Level II Placements 1st Rotation 2nd Rotation FW Site 1 1 Student 1- 1st Choice 1 Student 2- 1st Choice FW Site 1 1 Student 1- 1st 0 Student 2- 1st FW Site 2 1 Student 2- 1st Choice Student 3- 2nd Choice Student 4- 3rd Choice 1 Student A- 1st Student B- 2nd choice Student C- 3rd Choice FW Site 3 1 Student 4- 1st Choice Student 5- 1st Choice Student 6- 1st Choice Student 7-2nd Choice Student 8- 2nd Choice Student 9- 3rd Choice 1 Student 2- 1st Student 3- 1st Student 4- 1st Student 5- 1st Student 6- 1st Student 7- 1st FW Site 4 0 Student 10- 1st 0 Student 11- 1st Choice FW Site 5 1 No Student 1 No Student FW Site 6 1 No Student 1 Student 11- 1st Student 12- 1st Lake Charles MC with free housing
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We can form multi-hop relationships from RoloDex cards. AC confident if most of the fF related to every aA, are also related to every cC. F is the Focus Entity and “most” means at least a MinimumConfidence ratio. TD (P=k) CT DT (P=k) CD A confident DThk rule means: 1 0 0 1 0 1 1 … 1 0 0 3 1 … 9 1 0 0 0 … 0 0 1 3 0 0 0 T Is there a high payoff research area here? 1 1 1 … 9 1 0 0 1 … 7 0 0 0 0 0 0 0 1 0 D A confident DPhk rule means: C 1 DP 1 0 0 (T=k) 0 1 1 A D DT (P=h) … 3 T C 1 TP 1 0 0 (D=k) 0 1 1 0 … 3 0 1 … 7 0 0 0 0 0 0 0 1 0 P 1 … 7 Market Basket RoloDex w different Cust-Item card for each day 1 … 0 0 0 … 3 0 0 0 9 0 0 1 1 1 … 3 A I 0 0 0 0 1 1 1 … 0 0 3 … 0 0 0 9 0 1 0 0 1 1 1 … 0 0 9 1 … 3 1 0 0 0 … 0 0 1 9 0 0 0 D A T TD (P=h) 0 0 1 … 3 0 0 0 0 0 0 0 1 0 C Conf Buy12 rule: Custs who Buy A on Day=1, Buy B on Day=2 w hi prob Buys (Day=1) 7 … … … 2 2 2 1 1 1 Pos Term 1 D 2 D 3 1 … 9 1 2 Pos 3 T … 7 1 … 7 1 0 0 1 … 9 0 0 0 0 0 0 0 1 0 … 7 T A high fraction of the Terms, tT in Doc=h which occur at every Pos, p A, also occur at every Pos, pC in Doc=k Is this a high payoff research area? AP PT (D=h) Buys (Day=4) EI DC 1 0 0 1 0 0 0 0 1 1 … 0 1 1 … 0 0 1 1 0 0 3 1 0 0 3 0 0 0 1 … 9 1 … 9 1 0 0 0 … 0 0 1 3 0 0 0 0 0 0 0 0 0 0 1 0 Buys day=3 Protein-Protein Interaction RoloDex (different card for each interaction in some pathway) … 9 1 0 0 0 … 0 0 1 3 0 0 0 Gene Gene Interaction=k I I Buys (Day=2) Conf Buy123 pathway: Most custs who Buy A Day=1 Buy B Day=2. Most of those custs Buy all of D on Day=3 1 9 P A confident PThk rule means: C 1 PT 1 0 0 (D=k) 0 1 1 D 1 AI Buys day=1 … Term=h in every Pos, pA, also have Term=k in every Pos. pC. 0 I 2 Confident TDhk rule means a high fraction of the Documents, dD having in Position=h, every Term, t A, also have in Position=k, every Term, t C. Again, A,C must be singletons. Hi payoff? It suggests in 1-hop ARM: Conf TD rules: hi fraction of Docs, dD having every term t A also have every term t C. Again, A,C must be singletons. Is there a high payoff research area here? 0 C 1 P Conf PDhk rule: A high fraction of the Documents, dD having C 1 PD 1 0 0 (T=k) 0 1 1 1 7 1 Buys (Day=2) 0 1 0 Buys (Day=2) B I 1 1 9 “Buys” pathways? Cust Buys (Day=k) Item 0 Conf TPhk: Hi fraction of pP in Doc=h holding AP PD (T=h) every t A, also hold every t C in Doc=k This only makes sense for A ,C singleton Terms. Also it seems like P would have to be singleton? AT TP (D=h) 1 Hi fraction of Positions, pP which hold Term=h for every doc A, hold Term=k in Pos=p for every doc C AD DP (T=h) … 9 P 0 A high fraction of the terms, tT in Position=h of every doc A, are also in Position=k of every doc C. 1 DTPe k=1..3 PTCd DTPe k=1..9 PDCd DTPe k=1..7 TDRolodexCd 1 … 3 A I 1 0 0 1 0 1 1 … 1 0 0 3 1 … 9 0 0 0 0 0 0 0 1 0 C C Buys (Day=3) Buys (Day=1) Conf Buy1234 pathway: Some customers Buys all of A on Day=1, then most of those customers will Buy all of B on Day=2, then most of those customers will Buy all of D on Day=3 And most of those customers Buy all of E Day=4
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More on forming multi-hop relationships from RoloDex cards. AC, is confident if a high fraction of the fF which are related to every aA, are also related to every bB. F is the Focus Entity and the high fraction is the MinimumConfidence ratio. Buys (Day=2) B I Consider the Market Basket RoloDex (different Cust-Item card for each day) 1 … 3 1 … 9 0 0 0 0 0 0 0 1 0 Cust 1 … 3 Buys (Day=k) Item A I 1 0 0 1 0 1 1 … 1 0 0 3 1 … 9 0 0 0 0 0 0 0 1 0 A confident Buy12 rule means: Some customers Buys all of A on Day=1, then most of those customers will Buy all of B on Day=2 C Buys (Day=1) Buys (Day=4) EI I I Buys (Day=2) “Buys” pathways? 1 … 3 I I Buys (Day=2) 1 … 3 A I 1 0 0 1 0 1 1 … 1 0 0 3 1 … 9 0 0 0 0 0 0 0 1 0 C Buys (Day=1) 1 … 9 0 0 0 0 0 0 0 1 0 DC A I 1 0 0 1 0 1 1 … 1 0 0 3 1 … 9 0 0 0 0 0 0 0 1 0 C Buys (Day=1) 1 0 0 1 0 1 1 … 1 0 0 3 1 … 9 0 0 0 0 0 0 0 1 0 C Buys (Day=3) A confident Buy1234 pathway means: Some customers Buys all of A on Day=1, then most of those customers will Buy all of B on Day=2, then most of those customers will Buy all of D on Day=3 And most of those customers will Buy all of E on Day=4 Buys (Day=3) A confident Buy123 pathway means: Some customers Buys all of A on Day=1, then most of those customers will Buy all of B on Day=2 And most of those customers will Buy all of D on Day=3
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Need Help? • Live Chat: On the WebAssign Support page • Phone support: (800) 955-8275, and then press 1 • E-mail support: [email protected] See the WebAssign Support page at www.webassign.net/user_support/student/ for Live Chat and support hours.
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Need Help? • Live Chat: On the WebAssign Support page • Phone support: (800) 955-8275, and then press 1 • Email support: [email protected] See the WebAssign Support page at www.webassign.net/user_support/student/ for Live Chat and support hours.
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Opening Problem Problem: 100 times System.out.println("Welcome System.out.println("Welcome System.out.println("Welcome System.out.println("Welcome System.out.println("Welcome System.out.println("Welcome to to to to to to Java!"); Java!"); Java!"); Java!"); Java!"); Java!"); … … … System.out.println("Welcome to Java!"); System.out.println("Welcome to Java!"); System.out.println("Welcome to Java!");
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