This document outlines a proposal for a sponsorship of the Rolling Stones' 40th anniversary tour in 2002. Some key details include:
- The tour would visit over 90 cities across the US, Europe, Japan, China, South America, and Mexico, with multiple shows in many cities.
- Marketing activation platforms would provide on-site branding opportunities at concerts and off-site media benefits like logo inclusion in advertising.
- The sponsorship would entitle the sponsor to above-title positioning in materials and a $5 million media budget.
- It presents a unique opportunity to harness the power of the Rolling Stones brand and drive awareness, sales, and relationships through custom promotions.
Este documento presenta una monografía sobre Brasil escrita por un estudiante. Incluye una introducción que describe a Brasil como el país más grande de América del Sur, con una gran diversidad geográfica y cultural. También incluye secciones sobre la historia, forma de gobierno, población, economía y gastronomía de Brasil.
Mode Real Estate Management Services is a boutique commercial property management company located in Old Town Scottsdale, Arizona. They offer full-service property management including tenant retention programs, maintenance, accounting, and more. Mode manages multiple properties totaling over [NUMBER] square feet, and their experienced team and customized approach helps minimize expenses and maximize property values.
Portafolio de evidencias 1 er lalo guerrerolaloguerrero12
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Este portafolio de evidencias contiene varios documentos que resumen información sobre la historia y componentes de las computadoras, incluyendo trípticos, tablas, carteles, separadores y tutoriales sobre sistemas operativos, procesadores de texto y hojas de cálculo. También incluye información sobre protección contra virus, respaldos e importancia de dispositivos de entrada, salida y almacenamiento.
El documento habla sobre un blog sobre fútbol. Explica las reglas básicas como el tama?o de la cancha y el balón, el número de jugadores y cambios permitidos. El objetivo del blog es motivar a las personas a practicar este deporte tan popular en todo el mundo y resolver cualquier duda sobre las reglas. Incluye la dirección del blog.
Recruitment selection-process-methods-and-steps-1207897252784197-9samreen shah
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The document discusses recruitment, selection processes, methods, and the use of psychological testing in employee selection. It provides details on:
- The key steps in recruitment including identifying job requirements, attracting applicants, screening and selecting candidates.
- Common sources for recruiting such as present employees, agencies, and advertising.
- The selection process including preliminary interviews, tests, employment interviews and assessments, reference checks, and making a final selection.
- The use of psychological tests to objectively assess traits, minimize costs from bad hires, and ensure good person-job fit. It outlines types of tests and their appropriate uses.
This thesis proposes GraphIVM, a novel approach for incremental view maintenance (IVM) that leverages a non-relational join graph data structure. The join graph compactly represents how tuples join with each other and avoids redundancies of existing IVM approaches that materialize auxiliary views as relational tables. GraphIVM filters base table diffs using the join graph and incrementally maintains the view more efficiently. Experiments show GraphIVM significantly outperforms state-of-the-art IVM approaches for complex views, with its speedup increasing for higher fanout and more joins. The join graph and GraphIVM provide an efficient non-relational alternative for accelerating IVM.
The document presents a greedy algorithm for test suite minimization aimed at reducing the number of tests run on large software systems by eliminating redundant test cases. It defines a problem where tests cover multiple requirements, and provides a method to identify the minimum set of tests needed to satisfy all requirements using a test-requirement matrix. The proposed solution leverages delayed greedy algorithms and concept analysis to optimize test selection, ultimately addressing the NP-hard nature of the problem.
The document discusses two social constructivist programs - Fostering a Community of Learners (FCL) and Schools for Thought (SFT). FCL focuses on literacy and biology development through reflection, discussion, and collaboration between students and experts. SFT combines aspects of the Jasper Project, FCL, and CSILE to emphasize problem-based learning, extended inquiry projects, and collaboration using technology. Both programs aim to develop deeper understanding through social interaction and real-world problem solving rather than traditional instruction.
Pakistan faces a significant literacy challenge, with a national literacy rate of only 55%, ranking it 160th globally. The low transition rates between educational levels reflect further systemic issues, with barriers such as teacher shortages, poor school environments, and insecurity contributing to this situation. Urgent governmental action is required to enhance educational access and quality, particularly for children in rural areas.
5. Minimal Model Program
input output
X : 代数多様体 → MMP → (1) 極小モデル
(2) 森ファイバー空間
φ:X→Y
d = dim X > dim Y
φ?1 (p): ファノ多様体 (p ∈ Y )
6. 3 トーリック多様体
× d
T := (C ) : 代数的トーラス
T ? X (open dense): トーリック多様体
トーリック多様体 ? 扇 (多面体)
扇 (in R ? Z ) が X T のデータを持っている
d d
7. r r r r r r r
r r r r r r r
pt.
r r r r r r r u u
rrC×
r r r e r r r e rr
e (C× )2@@ u
@
r
r r r r r r r
eu @@
@
r r r r r r r
r r r r r r r
扇 ←→ トーリック多様体
8. 4 ファノ多面体
トーリック?ファノ多様体 ? ファノ多面体
P ? Rd ? Zd : 凸多面体 (頂点集合 ? Zd )
P : ファノ多面体
??
?
1. P ∩ Z = {0}
d
2. 任意の facet F ? P に対して,
F の頂点集合は Zd の基底をなす
9. r r r r r r r r r r r r r r
r r r r r r r r r r r r r r
r r r r r r r r r r r r r r
r r r?b r
? r r r r ??? r r
?b r
r?
?? ?
r r r?
r ?r r r r r ???r
r r r r?
r r r r r r r r r r r r r r
r r r r r r r r r r r r r r
ファノ多面体 例 not Fano
conv{e1 , . . . , ed , ?(e1 + · · · + ed )}: d 次元単体
d 次元ファノ多面体 ? d 次元射影空間 Pd
11. ~ P1
d=1 X= P = [?1, 1]
?1
s c +1
s
d = 2 X ~ P2 , F1 , S7 , S6 , P1 × P1
=
S: トーリック?デルペッツォ曲面
if ?φ : S → S: 一点爆発 (blow-up)
=? S: デルペッツォ曲面
12. blow-up (一般)
F : face {v1 , . . . , vm }: 頂点集合
′
P : v := v1 + · · · + vm を付け加えて星状細分
φ : X ′ → X: blow-up
1. X は一般のトーリックで良い
2. X ′ はファノとは限らない
¨ rv2 ¨ r tv := v1 + v2
¨ d ¨
F ←
d
P b drv1 b r P′
13. s s
?d ?d
? c ds ← ? c ds
s
?¨¨ ¨ ¨ ¨
s
¨
? s
¨ ¨
P2 F1
↑
s s s s
?d ?d ?
? c ds ←
s ? c ds ←
s ? c
s s
d ? ? ?
ds 1
? s ?
s s ?
s
P × P1 S7 S6
14. d = 3 Batyrev (1982), 渡辺?渡辺 (1982)
分類法
ピカール数 (=頂点の数 ? 次元) ≤ 5
=? 小田?三宅の分类表をチェック
小田?三宅 (1978)
射影的トーリック多様体の分類
for d = 3 and ピカール数 ≤ 5
15. d = 4 Batyrev (1999), 佐藤 (2000)
分類法
トーリック森理論
∑n
森コーン NE(X) = i=1 R≥0 [Ci ]
Ri = R≥0 [Ci ]: 端射線 Ci : extremal curve
φRi : X → Y : 端射収縮写像
?? Ci をつぶす
16. 高次元のファノ多面体 (扇) を表示する方法:
Primitive relation
S ? {P の頂点 }: Primitive collection
??
1. S 自身は面の頂点集合ではない
2. S の真部分集合はある面の頂点集合である
minimal non-face
28. 分类表
d 1 2 3 4
# ファノ多面体 1 5 18 124
d 5 6 7 8 ···
# 866 7, 622 72, 256 749, 892 ···
? d=7 平均的な PC で一日以内 (2007)
? d=8 fast PC で二週間 (2007)
29. 8 special facet and embedding
P : ファノ多面体 {v1 , . . . , vm }: P の頂点集合
F ? P : special facet
??
v1 + · · · + vm ∈ F (の生成するコーン)
{w1 , . . . , wd }: F の頂点集合
v1 + · · · + vm = a1 w1 + · · · + ad wd
(a1 , . . . , ad ≥ 0)
30. ※ special facet は唯一とは限らない
s s
?d ?d
? c ¨s
d ? c ds
s ¨
?¨¨ ¨¨
s
¨
? s
¨
s s s s
?d ?d ?
? c ds
s ? c ds
s ? c
s s
d ? ? ?
ds
? s ?
s s s
?
31. uF ∈ (Rd )? : F を定める元 F = {?uF , ?? = 1}
{uF , . . . , uF }: 双対基底 for {w1 , . . . , wd }
w1 wd
定理 F : special facet
=? for ?v ∈ P 頂点, ?d ≤ ?uF , v? ≤ 1 and
?uF , v? = 1 ? 0 ≤ ?uF , v? ≤ 1
wi
?uF , v? = 0 ? ? 1 ≤ ?uF , v? ≤ d ? 1
wi
?uF , v? < 0 ? ?uF , v? ≤ ?uF , v? ≤ d + ?uF , v?
wi
for ?i
32. {w1 , . . . , wd } が標準基底になるようにする
P : ファノ多面体
=? ?Q ~ P s.t.
=
conv{e1 , . . . , ed }: special facet for Q
Q: special embedding of P
33. 例
s s
?d ?d
d
? c ¨s ? c ds
s ¨
?¨¨ ¨¨
¨
?
s ¨
s
s.e. not!
s s s s
?d ?d ?
? c ds
s ? c ds
s ? c
s s
d ? ? ?
ds? s s
? s ?
s
s.e. not! not!
34. special embedding
s s
?d ?d
? c ds
¨ ? c ds
s
rr
?¨¨ r
s
¨
? rs
s s s s s
?d e d d
? c ds
s e c ds s c ds
d ? e d
ds
? es s ds s
35. Wd ? Zd
primitive lattice point からのみなる集合
v = (a1 , . . . , ad ) ∈ Wd
? ?d ≤ a := a1 + · · · + ad ≤ 1 and
a=1? 0 ≤ ai ≤ 1
a = 0 ? ? 1 ≤ ai ≤ d ? 1
a<0? a ≤ ai ≤ d + a
for ?i
36. 定理’
P : ファノ多面体, Q: special embedding
? {Q の頂点集合 } ? Wd
? Wd は有限集合
? 全ての d 次元ファノ多面体 (の special
embedding) は Wd 内に生息している!!
? ファノ多面体の分類完成と言って良い
37. 例: W2 ? v = (a1 , a2 ) a := a1 + a2
a = 1 (0 ≤ ai ≤ 1), a = 0 (?1 ≤ ai ≤ 1),
a = ?1 (?1 ≤ ai ≤ 1), a = ?2 (?2 ≤ ai ≤ 0)
s s
s s c s
s s s
#W2 = 7
s
38. special embedding
s s s s
?d ?d
s ? c ds¨ ? c ds
s
rr
?¨¨ r
s
¨ s
? s s s rs
s s s s s s
?d e d d
? c ds
s s e c ds s c ds
d ? e d
s ds
? s s es s s ds s
39. 9 全順序 for ファノ多面体
全順序 for Zd
x = (x1 , . . . , xd ), y = (y1 , . . . , yd ) ∈ Z d
x ? y ??
(?(x1 + · · · + xd ), x1 , . . . , xd )
≤lex (?(y1 + · · · + yd ), y1 , . . . , yd )
41. 全順序 for 有限集合 of Zd
X, Y ? Z : 有限部分集合
d
X ? Y ?? 以下のいずれかが成立
1. X = ?
2. X, Y ?= ? and min X ? min Y
3. X, Y ?= ? and min X = min Y and
X {min X} ? Y {min Y }