü ü µ ¹ µ ¹ ü ü µ ¹ µ þ ü þ þ º þº þü ü þü ú ü ü üþ û ü ü ü þ ¹ ý üþ ü ý þ þü ü ü ý þ þü ü Á ÇÊž¼½ µ ¾¾ ¾ ¾¼½ Á Á ÅÓ ÓÛ ÁÒØ ÖÒ Ø ÓÒ Ð ÓÒ Ö Ò ÓÒ ÇÔ Ö Ø ÓÒ Ê Ö ÇÊž¼½ µ ÅÓ ÓÛ ÇØÓ Ö ¾¾¹¾ ¾¼½ ÈÊÇ ÁÆ Ë ÎÇÄÍÅ Á þü ¾¼½ 519.8 ýý 22.18

More information

u x + u y = x u . u(x, 0) = e x2 The characteristics satisfy dx dt = 1, dy dt = 1

u x + u y = x u . u(x, 0) = e x2 The characteristics satisfy dx dt = 1, dy dt = 1 Õ 83-25 Þ ÛÐ Þ Ð ÚÔÜØ Þ ÝÒ Þ Ô ÜÞØ ¹ 3 Ñ Ð ÜÞ u x + u y = x u u(x, 0) = e x2 ÝÒ Þ Ü ÞØ º½ dt =, dt = x = t + c, y = t + c 2 We can choose c to be zero without loss of generality Note that each characteristic

More information

Outline. Calorimeters. E.Chudakov 1. 1 Hall A, JLab. JLab Summer Detector/Computer Lectures http: // gen/talks/calor lect.

Outline. Calorimeters. E.Chudakov 1. 1 Hall A, JLab. JLab Summer Detector/Computer Lectures http: //  gen/talks/calor lect. Outline Calorimeters E.Chudakov 1 1 Hall A, JLab JLab Summer Detector/Computer Lectures http: //www.jlab.org/ gen/talks/calor lect.pdf Outline Outline 1 Introduction 2 Physics of Showers 3 Calorimeters

More information

Stochastic invariances and Lamperti transformations for Stochastic Processes

Stochastic invariances and Lamperti transformations for Stochastic Processes Stochastic invariances and Lamperti transformations for Stochastic Processes Pierre Borgnat, Pierre-Olivier Amblard, Patrick Flandrin To cite this version: Pierre Borgnat, Pierre-Olivier Amblard, Patrick

More information

18.06 Quiz 2 April 7, 2010 Professor Strang

18.06 Quiz 2 April 7, 2010 Professor Strang 8.06 Quiz 2 April 7, 200 Professor Strang Your PRINTED name is:. Your recitation number or instructor is 2. 3.. (33 points) (a) Find the matrix P that projects every vector b in R 3 onto the line in the

More information

Seminar to the lecture Computer-based Engineering Mathematics

Seminar to the lecture Computer-based Engineering Mathematics Seminar to the lecture Computer-based Engineering Mathematics N T S Prof. Dr.-Ing. A. Czylwik Mohammad Abdelqader, M.Sc. Room: BA 249, Tel: +49-203-379-3474 E-Mail: abdelqader@nts.uni-duisburg-essen.de

More information

Theoretical investigation of mechanism for the gas-phase reaction of OH radical and ethane

Theoretical investigation of mechanism for the gas-phase reaction of OH radical and ethane J. At. Mol. Sci. doi: 10.4208/jams.122810.011811a Vol. 2, No. 3, pp. 225-233 August 2011 Theoretical investigation of mechanism for the gas-phase reaction of OH radical and ethane Xiao-Ping Hu a, Bing-Xing

More information

Visit our WWW site:

Visit our WWW site: For copies of this Booklet and of the full Review to be sent to addresses in the Americas, Australasia, or the Far East, visit http://pdg.lbl.gov/pdgmail or write to Particle Data Group MS 50R6008 Lawrence

More information

Multi-agent learning

Multi-agent learning Multi-agent learning Ê Ò ÓÖ Ñ ÒØ Ä ÖÒ Ò Gerard Vreeswijk, Intelligent Systems Group, Computer Science Department, Faculty of Sciences, Utrecht University, The Netherlands. Gerard Vreeswijk. Last modified

More information

Analysis of positive descriptor systems

Analysis of positive descriptor systems Analysis of positive descriptor systems vorgelegt von Dipl.-Math. oec. Elena Virnik von der Fakultät II - Mathematik und Naturwissenschaften der Technischen Universität Berlin zur Erlangung des akademischen

More information

Temperature profiles with bi-static Doppler-RASS and their correction

Temperature profiles with bi-static Doppler-RASS and their correction Atmos. Meas. Tech., 5, 1399 148, 212 www.atmos-meas-tech.net/5/1399/212/ doi:1.5194/amt-5-1399-212 Author(s) 212. CC Attribution 3. License. Atmospheric Measurement Techniques Temperature profiles with

More information

Table of Contents... 4 Preface Introduction Notation Background and Related Work 11

Table of Contents... 4 Preface Introduction Notation Background and Related Work 11 ÁÄÄ Ê ÅÇ ÄË ÇÊ Ä ÊÇÅ Ì Ê Ë ÇÆÌÁÆÍÇÍË ËÌÍÊ Ê Ç ÆÁÌÁÇÆ Å Ó Ã ÙÔÔ Ð Å Ø Ö³ Ì Å Ý ¾¼¼ È ÊÌÅ ÆÌ Ç Å ÌÀ Å ÌÁ Ä Ë Á Æ Ë ÍÆÁÎ ÊËÁÌ Ç ÇÍÄÍ ÁÆÄ Æ ÍÒ Ú Ö ØÝ Ó ÇÙÐÙ Ì ØÖ Ø ÙÐØÝ Ó Ë Ò Ô ÖØÑ ÒØ Ô ÖØÑ ÒØ Ó Å Ø Ñ Ø Ð

More information

Radiative Electroweak Symmetry Breaking with Neutrino Effects in Supersymmetric SO(10) Unifications

Radiative Electroweak Symmetry Breaking with Neutrino Effects in Supersymmetric SO(10) Unifications KEKPH06 p.1/17 Radiative Electroweak Symmetry Breaking with Neutrino Effects in Supersymmetric SO(10) Unifications Kentaro Kojima Based on the work with Kenzo Inoue and Koichi Yoshioka (Department of Physics,

More information

Planning for Reactive Behaviors in Hide and Seek

Planning for Reactive Behaviors in Hide and Seek University of Pennsylvania ScholarlyCommons Center for Human Modeling and Simulation Department of Computer & Information Science May 1995 Planning for Reactive Behaviors in Hide and Seek Michael B. Moore

More information

arxiv:cs.na/ v2 14 Oct 2003

arxiv:cs.na/ v2 14 Oct 2003 arxiv:cs.na/0300 v 4 Oct 003 Smoothed Analysis of the Condition Numbers and Growth Factors of Matrices Arvind Sankar Department of Mathematics Massachusetts Institute of Technology Shang-Hua Teng Department

More information

arxiv:cs/ v4 [cs.na] 21 Nov 2005

arxiv:cs/ v4 [cs.na] 21 Nov 2005 arxiv:cs/0300v4 [cs.na] Nov 005 Smoothed Analysis of the Condition Numbers and Growth Factors of Matrices Arvind Sankar Department of Mathematics Massachusetts Institute of Technology Shang-Hua Teng Department

More information

Periodic monopoles and difference modules

Periodic monopoles and difference modules Periodic monopoles and difference modules Takuro Mochizuki RIMS, Kyoto University 2018 February Introduction In complex geometry it is interesting to obtain a correspondence between objects in differential

More information

Elastoviscoplastic Finite Element analysis in 100 lines of Matlab

Elastoviscoplastic Finite Element analysis in 100 lines of Matlab J. Numer. Math., Vol., No. 3, pp. 57 92 (22) c VSP 22 Elastoviscoplastic Finite Element analysis in lines of Matlab C. Carstensen and R. Klose Received 3 July, 22 Abstract This paper provides a short Matlab

More information

Study of coherentπ 0 photoproduction on the deuteron

Study of coherentπ 0 photoproduction on the deuteron J. At. Mol. Sci. oi:.428/jams.8.22a Vol. 2, o. 3, pp. 87-22 August 2 Stuy of coherent photoprouction on the euteron E. M. Darwish a,b,,. Akopov c, an M. A. El-Zohry a Applie Physics Department, Faculty

More information

hal , version 1-27 Mar 2014

hal , version 1-27 Mar 2014 Author manuscript, published in "2nd Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2005), New York, NY. : United States (2005)" 2 More formally, we denote by

More information

Tight Enclosure of Matrix Multiplication with Level 3 BLAS

Tight Enclosure of Matrix Multiplication with Level 3 BLAS Tight Enclosure of Matrix Multiplication with Level 3 BLAS K. Ozaki (Shibaura Institute of Technology) joint work with T. Ogita (Tokyo Woman s Christian University) 8th Small Workshop on Interval Methods

More information

Subspace angles and distances between ARMA models

Subspace angles and distances between ARMA models ÔÖØÑÒØ ÐØÖÓØÒ Ë̹ËÁËÌ»ÌÊ ß ËÙ Ô ÒÐ Ò ØÒ ØÛÒ ÊÅ ÑÓÐ ÃØÖÒ Ó Ò ÖØ ÅÓÓÖ ÅÖ ÈÙÐ Ò ÈÖÓÒ Ó Ø ÓÙÖØÒØ ÁÒØÖÒØÓÒÐ ËÝÑÔÓ ÙÑ Ó ÅØÑØÐ ÌÓÖÝ Ó ÆØÛÓÖ Ò ËÝ ØÑ ÅÌÆË µ ÈÖÔÒÒ ÖÒ ÂÙÒ ß Ì ÖÔÓÖØ ÚÐÐ Ý ÒÓÒÝÑÓÙ ØÔ ÖÓÑ ØÔº غÙÐÙÚÒºº

More information

arxiv: v1 [astro-ph] 14 Nov 2008

arxiv: v1 [astro-ph] 14 Nov 2008 Astronomy & Astrophysics manuscript no. papersfr_new c ESO 2018 October 24, 2018 Ñ ÓÒ¹Ð Ò Ð Ö Ø ÓÒ Ó Ø ËØ Ö ÓÖÑ Ø ÓÒ Ê Ø ÖÓÑ Ø ËÐÓ Ò Ø Ð Ë Ý ËÙÖÚ Ý B. Argence 1,2 and F. Lamareille 1,3 arxiv:0811.2420v1

More information

An Introduction to Optimal Control Applied to Disease Models

An Introduction to Optimal Control Applied to Disease Models An Introduction to Optimal Control Applied to Disease Models Suzanne Lenhart University of Tennessee, Knoxville Departments of Mathematics Lecture1 p.1/37 Example Number of cancer cells at time (exponential

More information

Lecture 10, Principal Component Analysis

Lecture 10, Principal Component Analysis Principal Cmpnent Analysis Lecture 10, Principal Cmpnent Analysis Ha Helen Zhang Fall 2017 Ha Helen Zhang Lecture 10, Principal Cmpnent Analysis 1 / 16 Principal Cmpnent Analysis Lecture 10, Principal

More information

LA PRISE DE CALAIS. çoys, çoys, har - dis. çoys, dis. tons, mantz, tons, Gas. c est. à ce. C est à ce. coup, c est à ce

LA PRISE DE CALAIS. çoys, çoys, har - dis. çoys, dis. tons, mantz, tons, Gas. c est. à ce. C est à ce. coup, c est à ce > ƒ? @ Z [ \ _ ' µ `. l 1 2 3 z Æ Ñ 6 = Ð l sl (~131 1606) rn % & +, l r s s, r 7 nr ss r r s s s, r s, r! " # $ s s ( ) r * s, / 0 s, r 4 r r 9;: < 10 r mnz, rz, r ns, 1 s ; j;k ns, q r s { } ~ l r mnz,

More information

AST 248, Lecture 5. James Lattimer. Department of Physics & Astronomy 449 ESS Bldg. Stony Brook University. February 12, 2015

AST 248, Lecture 5. James Lattimer. Department of Physics & Astronomy 449 ESS Bldg. Stony Brook University. February 12, 2015 AST 248, Lecture 5 James Lattimer Department of Physics & Astronomy 449 ESS Bldg. Stony Brook University February 12, 2015 The Search for Intelligent Life in the Universe james.lattimer@stonybrook.edu

More information

Final exam: Automatic Control II (Reglerteknik II, 1TT495)

Final exam: Automatic Control II (Reglerteknik II, 1TT495) Uppsala University Department of Information Technology Systems and Control Professor Torsten Söderström Final exam: Automatic Control II (Reglerteknik II, TT495) Date: October 22, 2 Responsible examiner:

More information

Applications of Discrete Mathematics to the Analysis of Algorithms

Applications of Discrete Mathematics to the Analysis of Algorithms Applications of Discrete Mathematics to the Analysis of Algorithms Conrado Martínez Univ. Politècnica de Catalunya, Spain May 2007 Goal Given some algorithm taking inputs from some set Á, we would like

More information

Seminarberichte Mathematik

Seminarberichte Mathematik Seminarberichte Mathematik Band 86-2014 Herausgegeben von den Dozentinnen und Dozenten der Mathematik Seminarberichte aus der FAKULTÄT für Mathematik und Informatik der FernUniversität in Hagen Ë Ñ Ò Ö

More information

Lecture 2. Distributions and Random Variables

Lecture 2. Distributions and Random Variables Lecture 2. Distributions and Random Variables Igor Rychlik Chalmers Department of Mathematical Sciences Probability, Statistics and Risk, MVE300 Chalmers March 2013. Click on red text for extra material.

More information

pnrqcd determination of E1 radiative transitions

pnrqcd determination of E1 radiative transitions pnrqcd determination of E radiative transitions Sebastian Steinbeißer Group T3f Department of Physics Technical University Munich IMPRS talk 3.3.7 In collaboration with: Nora Brambilla, Antonio Vairo and

More information

Personalizing Declarative Repairing Policies for Curated KBs. Ioannis Roussakis Master s Thesis Computer Science Department, University of Crete

Personalizing Declarative Repairing Policies for Curated KBs. Ioannis Roussakis Master s Thesis Computer Science Department, University of Crete ÍÒ Ú Ö ØÝ Ó Ö Ø ÓÑÔÙØ Ö Ë Ò Ô ÖØÑ ÒØ È Ö ÓÒ Ð Þ Ò Ð Ö Ø Ú Ê Ô Ö Ò ÈÓÐ ÓÖ ÙÖ Ø Ã ÁÓ ÒÒ ÊÓÙ Å Ø Ö³ Ì À Ö Ð ÓÒ Ñ Ö ¾¼½¼ È Æ ÈÁËÌÀÅÁÇ ÃÊÀÌÀË ËÉÇÄÀ Â ÌÁÃÏÆ Ã Á Ì ÉÆÇÄÇ ÁÃÏÆ ÈÁËÌÀÅÏÆ ÌÅÀÅ ÈÁËÌÀÅÀË ÍÈÇÄÇ ÁËÌÏÆ

More information

Framework for functional tree simulation applied to 'golden delicious' apple trees

Framework for functional tree simulation applied to 'golden delicious' apple trees Purdue University Purdue e-pubs Open Access Theses Theses and Dissertations Spring 2015 Framework for functional tree simulation applied to 'golden delicious' apple trees Marek Fiser Purdue University

More information

Concrete subjected to combined mechanical and thermal loading: New experimental insight and micromechanical modeling

Concrete subjected to combined mechanical and thermal loading: New experimental insight and micromechanical modeling Concrete subjected to combined mechanical thermal loading: New experimental insight micromechanical modeling Thomas Ring 1, Matthias Zeiml 1,2, Roman Lackner 3 1 Institute for Mechanics of Materials Structures

More information

Turbulence and Aeroacoustics Research Team of the Centre Acoustique Laboratoire des Fluides et d Acoustique UMR CNRS 5509, Ecole Centrale de Lyon MUSAF II Colloquium Toulouse, September 2013 Ö Ø ÓÑÔÙØ

More information

First-principles investigations on the structural, electronic and magnetic properties of Cr-doped (ZnTe) 12 clusters

First-principles investigations on the structural, electronic and magnetic properties of Cr-doped (ZnTe) 12 clusters J. At. Mol. Sci. doi: 10.4208/jams.100210.102510a Vol. 2, No. 3, pp. 262-272 August 2011 First-principles investigations on the structural, electronic and magnetic properties of Cr-doped (ZnTe) 12 clusters

More information

Tools for SUSY Summary and Outlook

Tools for SUSY Summary and Outlook Tools for SUSY Summary and Outlook Fawzi BOUDJEMA OUTLINE LAPTH-Annecy, France What s a tool and what it takes to make one How tools should talk to each other Talks and Issues discussed in Barcelona Organising

More information

Problem 1 (From the reservoir to the grid)

Problem 1 (From the reservoir to the grid) ÈÖÓ º ĺ ÙÞÞ ÐÐ ÈÖÓ º ʺ ³ Ò Ö ½ ½¹¼ ¼¹¼¼ ËÝ Ø Ñ ÅÓ Ð Ò ÀË ¾¼½ µ Ü Ö ÌÓÔ ÀÝ ÖÓ Ð ØÖ ÔÓÛ Ö ÔÐ ÒØ À Èȵ ¹ È ÖØ ÁÁ Ð ÖÒ Ø Þº ÇØÓ Ö ½ ¾¼½ Problem (From the reservoir to the grid) The causality diagram of the

More information

Minimization of Quadratic Forms in Wireless Communications

Minimization of Quadratic Forms in Wireless Communications Minimization of Quadratic Forms in Wireless Communications Ralf R. Müller Department of Electronics & Telecommunications Norwegian University of Science & Technology, Trondheim, Norway mueller@iet.ntnu.no

More information

106 Chapter 5 Curve Sketching. If f(x) has a local extremum at x = a and. THEOREM Fermat s Theorem f is differentiable at a, then f (a) = 0.

106 Chapter 5 Curve Sketching. If f(x) has a local extremum at x = a and. THEOREM Fermat s Theorem f is differentiable at a, then f (a) = 0. 5 Curve Sketching Whether we are interested in a function as a purely mathematical object or in connection with some application to the real world, it is often useful to know what the graph of the function

More information

Thermodynamics of histories: some examples

Thermodynamics of histories: some examples Thermodynamics of histories: some examples Vivien Lecomte 1,2, Cécile Appert-Rolland 1, Estelle Pitard 3, Frédéric van Wijland 1,2 1 Laboratoire de Physique Théorique, Université d Orsay 2 Laboratoire

More information

A Language for Task Orchestration and its Semantic Properties

A Language for Task Orchestration and its Semantic Properties DEPARTMENT OF COMPUTER SCIENCES A Language for Task Orchestration and its Semantic Properties David Kitchin, William Cook and Jayadev Misra Department of Computer Science University of Texas at Austin

More information

Efficient Chebyshev Spectral Method for Solving Linear Elliptic PDEs Using Quasi-Inverse Technique

Efficient Chebyshev Spectral Method for Solving Linear Elliptic PDEs Using Quasi-Inverse Technique Numer. Math. Theor. Meth. Appl. Vol. 4, No. 2, pp. 197-215 doi: 1.428/nmtma.211.42s.5 Ma 211 Efficient Chebshev Spectral Method for Solving Linear Elliptic PDEs Using Quasi-Inverse Technique Fei Liu, Xingde

More information

Effect of isotope substitution on the stereodynamics for O+H(D)Br OH(D)+Br reactions. 1 Introduction

Effect of isotope substitution on the stereodynamics for O+H(D)Br OH(D)+Br reactions. 1 Introduction J. At. Mol. Sci. doi: 10.4208/jams.052411.070811a Vol. 3, No. 2, pp. 114-121 May 2012 Effect of isotope substitution on the stereodynamics for O+H(D)Br OH(D)+Br reactions Hong Li, Bin Zheng, Ji-Qing Yin,

More information

General Neoclassical Closure Theory: Diagonalizing the Drift Kinetic Operator

General Neoclassical Closure Theory: Diagonalizing the Drift Kinetic Operator General Neoclassical Closure Theory: Diagonalizing the Drift Kinetic Operator E. D. Held eheld@cc.usu.edu Utah State University General Neoclassical Closure Theory:Diagonalizing the Drift Kinetic Operator

More information

Finite size scaling of the dynamical free-energy in the interfacial regime of a kinetically constrained model

Finite size scaling of the dynamical free-energy in the interfacial regime of a kinetically constrained model Finite size scaling of the dynamical free-energy in the interfacial regime of a kinetically constrained model Thierry Bodineau 1, Vivien Lecomte 2, Cristina Toninelli 2 1 DMA, ENS, Paris, France 2 LPMA,

More information

HOMOGENIZATION OF STRATIFIED THERMOVISCOPLASTIC MATERIALS. (µ ε (x, θ ε ) vε = f,

HOMOGENIZATION OF STRATIFIED THERMOVISCOPLASTIC MATERIALS. (µ ε (x, θ ε ) vε = f, QUARTERLY OF APPLIED MATHEMATICS VOLUME, NUMBER XXXX XXXX, PAGES S 33-569X(XX- HOMOGENIZATION OF STRATIFIED THERMOVISCOPLASTIC MATERIALS By NICOLAS CHARALAMBAKIS (Department of Civil Engineering, Aristotle

More information

Hamburger Beiträge zur Angewandten Mathematik

Hamburger Beiträge zur Angewandten Mathematik Hamburger Beiträge zur Angewandten Mathematik Shift Generated Haar Spaces on Track Fields Dedicated to the memory of Walter Hengartner Gerhard Opfer Will be published in Proceedings of a conference on

More information

Set-valued solutions for non-ideal detonation

Set-valued solutions for non-ideal detonation Set-valued solutions for non-ideal detonation The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation As Published Publisher Semenko,

More information

Thermodynamic properties of Zr 2 Al under high pressure from first-principles calculations

Thermodynamic properties of Zr 2 Al under high pressure from first-principles calculations J. At. Mol. Sci. doi: 10.4208/jams.071011.080511a Vol. 3, No. 2, pp. 160-170 May 2012 Thermodynamic properties of Zr 2 Al under high pressure from first-principles calculations X. L. Yuan a,b, D. Q. Wei

More information

Service-Oriented Architecture

Service-Oriented Architecture erl.book XXXXXXXXXXXXXXXXXXXXXXXX Page iii Thursday, March 25, 2004 1:05 PM Service-Oriented Architecture A Field Guide to Integrating XML and Web Services Thomas Erl PRENTICE HALL PROFESSIONAL TECHNICAL

More information

Part III deals with statistical modelling and inference for point pattern data, starting in this chapter with Poisson point process models.

Part III deals with statistical modelling and inference for point pattern data, starting in this chapter with Poisson point process models. 9 Poisson Models Part III deals with statistical modelling and inference for point pattern data, starting in this chapter with Poisson point process models. 9.1 Introduction Poisson point processes were

More information

Problem 1 (From the reservoir to the grid)

Problem 1 (From the reservoir to the grid) ÈÖÓ º ĺ ÙÞÞ ÐÐ ÈÖÓ º ʺ ³ Ò Ö ½ ½¹¼ ¹¼¼ ËÝ Ø Ñ ÅÓ Ð Ò ÀË ¾¼½ µ Ü Ö ËÓÐÙØ ÓÒ ÌÓÔ ÀÝ ÖÓ Ð ØÖ ÔÓÛ Ö ÔÐ ÒØ À Èȵ ¹ È ÖØ ÁÁ Ð ÖÒ Ø Þº ÇØÓ Ö ¾ ¾¼½ Problem 1 (From the reservoir to the grid) The causality diagram

More information

Modal Logics of Topological Relations

Modal Logics of Topological Relations Ö Ò ÍÒÚÖ ØÝ Ó ÌÒÓÐÓÝ ÁÒ ØØÙØ ÓÖ ÌÓÖØÐ ÓÑÔÙØÖ ËÒ Ö ÓÖ ÙØÓÑØ ÌÓÖÝ ÄÌËßÊÔÓÖØ Modal Logics of Topological Relations Carsten Lutz and Frank Wolter LTCS-Report 04-05 ÄÖ ØÙÐ ĐÙÖ ÙØÓÑØÒØÓÖ ÁÒ ØØÙØ ĐÙÖ ÌÓÖØ ÁÒÓÖÑØ

More information

Green s function, wavefunction and Wigner function of the MIC-Kepler problem

Green s function, wavefunction and Wigner function of the MIC-Kepler problem Green s function, wavefunction and Wigner function of the MIC-Kepler problem Tokyo University of Science Graduate School of Science, Department of Mathematics, The Akira Yoshioka Laboratory Tomoyo Kanazawa

More information

Factors and processes controlling climate variations at different time scales: supporting documents

Factors and processes controlling climate variations at different time scales: supporting documents Factors and processes controlling climate variations at different time scales: supporting documents Camille Risi LMD/IPSL/CNRS 3 july 2012 Outline Goals understand factors and processes controlling climate

More information

Introduction of New Seismic Ground Motion Parameter Zonation Map of China and Case Study Analysis in Lanzhou Region

Introduction of New Seismic Ground Motion Parameter Zonation Map of China and Case Study Analysis in Lanzhou Region Introduction of New Seismic Ground Motion Parameter Zonation Map of China and Case Study Analysis in Lanzhou Region Xiaojun LI Institute of Geophysics, China Earthquake Administration Korea 2012.10 Report

More information