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==> First Aid for listings.sty no longer applied!
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    so I'm assuming it got fixed.
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\OT1/cmr/m/n/10.95 quences, and sim-ple trans-for-ma-tions. When the prob-lem d
o-main shifts|different

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[]\OT1/cmr/m/n/10.95 Consider the bond strength cal-cu-la-tion im-ple-mented in
 \OT1/cmtt/m/n/10.95 bond.py:103-121\OT1/cmr/m/n/10.95 .
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[]\OT1/cmr/m/n/10.95 Neuroscience and cog-ni-tive psy-chol-ogy in-creas-ingly e
m-pha-size the brain's
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\OT1/cmr/m/n/10.95 man-tic func-tion in the net-work. These edge types, cre-ate
d in \OT1/cmtt/m/n/10.95 slipnet.py:200-236\OT1/cmr/m/n/10.95 ,
[6]
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 []\OT1/cmr/bx/n/10.95 Category Links[] \OT1/cmr/m/n/10.95 form tax-o-nomic hi-
er-ar-chies, con-nect-ing spe-cific in-stances
[7]
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[]\OT1/cmr/m/n/10.95 This for-mu-la-tion au-to-mat-i-cally as-signs ap-pro-pri-
ate depths. Let-ters them-
[8]
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\OT1/cmr/m/n/10.95 con-cepts au-to-mat-i-cally as-signs them ap-pro-pri-ate dep
ths based on their graph

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[][][][]\OT1/cmr/m/n/10.95 (\OML/cmm/m/it/10.95 i \OMS/cmsy/m/n/10.95 ! \OML/cm
m/m/it/10.95 j\OT1/cmr/m/n/10.95 ) = []
[9]
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\OT1/cmr/m/n/10.95 col-ors rep-re-sent-ing con-cep-tual depth and edge thick-ne
ss in-di-cat-ing link strength
[10] [11 <./figure1_slipnet_graph.pdf>] [12 <./figure2_activation_spreading.pdf
> <./figure3_resistance_distance.pdf>]
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\OT1/cmr/m/n/10.95 We for-mal-ize the Workspace as a time-varying graph $\OMS/c
msy/m/n/10.95 W\OT1/cmr/m/n/10.95 (\OML/cmm/m/it/10.95 t\OT1/cmr/m/n/10.95 ) = 
(\OML/cmm/m/it/10.95 V[]\OT1/cmr/m/n/10.95 (\OML/cmm/m/it/10.95 t\OT1/cmr/m/n/1
0.95 )\OML/cmm/m/it/10.95 ; E[]\OT1/cmr/m/n/10.95 (\OML/cmm/m/it/10.95 t\OT1/cm
r/m/n/10.95 )\OML/cmm/m/it/10.95 ; ^^[\OT1/cmr/m/n/10.95 )$

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\OT1/cmr/m/n/10.95 nodes or edges to the graph. Struc-tures break (\OT1/cmtt/m/
n/10.95 bond.py:56-70\OT1/cmr/m/n/10.95 , \OT1/cmtt/m/n/10.95 group.py:143-165\
OT1/cmr/m/n/10.95 ,

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\OT1/cmr/m/n/10.95 Current Copy-cat im-ple-men-ta-tion com-putes ob-ject salien
ce us-ing fixed weight-

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\OT1/cmr/m/n/10.95 ing schemes that do not adapt to graph struc-ture. The code 
in \OT1/cmtt/m/n/10.95 workspaceObject.py:88-95

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[]
[13]
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[]\OT1/cmr/m/n/10.95 In Copy-cat's Workspace, be-tween-ness cen-tral-ity nat-u-
rally iden-ti-fies struc-
[14] [15]
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[]|\OT1/cmr/bx/n/10 Original Con-

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[]|\OT1/cmr/bx/n/10 Graph Met-ric Re-place-

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[]|\OT1/cmr/m/n/10 memberCompatibility

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[]|\OT1/cmr/m/n/10 Structural equiv-a-lence:

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[]|\OT1/cmr/m/n/10 Clustering co-ef-fi-cient:

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[]|\OT1/cmr/m/n/10 jump[]threshold

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[]|\OT1/cmr/m/n/10 salience[]weights

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[]|\OT1/cmr/m/n/10 Betweenness cen-tral-ity:

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[]|\OT1/cmr/m/n/10 length[]factors (5,

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[]|\OT1/cmr/m/n/10 mapping[]factors

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[16] [17]
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\OT1/cmr/m/n/10.95 tently higher be-tween-ness than ob-jects that re-main un-ma
pped (dashed lines),
[18] [19 <./figure4_workspace_evolution.pdf> <./figure5_betweenness_dynamics.pd
f>] [20 <./figure6_clustering_distribution.pdf>]
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\OT1/cmr/m/n/10.95 the brit-tle-ness of fixed pa-ram-e-ters. When the prob-lem 
do-main changes|longer
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\OT1/cmr/m/n/10.95 Specif-i-cally, we pre-dict that tem-per-a-ture in-versely c
or-re-lates with Workspace

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 []\OT1/cmr/bx/n/10.95 Hypothesis 3: Clus-ter-ing Pre-dicts Suc-cess[] \OT1/cmr
/m/n/10.95 Suc-cess-ful problem-solving
[23] [24] [25] [26]
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 []\OT1/cmr/bx/n/10.95 Neuroscience Com-par-i-son[] \OT1/cmr/m/n/10.95 Com-par-
ing Copy-cat's graph met-rics to brain

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 []\OT1/cmr/bx/n/10.95 Meta-Learning Met-ric Se-lec-tion[] \OT1/cmr/m/n/10.95 D
e-vel-op-ing meta-learning sys-tems that
[27]
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[]\OT1/cmr/m/n/10.95 The graph-theoretical re-for-mu-la-tion hon-ors Copy-cat's
 orig-i-nal vi-sion|modeling
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Output written on paper.pdf (29 pages, 642536 bytes).
Transcript written on paper.log.
pdflatex: major issue: So far, you have not checked for MiKTeX updates.