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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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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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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Lab Meeting -1/12/2004 Event Selection Step 1: Reproduce the Run I analysis 3 Sets of Cuts PHOTON_DIJET: 1 - EM8 2 - EM18_ISO 3 - 2 Jets with Et>18 GeV 1 Jet with Et>10 GeV       A) Vertex and Met cuts (only one good vtx, good z, and Met<0.8) B) Photon Cuts ( Run I ~standard. Run II official cuts Different. No Run II tight cut for serious Photon analysis ) # Jets (0.7) > 2 3rd Jet Et_corr < 10 GeV C) Jets Cuts nd 2 Jet Et_corr > 10 GeV ( Run I used harder data, 25_ISO trigger 1st Jet Et_corr > 15 GeV With no jets requirement at trigger level) #trk > 1 on 1st and 2nd jet ( Offline cuts very close to trigger cuts, Delta R(jet1,jet2) > 2 Efficiency will suffer… )
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Simple Barrier Synchronization lock(); if(count==0) release=FALSE; /* First resets release */ count++; /* Count arrivals */ unlock(); if(count==total) /* All arrived */ { count=0; /* Reset counter */ release = TRUE; /* Release processes */ } else /* Wait for more to come */ { while (!release); /* Wait for release */ } • Problem: deadlock possible if reused – Two processes: fast and slow – Slow arrives first, reads release, sees FALSE – Fast arrives, sets release to TRUE, goes on to execute other code, comes to barrier again, resets release to FALSE, starts spinning on wait for release – Slow now reads release again, sees FALSE again – Now both processors are stuck and will never leave
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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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Changes in Prison Release Policies The major shift in parole release mechanisms over the past 25 years has been away from discretionary release and toward supervised mandatory release . Discretionary Release: In 1980, about 55% of all offenders were released from prison based on a discretionary decision by a state parole board. By 2005, only slightly more than 20 % were released from prison in this manner . Mandatory Release: During this same period, many state legislatures rewrote their parole release guidelines to create a new release mechanism, supervised mandatory release, which essentially eliminated the need for a discretionary parole board review. Once offenders completed their mandatory minimum period of incarceration, they were released from prison and placed under mandatory community supervision for a specified follow-up period. In 1980, approximately 18% of all prisoners were released in this manner, but by 2005, almost 40% of all inmates re-entered the community on supervised mandatory release.
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Three Releases Your project will be completed in three releases, with some additional follow-up afterwards. Release 1 Release 2 Release Follow-Up Release 3 3 Follow-Up •Initial Initial code code & & documentatio documentatio n n •Specification Specification ss required: required: week week of of 8/28 8/28 •Completed Completed release release due: due: week week of of 9/18 9/18 •More More thorough thorough code code & & documentation documentation •Complete Complete code code & & documentation documentation •Specifications Specifications required: required: week of of 9/18 9/18 •Specifications Specifications required: required: week week of of 10/16 10/16 •Completed Completed release release due: due: week week of of 10/16 10/16 •Completed Completed release release due: due: week week of of 11/13 11/13 3 •Peer Peer reviews: reviews: week week of of 11/27 11/27 •Faculty Faculty presentation: presentation: week week of of 12/4 12/4 •Post-mortem Post-mortem discussion: discussion: week week of of 12/11 12/11
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EffiCuts Technique #3: Equidense Cuts  Recall: HyperCuts uses equi-sized cuts to separate dense areas – create ineffectual nodes in nearby, sparse areas  Nearly-empty nodes or nodes with replicated rules Y X Z A  D B E F C G Equi-dense Cuts: Unequal cuts to distribute rules evenly among fewer children by fusing adjacent equi-sized cuts  Fine/coarse cuts in dense/sparse areas 15
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Equi-dense Cuts (contd.)  Equi-dense cuts slightly increase lookup complexity over equi-size cuts  We can handle this, details in the paper  Fusion heuristics to create equi-dense cuts  Details in the paper Equi-dense cuts reduce memory by 40% over equi-sized cuts 16
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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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Right Drug: Immediate vs. Timed Release • Timed release – Prolong absorption: Longer dosing intervals & less drug level fluctuation • • • • • • • Sustained-release (SR) Sustained-action (SA) Extended-release (ER, XR, XL) Timed-release (TR) Controlled-release (CR) Modified release (MR) Continuous-release (Contin)
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Correct Barrier Synchronization initially localSense = FALSE, release = FALSE localSense=!localSense; /* Toggle local sense */ lock(); count++; /* Count arrivals */ if(count==total){ /* All arrived */ count=0; /* Reset counter */ release=localSense; /* Release processes */ } unlock(); while(release!=localSense); /* Wait to be released */ • Release in first barrier acts as reset for second – When fast comes back it does not change release, it just waits for it to become FALSE – Slow eventually sees release is TRUE, stops waiting, does work, comes back, sets release to FALSE, and both go forward.
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Protocol Size Error Detection Retransmission Media Access Asynchronous Transmission 1 Parity Continuous ARQ Full Duplex XMODEM 132 8-bit Checksum Stop-and-wait ARQ Controlled Access XMODEM-CRC 132 8-bit CRC Stop-and-wait ARQ Controlled Access XMODEM-1K 1028 8-bit CRC Stop-and-wait ARQ Controlled Access ZMODEM * 32-bit CRC Continuous ARQ Controlled Access KERMIT * 24-bit CRC Continuous ARQ Controlled Access SDLC * 16-bit CRC Continuous ARQ Controlled Access HDLC * 16-bit CRC Continuous ARQ Controlled Access Token Ring * 32-bit CRC Stop-and wait ARQ Controlled Access Ethernet * 32-bit CRC Stop-and wait ARQ Contention SLIP * None None Full Duplex PPP * 16-bit CRC Continuous ARQ Full Duplex File Transfer Protocols Synchronous Protocols * Varies depending on message length. Figure 4-8 Data Link Protocol Summary 37
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Algorithm Description A Cut-enumeration-based method consisting of cut generation and cut selection • Cut generation traverses the network from the PI to the PO, and combines subcuts on the fanin nodes of a target node to generate all the cuts on the target node • After generating the cuts, the network is traversed from the PO to the PI, and the cuts are selected to produce the LUT mapping result. 8
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Cut Enumeration: Example All the cuts rooted on node s can be generated by combining the cuts rooted on its fanin nodes q and r. The cuts on the fanin nodes are called subcuts. Combining C1 with C2 will form a new cut Cs = {m, n, o, p} rooted on s. If the input of the new cut exceeds K, the cut is discarded. 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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Bottom-Up Cut Enumeration The set of cuts of a node is a cross product of the sets of cuts of its children { {t}, {u, v}, {u, r, s}, .. {a, b, c} } t { {u}, {p, q}, {p, a, b}, {a, c, q}, {a, b, c} } u v { {v}, {r, s}, {r, a, c}, {b, c, s}, {a, b, c} } s Any cut that is of size greater than k is discarded. { {q}, {a, b} } { {p}, {a, c} } p Computation is done bottom-up (Pan ’98, Cong ’99) q a { {a} } r b { {b} } c { {c} } o need to enumerate cuts larger than k to obtain k-feasible cuts. Electrical and Computer EngineeringAlan Mishchenko et al. “Improvements to Technology Mapping for LUT-Based FPGAs” Credits: Alan Mishchenko, et al., Prof. Maciej Ciesielski , P Pany 9
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• PROVISIONAL RELEASE DATA: Provisional release data are the data that were reported to NCES in the original data collection and underwent all NCES data quality control procedures, including imputation. Institutions may have updated these data in the subsequent data collection year, however updates are not reflected in these data. • FINAL RELEASE DATA: Final release data include revisions to the provisional release data that have been made by institutions during the subsequent data collection year. The final release data can be used when the most up to date data are required; however, these data may not match tables from the First Look reports based on preliminary and provisional data. • PRELIMINARY RELEASE DATA: Preliminary release data have been edited but are subject to further NCES quality control procedures. Imputed data for nonresponding institutions are not included. These data are
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