Diagnosis in Fast-TAD (overlaid on BISTer-1) Ses Ses S1 S1 PLB PLB A A B B C DC TPG ORA TPG ORA CUT TPG b1,b2 CUT TPG b1,b2 CUT CUT c1,c2 c1,c2 b1,b2 D A S3 S3 S4 S4 CUT CUT CUT CUT d1,d2 d1,d2 a1,a2 a1,a2 a1,a2 CUT b1,b2 ORA S1 Theorem: Fast-TAD using BISTer-1 is 1-diagnosable • A f-faulty PLB Q config. as a TPG will have a GS of √ while Q configured as a CUT & performing its oper. functions will have GS of X. In all other cases GS is either a √ or a X a1,a2 CUT • In some cases, faults in A and C ( or B and D) ORA a1,a2 TPG ORA b1,b2 CUT CUT CUT CUT ORA TPG TPG ORA b1,b2 c1,c2 c1,c2 d1,d2 c1,c2 f-faulty PLB S2 S2 • Each PLB is tested in its two operational fn. d1,d2 CUT S3CUTS4 S2 may not be distinguishable – a 2nd test reqd. • Require 10.t1 time versus 16.t1 if both CUTs in a session are config. both their oper fns. Ses. PLB S1 S2 (C/A) (B/D) CUT b1,b2 ORA c1,c2 d1,d2 TPG √√ Xd1,d2 /√ X/√ a1,a2 X X/√ X/√ X A TPG B X √ X/√ X/√ B C X/√ X/√ X X √ √ X/√ X/√ CUT CUT c1,c2 b1,b2 C ORA D X/√ X/√ X √ CUT c1,c2 D ORA TPG Faulty PLB S1 (C/A) A √ B C D S2 (B/D) X X √
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Very Simple Weighted SP1 and SP2 K-plex Search on G6 Weighting: 0,1path nbrs of x times 3; 2path nbrs of x times 2; Until all degrees are weighted, then back to actual subgraph degrees H={123456789abc deg999923634438 H={123456789abc deg 999923634438 H={123456789abc deg 99962333886c H={123456789abc deg 996946334434 UNWEIGHTED Degrees H={123456789abc deg 333323334434 SP1 1 2 3 4 5 6 7 8 9 a b c 1 0 1 1 1 2 1 0 1 1 3 1 1 0 0 4 1 1 0 0 5 0 0 0 0 6 0 0 0 0 7 0 0 0 1 8 0 0 0 0 9 0 0 0 0 a 0 0 0 0 b 0 0 0 0 c 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0 3 3 3 3 2 3 3 3 4 4 3 4 H=15 H=7 kplex k8 x=1 after cutting x=2 H={123456789abc deg999923634438 2,3,4 H={123456789abc deg999923634438 2,3,4 x=3 H={123456789abc deg 99962333886c H=6 H=4 2plex x=3, after cut 2368 x=1 x=4 H={123456789abc deg 996946334434 H=15 H=7 kplex k8 x=2 after cutting H=3 x=4 H={123456789abc k1 deg999923634438 H={123456789abc k1 deg999923634438 H={123456789abc deg 222623338861 H=6 H=5 kplex x=1, after cut 23468 H=6 H=5 kplex x=2, after cut 23468 H=3 H=3 0plex x=3 after cut 1 (actual subgraph degrees) H=3 0plex after cut 2346 H={123456789abc deg 333669964434 H=10 H=5 5plex x=5 after cut 34 H={123456789abc deg 333669964434 x=5 H={123456789abc deg 333669998834 x=6 H={123456789abc deg 333669998834 x=6 after cut 34 H={123456789abc deg 33312333223 H=3 H=2 1plex x=6 after cut 12 SG degs 211 H={123456789abc deg 333969934434 x=7 H={123456789abc deg 333969998834x=7 after cut 34 H={123456789abc deg 333122232234 H=3 H=3 0plex x=7 after cut 1 SG degs H={123456789abc deg 33334969cc68 x=8 H={123456789abc deg 33334969cc68 x=8 after cut 34 H={123456789abc H=3 H=3 0plex deg 333123314434x=5 after cut 1 from SG degs H={123456789abc deg 333342134433 SP2 1 2 3 4 1 0 0 0 0 2 0 0 0 0 3 0 0 0 1 4 0 0 1 0 5 0 0 0 1 6 0 0 0 1 7 1 1 0 0 8 0 0 0 0 9 0 0 1 0 a 0 0 1 0 b 0 0 1 0 c 1 1 0 0 H={123456789abc deg 33632639cc9c x=9 5 6 7 8 9 a b c 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 H={123456789abc deg 33632639cc9c x=a H={123456789abc deg 33632639cc9c H=10 H=8 H a kplex k 2 x=a after cut 2,3,6 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 H={123456789abc deg 33632336cc9c x=b H={123456789abc deg 33632639cc9c H=6 H=6 H a kplex k 0 x=b after cut 2,3,6 SP3 1 2 3 4 5 6 7 8 1 0 0 0 0 2 0 0 0 0 3 0 0 0 0 4 0 0 0 0 5 1 1 0 0 6 1 1 0 0 7 0 0 1 0 8 0 0 1 1 9 1 1 0 0 a 1 1 0 0 b 1 1 0 0 c 0 0 0 1 H={123456789abc deg 66932336ccpc x=c 1 1 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 9 a b c 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SP4 1 2 3 4 1 0 0 0 0 2 0 0 0 0 3 0 0 0 0 4 0 0 0 0 5 0 0 1 0 6 0 0 1 0 7 0 0 0 0 8 1 1 0 0 9 0 0 0 1 a 0 0 0 1 b 0 0 0 1 c 0 0 0 0 5 6 7 8 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 9 a b c 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 H={123456789abc deg 33632639cc9c 2,3,6 H={123456789abc deg 66932336cc9c 2plex x=8 after cut12 SG degs H=10 H=8 H a kplex k 2 x=9 after Cutting H=6 H=6 H a kplex k 0 x=c after cut 2,3,6 By weighting the initial round we have gotten nearly perfect information for this example (G6). The weightings, 3 and 2, were arbitrarily chosen but worked here. In general, one should devise a formula to determine them. Also we could weight SP3 and etc. as well? If we have paid the price of constructing SPk k>1, this is a much simpler way to do it, as compared to the Clique Percolation method of Palla (next slide). G6 1 5 4 2 6 7 3 c 9 b 8 a
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Our BISTer-1 Architecture TPG A ORA B CUT TPG C CUT Sess  PLB  S1 ORA CUT D S2 S3 S4 S1 S2 S3 S4 Inference √ √ √ √ No faulty PLB X √ √ √ Fault not in PLB √ X √ √ Fault not in PLB √ √ X √ Fault not in PLB √ √ √ X Fault not in PLB X X √ √ Faulty C (CUT) √ X X √ Faulty D (CUT) √ √ X X Faulty A (CUT) X √ √ X Faulty B (CUT) X √ X √ Fault not in PLB √ X √ X Fault not in PLB A TPG ORA CUT CUT B CUT TPG ORA CUT X X X √ Faulty D √ X X X Faulty A C CUT CUT TPG ORA X X √ X Faulty C D ORA CUT CUT TPG X √ X X Faulty B X X X X Fault not in PLB
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BISTer-2 architecture B CUT A TPG C ORA F ORA 1 2 Y1 Y1 – output of the ORA comparing CUTs Y2 – output of the ORA comparing TPGs Theorem: BISTer-2 is 1-diagnosable Proof: Gross syndrome corresponding to Y1 for each faulty PLB is unique. E.g. Y1 is pass in section 2 only for faulty PLB A and no other PLB. Y2 E D CUT TPG OR1 => ORA 1 (Y1) OR2 => ORA 2 (Y2) S1 S2 S3 S4 S5 S6 A TPG OR2 TPG CUT OR1 CUT B CUT TPG OR2 TPG CUT OR1 C OR1 CUT TPG OR2 TPG CUT D CUT OR1 CUT TPG OR2 TPG E TPG CUT OR1 CUT TPG OR2 F OR2 TPG CUT OR1 CUT TPG Gross syndrome corresponding to Y1 Faulty PLB S1 S2 S3 S4 S5 S6 A X √ X X X X B X X √ X X X C X X X √ X X D X X X X √ X E X X X X X √ F √ X X X X X
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Our BISTer-1 Architecture Each PLB is a CUT in 2 unique sessn’s and a TPG in another unique session – this serves to uniquelyis identify the Theorem: BISTer-1 1-diagnosable faulty PLB which will have a X X √ in these sessions. Sess  PLB  S1 S2 S3 S4 S1 S2 S3 S4 Inference √ √ √ √ No faulty PLB X √ √ √ Fault not in PLB √ X √ √ Fault not in PLB √ √ X √ Fault not in PLB √ √ √ X Fault not in PLB X X √ √ Faulty C (CUT) √ X X √ Faulty D (CUT) √ √ X X Faulty A (CUT) X √ √ X Faulty B (CUT) X √ X √ Fault not in PLB √ X √ X Fault not in PLB A TPG ORA CUT CUT B CUT TPG ORA CUT X X X √ Faulty D √ X X X Faulty A C CUT CUT TPG ORA X X √ X Faulty C D ORA CUT CUT TPG X √ X X Faulty B X X X X Fault not in PLB
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BISTer-0 [M. Abramovici et. al., ITC ’99] S1 S2 S3 S4 Theorem: BISTer-0 is zero-diagnosable. A B C D TPG CUT ORA CUT Proof: The same pair of PLBs are configured as CUT TPG CUT ORA CUTs in two different sessions: PLBs A and C in S2 and S4 ORA CUT TPG CUT PLBs B and D in S1 and S3. CUT ORA CUT TPG When either PLB fails, the gross syndrome will be identical in these sessions. Faulty S1 S2 S3 S4 PLB E.g. if A fails as a CUT only, then its gross syndrome is identical to the gross syn. of √/ A √ X X C failing as a CUT only. Hence we cannot X distinguish between faulty PLBs A and C. C √/ X X √ X Thus has a complex adaptive diagnosis phase
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I. Balanced Communities • Community detection can be thought of graph clustering • Graph clustering: we cut the graph into several partitions and assume these partitions represent communities • Cut: partitioning (cut) of the graph into two (or more) sets (cutsets) – The size of the cut is the number of edges that are being cut • Minimum cut (min-cut) problem: find a graph partition such that the number of edges between the two sets is minimized Min-cut Min-cuts can be computed efficiently using the max-flow mincut theorem Min-cut often returns an imbalanced partition, with one set being a singleton 39
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BISTer-0 [M. Abramovici et. al., ITC ’99] A TPG CUT B D CUT A CUT D ORA ORA TPG CUT C B C (S2) (S1) D CUT A CUT D TPG CUT TPG B C (S3) ORA B CUT C (S4) A ORA TPG - Test Pattern Generator CUT - Cells Under Test ORA - Output Response Analyser • Exhaustive testing of CUTs • S1, S2, S3, S4 are four sessions of testing in a BISTer tile
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dist. 1 pair dist. 3 pair B CUT A TPG C OR1 Y1 F OR2 D CUT E TPG BISTer-2 Architecture (cont.) Theorem: BISTer-2 is 2-diagnosable under the assumptions: 1. No fault masking for all detailed syndromes 2. Faulty PLBs either uniformly all fail or all pass as TPG/ORA Proof: Y2 • For the case faulty PLBs fail as TPG/ORA also, possible gross syndromes (GS) are: Y1Y2 = X √ and XX • Class 1: faulty pairs corresponding to GS= X √. • 3 Class 1 pairs: (CUT,CUT)2, (CUT,OR1)1 and (OR1,CUT)1 (S1) dist. 2 Class 2 includes remaining faulty pairs (GS=XX). Class 1 pairs1• pair Class 1 BC pairs only pair Class from S1 B TPG Y1 OR1 C CUT D TPG OR1 Y1 2 1 1 A • ForS1: session , BC S1: BD GS = X X and CD GS = XS1, √ Class 1 includes CUT OR2 Y2 => Class 2 pairs => BC/CD/BD F TPG E OR2 CUT Y2 (S6) (S2) S2: GS = X √ => CD S6: GS = X √ => BC S2: GS = X X => BC/BD S6: GS = X X => BD In S1-S6 all the faulty pairs at dist. 1 & 2 will be in Class 1 and hence will be diag. CD only Class 1 pair from=> S1GS’s are distinct for all dist. 1 & 2 faulty pairs
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Definition of a Cut Let X be a set of vertices in a network that includes its source but does not include its sink, and let X, the complement of X, be the rest of the vertices including the sink. The cut induced by this partition of the vertices is the set of all the edges with a tail in X and a head in X. Capacity of a cut is defined as the sum of capacities of the edges that compose the cut.  We’ll denote a cut and its capacity by C(X,X) and c(X,X)  Note that if all the edges of a cut were deleted from the  network, there would be no directed path from source to sink Minimum cut is a cut of the smallest capacity in a given network A. Levitin Copyright © 2007 Pearson Addison-Wesley. All rights reserved. 10-35 “Introduction to the Design & Analysis of Algorithms,” 2nd ed., Ch. 10
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 Making ads less complex, yet more creative and persuasive: the effects of conventional metaphors and irony in print advertising Complex tropes vs. conventional tropes    Novel metaphors  Become conventional with use  Are more persuasive when moderately complex Conventional Metaphors  Simplify abstract concepts into something more concrete  Can affect people’s evaluations of a politician, product, or idea Metaphor vs. Irony  165 Participants (Mage = 33.71, SDage = 15.22, Rangeage = 17-70)  2 x 2 x 4 design  Participants each viewed four advertisements  Literal, conventional metaphor, irony, metaphor and irony Burgers, C., Konijn, E. A., Steen, G. J., & Iepsma, M. A. R. (2015). Making ads less complex, yet more creative and persuasive: The effects of conventional metaphors and irony in print advertising. International Journal of Advertising: The Review of Marketing Communications, 34, 515–532. Retrieved from https://login.proxy.lib.uni.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-28297-007&site=ehost-live
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Corn Treatments Treatment Groundcover Tillage Method Hybrid Residue Management 1 None Conventional Population sensitive Removed 2 None Conventional Population sensitive Not Removed 3 None Conventional Removed 4 None Conventional 5 None Conventional Population insensitive Population insensitive Nonautotoxic 6 None Conventional Nonautotoxic Not Removed 7 Bluegrass Zone tillage Population sensitive Removed 8 Bluegrass Zone tillage Removed 9 Bluegrass Zone tillage Population insensitive Nonautotoxic 10 Tall fescue Zone tillage Population sensitive Removed 11 Tall fescue Zone tillage Removed 12 Tall fescue Zone tillage Population insensitive Nonautotoxic Not removed Removed Removed Removed Managing Perennial Cover Crops for Sustainable Corn Stover Biomass Production
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Use the 3-step pruning cut for larger branches  Use for branches that are too big to support with your hand  Reduces weight of branch before final cut.  1st cut- up from bottom, 6”12” out  2nd cut-down from top, outside 1st cut  3rd cut- final cut. Near trunk, leaving branch collar intact. 2 1 3
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Definitions • A cut (S, V - S) b is a partition of vertices into disjoint sets S and V - S • An edge crosses the 8 4 c 7 d 2 S a 11 i  7 V- S  8 cut h 1 9 e 14 4 6 10 g 2 f S  V- S (S, V - S) if one endpoint is in S and the other in V – S • A cut respects a set A of edges ⟺ no edge in A crosses the cut • An edge is a light edge crossing a cut ⟺ its weight is minimum over all edges crossing the cut – For a given cut, there can be several light edges crossing it CS 477/677 - Lecture 24 16
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Definition of a Cut X - a set of vertices in a network that includes its source but does not include its sink  Xc - the complement of X, including the sink   C(X,Xc) : the cut induced by this partition of the vertices - the set of all the edges with a tail in X and a head in Xc.  c(X,Xc) : Capacity of a cut - the sum of capacities of the edges that compose the cut.  Note that if all the edges of a cut were deleted from the network, there would be no directed path from source to sink  Minimum cut is a cut of the smallest capacity in a given network 22
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Very Simple Weighted SP1 k-plex Search on G7 Weighting: 0,1path nbrs of x times 1; 2path nbrs of x times 0; 1 2 3 H=1234567890123456789012345678901234 D g9a63444523125222223222533243446bg Cut 123: 1 2 3 H=1234567890123456789012345678901234 D 9685322452322522222322243323334367 Cut 23: 1 2 3 H=1234567890123456789012345678901234 D 6675322452322522222322223323334344 Cut 24: 1 2 3 H=1234567890123456789012345678901234 D 5454322422322422222322223323334344 SP1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 H=561 H=77 kplx k484 20 kcore k77 21 22 23 24 H=120 H=38 kplx k82 25 kcore k38 26 27 28 29 H=55 H=26 kplx k24 30 kcore k26 31 32 33 H=15 H=12 kplx k3 34 kcore k12 Cut 2: 1 2 3 H=1234567890123456789012345678901234 H=10 H=10 kplx k0 D 4444322422322422222322223323334344 kcore k10 {1,2,3,4, 14} is a clique. {1,2,3,4,9,14} is a 3plex. 1 2 3 H=56789012356789012345678901234 D 232031200222021202533232435af Cut012:1 2 3 H=56789012356789012345678901234 D 20203120022202120253323233456 Cut03: 1 2 3 H=56789012356789012345678901234 D 20203120022202120223323233222 H=55 H=19 kplx k36 kcore k19 H=6 H=4 kplx k2 kcore k6 {24,32,33,34} is a 2plex 1 2 3 H=5678901235678901235678901 D 2330102000020000002111011 Cut01: 1 2 3 H=5678901235678901235678901 H=15 H=6 kplx k9 D 2330102000020000000111011 kcore k6 Cut0: 1 2 3 H=5678901235678901235678901 H=10 H=6 kplx k4 D 2330102000020000000111011 kcore k6 {5,6,7,11,17} is a 4plex 1 2 3 H=89023568901235678901 D 01000000000002111011 Cut0: 1 2 3 H=5678901235678901235678901 D 2330102000020000002111011 H=21 H=4 kplx k17 kcore k4 1 5 1 1 6 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 3 4 1 1 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6 9 0 6 3 4 4 4 5 2 3 1 2 5 2 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 4 5 6 7 8 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 9 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 5 5 4 1 4 4 4 8 5 5 2 6 6 6 1 3 Cut0: 1 2 3 H=5678901235678901235678901 D 2330102000020000002111011 1 2 3 H=89023568901235678901 D 01000000000002010011 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 6 8 7 0 0 0 0 1 5 7 2 2 1 1 2 8 9 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 1 2 3 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 3 3 3 3 3 4 5 6 7 8 9 0 1 2 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 3 2 2 2 5 3 3 2 4 3 4 4 6 1 6 8 9 1 3 5 H=21 H=4 kplx k17 kcore k4 9 8 0 2 10 4 9 2 5 11 7 1 4 8 12 13 15 2 8 9 5 Cut 1 leaves 25 only. H=19 H=4 kplex k15 kcore k4 Cut0: 2 3 H=89023568901235678901 H=19 H=4 kplex k15 D 01000000000002010001 kcore k4 Cut 0 leaves {9,31} as a 0plex G7 1 2 3 H=89023568901235678901 H=17 H=2 kplex k15 D 01000000000002010011 kcore k2 Cut 0 leaves {27,30} as a 0plex 1 2 3 H=89023568901235678901 H=14 H=0 kplex k14 D 0100000000000201001kcore k0 no edges left The expected communities are mostly not detected as kplexes or kcores. 1 2 3 4 5 6 01234567890123456789012345678901234567890123456789012345678901234 [email protected]#$ (Symbols for base 65 )
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Cut examples • • • • • • 6-9-15 cut -c 10-20 /etc/passwd cut -c -10 /etc/passwd cut -c 30- /etc/passwd cut -d ":" -f 5 /etc/passwd cut -d ":" -f 2,5 /etc/passwd cut -d ":" -f 3-5 /etc/passwd 13
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Seismic attribute-assisted interpretation of incised valley fill episodes: A case study of Anadarko Basin Red Fork interval. Yoscel Suarez*, Chesapeake Energy and The University of Oklahoma, USA Kurt J. Marfurt, The University of Oklahoma, USA Mark Falk, Chesapeake Energy, USA Al Warner , Chesapeake Energy, USA Abstract Previous Work Discrimination of valley-fill episodes and their lithology has always posed a challenge for exploration geologists and geophysicists, and the Red Fork sands in the Anadarko Basin do not fall outside of this challenge. The goal of this study is to take a new look at seismic attributes given the considerable well control that has been acquired during the past decade. By using this well understood reservoir as a natural laboratory, we calibrate the response of various attributes to a well-understood incised valley system. The extensive drilling program shows that seismic data has difficulty in distinguishing shale episodes vs. sand episodes, where the ultimate exploration goal is to find productive valley fill sands. In 1998 Lynn Peyton, Rich Bottjer and Greg Partyka published a paper in the Leading Edge describing their use of coherency and spectral decomposition to identify valley fill in the Red Fork interval in the Anadarko Basin. Their work help them identify five valley-fill sequences in order to find optimum reservoir intervals and to reduce exploration risk . Due to the discontinuity of the valley-fill episodes the mapping of such events by using conventional seismic displays is extremely challenging. Figure 3 shows one of the stratigraphic well cross-section presented by Peyton et al where the discontinuities of this complex are evident. Figure 4 shows a seismic profile that parallels the wells cross-section highlighting the same stages. The seismic section is flattened in the Novi. Since original work done in 1998 both seismic attributes and seismic geomorphology have undergone rapid advancement. The findings of this work will be applicable to nearby active areas as well as other intervals in the area that exhibit the same challenges. Using Peyton et al’s (1998) work as a starting point we generated similar displays of conventional seismic profiles and well x-sections that will become the bases of our research efforts. Figure 8 shows the geometry and extents of the different episodes of the Red Fork incised valley system based on well data interpretation and conventional seismic displays. This map will be compared to the different seismic attributes to calibrate their response. Figure 9 (a,b) show couple of well x-sections and their corresponding seismic profiles that supported the valley-fill stages map in Figure 8. Seismic attributes have undergone rapid development since the mid 1990s. In lieu of the horizon-based spectral decomposition based on the discrete Fourier transform, we use volumetric-based spectral decomposition based on matched pursuit and wavelet transforms (e.g. Liu and Marfurt,2007) . Other edge-sensitive attributes include more modern implementations of coherence, long-wavelength Sobel filters, and amplitude gradients. Figure 10 shows a horizon slice at the Red Fork level. Note that on conventional data the channel complex is identifiable. However, the use of seismic attributes may help delineate in more detail the different episodes within the same fluvial system and better define channel geomorphology. We will compare different edge detection algorithms and the advantages and disadvantages that each of them provides to the interpreter. Also, matching pursuit spectral decomposition results will be presented as well as combinations of Relative Acoustic Impedance and semblance that provide helpful information in the interpretation of this dataset. The surveys are located in west central Oklahoma. They were shot by Amoco from 19931996 and later merged into a 136 sq.mi. survey. In 1998, Chesapeake acquired many of Amoco’s Mid-continent properties including those discussed by Peyton et al. (1998). In this study we present alternative seismic attribute-assisted interpretation workflows that show the potential information that each of the geometric and amplitude-based attributes offer to the interpreter when dealing with Red Fork valley-fill episodes in the Anadarko Basin. It is important to mention that one of the biggest challenges of this dataset is the acquisition footprint, which contaminates the data and limits the resolution of some of the seismic attributes. Geological Framework Methodology A Figure 3. Stratigraphic cross-section Red Fork valley –fill complex Figure 4. Seismic profile associated to the prior crosssection. Flattened in the Novi interval By generating horizon slices in the coherency volume they were able to identify and delineate the main geometries of the incised valley (Figure 5). The event used to generated the horizon slice is the Skinner Lime above the Red Fork interval. A’ The Pennsylvanian incised valley sequence associated with the Red Fork interval has, throughout most of its extent, three major events or facies (Phase I, II, and III) which can be differentiated by log signatures, production characteristics, and gross geometry. Two additional events (Phase IV and V) are present in the eastern and northeastern headward portion of the valley, also recognizable by log signature and gross geometry. Phase II Phase III Phase V Figure 8. Red Fork incised valley geometries and valley-fill episodes The multi phase events of the Upper Red Fork Valley system were most likely caused by repeated sea level changes resulting from Pennsylvania glacial events that were probably related to the Milankovitch astronomical cycles including the changing tilt of the earth’s axis and eccentricity of the earth’s elliptical orbit. Phase I is the earliest valley event and Phase II generally has a much wider represents the narrow, initial downcutting of the valley sequence. Where present (a considerable portion of Phase I has been eroded by later events), the rocks are generally poorly correlative shales, silts, and tight sandstones overlying a basal “lag” deposit. areal distribution (up to four miles) with a variety of valley fill facies deposited which record a period of valley widening and maturation. Logs over Phase II rocks illustrate a classic fining upward pattern and shale resistivities of 10 or more ohms. Phase III rocks record the last major incisement within the valley and occur within a narrow (0.25-.05 mile wide) steep walled system that is correlative for 70 miles. This rejuvenated channel actually represents the final position of the Phase II river before base level was lowered and renewed downcutting began. Phase III reservoirs are primarily thick, blocky, porous sands at the base of the sequence that have been backfilled, reworked, and overlain by low resistivity marine shales deposited by a major transgression which drowned the valley sequence. Figure 5. Coherency horizon slice at the Red Fork level Phase V the last event before the transgression that deposited the Pink. It’s primary significance is that it either partially or completely eroded much of the Phase III Valley event. Phase V rocks are poorly developed, non productive sand and shales which also have a characteristic log signature. end of Phase III marine shale deposition. Phase IV rocks are characterized by thin, tight, interbedded sands and shales with a coal or coaly shale near the base. This facies is interpreted as an elongated lagoon/ coal swamp or possibly bay head delta as it often extends beyond the confines of the deeper valley. The Induction log signature is a very distinct “serrated” pattern with a “hot” gamma ray near the base identifying the coal or coaly shale. Pink Lime In their workflow they also estimated the spectral decomposition. They found that the 36 Hz component best represented the different valley-fill stages (Figure 6). By combining the well-data with the information from the seismic attributes they were able to delineate the extents of the different valley –fill episodes (Figure 7) and generate and integrated interpretation of the system. Lower Red Fork II III II Middle Red Fork V a) Figure 9. a) Red Fork stratigraphic cross-section. b) Seismic profile showing the stratigraphic interpretation derived from the well data Phase IV records a modest regression at the The geological framework summary is courtesy of Al Warner. Senior Geologist at Chesapeake Energy Figure 10. Conventional seismic horizon slice at the Red Fork level. The channel discernible although signal/noise ratio is affected by acquisition footprint Figure 6. Spectral decomposition (36 Hz) horizon slice at the Red Fork level Figure 7. Spectral decomposition (36 Hz) horizon slice at the Red Fork level with interpretation. III b) II V
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Making ads less complex, yet more creative and persuasive: the effects of conventional metaphors and irony in print advertising  Results  Ads with conventional metaphors seen as less complex (F(1, 151) = 44.89, p < 0.001, ηp2 = 0.23) and more creative (F(1, 151) = 15.65, p < 0.001, ηp2 = 0.09)  Ads with conventional metaphors resulted in a better brand attitude (F(1, 151) = 20.09, p < 0.001, ηp2 = 0.12) and higher purchase intention (F(1, 151) = 9.32, p < 0.01, ηp2 = 0.06)  What about metaphors do you think makes them so persuasive? Is it just that they can help make abstract or complicated things less complex, or is it something more?  How do you think conventional metaphors affect persuasion outside sales and advertising? Burgers, C., Konijn, E. A., Steen, G. J., & Iepsma, M. A. R. (2015). Making ads less complex, yet more creative and persuasive: The effects of conventional metaphors and irony in print advertising. International Journal of Advertising: The Review of Marketing Communications, 34, 515–532. Retrieved from https://login.proxy.lib.uni.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-28297-007&site=ehost-live
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44. Which of the following statements about the tax cut enacted in 1981 during the Reagan administration is correct? a. The tax cut caused the recession of 1982. b. The tax cut of 1981 and the recession of 1982 combined to produce one of the largest peace-time deficits up to that time. c. The tax cut stimulated a large increase in economic activity and led directly to the balanced budget of 1982. B. In the long run, a tax cut will increase economics growth and tax receipts. But, in the short run, the tax cut will lower government receipts. During recessions, government outlays grows while tax receipts diminish. 45
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Ratio Cut & Normalized Cut: Example B For Cut A A For Cut B Both ratio cut and normalized cut prefer a balanced partition 41
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Consistent cut A cut is a set of events. It contains one event per process. A cut C is consistent if the following property holds: Let, an b be two events. then a ∈ Consistent Cut C ∧ (b ⧼ a) ⇒ b ∈C p1 p2 p3 Cut 1 is consistent, but Cut 2 is not consistent. time
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Three dist. 3 pairs B A CUT C OR1 Y1 D CUT TPG F OR2 For faulty pairs at dist. 3, i.e., pairs AD, BE and CF, G.S. of Y1Y2 = XX in all sessions. Hence they don’t fall in Class 1 and hence are not distinguishable among themselves. Y2 To distinguish these dist. 3 pairs we compare their detailed syndromes: E AD: dS1 = dS3 (T-C in both sess’s), dS4 = dS6 (C-T in both) TPG (S1) B BISTer-2 Architecture (cont.) Similarly, BE: dS1 = dS5, dS2 = dS4 CF: dS2 = dS6, dS3 = dS5 OR2 A TPG C TPG F CUT D CUT E OR1 Y1 Thus all faulty pairs are diagnosable with high probability. Y2 (S3) These pairs are uniquely diag. except for the case when dS1 = dS3 = dS5 and dS2 = dS4 = dS6; which is a very low probability event---e.g. requires 4 v. low prob. events of the type ds(CUT, TPG) = ds(TPG, CUT) The detailed syndrome for a session is the 0/1 bit pattern observed at the ORA output (0 => match, 1 => mismatch) over all the test vectors of the TPG.
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Cut Example Cut: set of edges whose removal disconnects G Min-Cut: a cut in G of minimum cost Weight of this cut: 11 Weight of min cut: 4 3
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Very Simple Weighted SP1 and SP2 K-plex Search on G8 1 5 4 2 3 41 42 Weighting 444 0,1path neighbors (12012) times 5 47 7 44 43 40 46 8 45 6 9 39 38 53 334 2 path nbrs (39893) times 3 48 12 52 10 13 14 11 17 36 54 16 24 35 15 23 22 37 21 49 19 221 11 1 1 1 1 1 1 1 13231 00244105845697461218645954938634545429855587353534965 next cut<18 12345678901234567890123456789012345678901234567890123 x=1 221 11 1 1 1 1 1 1 1 13231 00244105845697461218645954938634545429855587353534965 instead cut<19 12345678901234567890123456789012345678901234567890123 x=1 34 20 27 25 18 51 26 G8 30 28 29 31 This gives C0={1,2,9,39,40,41,42,43} which is exactly the Intelligence Class except that v=38 (gifted) is missing. It is a kplex k8 (not that strong of a community!) 221 11 1 1 1 1 1 1 1 13231 00244105845697461218645954938634545429855587353534965 12345678901234567890123456789012345678901234567890123 x=1 Within the Intelligence Class this is the 1plex, C1={1, 2,40,41,42} ( only edge missing is (2,40) ) with C1-degrees: 4 3 3 4 4 Thus if we cut next using C1-degrees (cut 2,40) leaves the clique (0plex) C2={1,41,42} Cutting C0 and starting over: Weighting 0,1path neighbors (367) times 5 1111445 2 path nbrs (452347483) times 3 11 1 1 1 1 1 1 44544105645697461218645954938634545421675766353534965 G-C0 degs 12345678901234567890123456789012345678901234567890123 x=3 21155 1422 3 1 1 1 1 1 1 1 44522505645887163218645954938634545421675768353534965 next cut<10 12345678901234567890123456789012345678901234567890123 x=3 21155 1422 3 1 1 1 1 1 1 1 44522505645887163218645954938634545421675768353534965 next cut<12 12345678901234567890123456789012345678901234567890123 x=3 This gives C2={3,4,5,6,7, 12,13,14,15,17,23,25,31,44, 48, 53} Astronomy is 3,4,5,6,7,8,10,11,12,13,14,16,17, 44,45,46,47,48,52,53 50 Whereas, so, not a good fit! On the next slide we try again with replacement but using as starting vertex, the remaining vertex of highest degree. 33 32
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Homework revu cont cont: cat one | cut -d"," -f 1 | while read customer do grep $customer two done | cut -d"," -f 1 | while read commoncust do a=`grep $commoncust one | cut -d"," -f 2` b=`grep $commoncust two | cut -d"," -f 2` let total=a+b if [ $total -gt 100000 ] then echo -n "$commoncust buys " prod1=`grep $commoncust one |cut -d"," -f 3` prod2=`grep $commoncust two |cut -d"," -f 3` if [ $prod1 != $prod2 ] then echo $prod1 and $prod2 else echo $prod1 fi fi done 6-9-15 8
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5 Using Half-Space Cut Tree (y1 , y7) 2 • • • If the node’s left subtree is cut but the node itself is not cut, it’s v’l will be of no use and its v’r will be used to update tmpvmax. If the node is cut (so its left subtree must also be cut), its v’l and v’r will be of no use. If a node’s left subtree is not cut, both of its v’l and v’r will be used to update tmp vmax 7 (y1, y4) 1 (y6 , y7) 4 (-, y1) (y3, y4) 6 8 (- , y6) (- , y8) 3 (-, y3) V’ 1 4 3 tmpvmax= max max(y4, 0 y4 (y4,y7) 0) 7 5 2 8 6 C’ 19
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