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train · 14.7k rows

premise string	hypothesis string	label int64
\| Entity \| Categorization \| Conditions \| \| --- \| --- \| --- \| \| EndofSπ(j−1),i \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij−1<br>(q∈III)∧(p∈II)j−1i−2<br>(q∈IV)∧(p∈II)j−1i+2 \| \| \| right(3) \| q∈IIj−1<br>(q∈III)∧(p∈I)j−1i−2<br>(q∈IV)∧(p∈I)j−1i+2 \| \|	\| BeginningofSi,π(j+1) \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| --- \| --- \| --- \| \| left(2) \| q∈Ij+1<br>(q∈III)∧(p∈II)j+1i−2<br>(q∈IV)∧(p∈II)j+1i+2 \| \| \| right(3) \| q∈IIj+1<br>(q∈III)∧(p∈I)j+1i−2<br>(q∈IV)∧(p∈I)j+1i+2 \| \|	1
\| Entity \| Categorization \| Conditions \| \| --- \| --- \| --- \| \| EndofSπ(j−1),i \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij−1<br>(q∈III)∧(p∈II)j−1i−2<br>(q∈IV)∧(p∈II)j−1i+2 \| \| \| right(3) \| q∈IIj−1<br>(q∈III)∧(p∈I)j−1i−2<br>(q∈IV)∧(p∈I)j−1i+2 \| \|	\| \| k=3 \| k=4 \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| h \| q \| r \| t \| q \| r \| t \| q \| \| 1 \| 3x−1 \| 3x+3x+1 \| −3x−1 \| x+x+1 \| x+2x+2 \| −x \| x+1 \| \| 2 \| 2x+x+1<br>2<br>14x+3x−1<br>2<br>14x+17x+4 \| x+x+1<br>2<br>7x+5x+1<br>2<br>7x+5x+1 \| −x<br>−7x−2<br>7x+3 \| 4x+2x+1 \| 2x+2x+1 \| −2x \| 2x+x+2<...	0
\| Entity \| Categorization \| Conditions \| \| --- \| --- \| --- \| \| EndofSπ(j−1),i \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij−1<br>(q∈III)∧(p∈II)j−1i−2<br>(q∈IV)∧(p∈II)j−1i+2 \| \|	\| right(3) \| q∈IIj−1<br>(q∈III)∧(p∈I)j−1i−2<br>(q∈IV)∧(p∈I)j−1i+2 \| \| \| --- \| --- \| --- \| \| BeginningofSi,π(j+1) \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij+1<br>(q∈III)∧(p∈II)j+1i−2<br>(q∈IV)∧(p∈II)j+1i+2 \| \| \| right(3) \| q∈IIj+1<br>(q∈III)∧(p∈I)j+1i−2<br>(q∈IV)∧(p∈I)j+1i+2 \| \|	1
\| Entity \| Categorization \| Conditions \| \| --- \| --- \| --- \| \| EndofSπ(j−1),i \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij−1<br>(q∈III)∧(p∈II)j−1i−2<br>(q∈IV)∧(p∈II)j−1i+2 \| \|	\| 2 \| 2x+x+1<br>2<br>14x+3x−1<br>2<br>14x+17x+4 \| x+x+1<br>2<br>7x+5x+1<br>2<br>7x+5x+1 \| −x<br>−7x−2<br>7x+3 \| 4x+2x+1 \| 2x+2x+1 \| −2x \| 2x+x+2<br>2<br>6x+3x+1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 3 \| 3x+2x+2 \| x+x+1 \| −x \| 5x+9x+9<br>2<br>25x+15x+3<br>2<br>25x+25x+7 \| x+2x+2<br>2<br>5x+4x+1<br>2<br>5...	0
\| n \| n-k \| m \| q \| d \| t \| r’ \| r \| GVR \| Singleton \| pk(bits) \| sign(bits) \| LP \| Dual \| DS \| DA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5 \| 8 \| 57600 \| 8640 \| 130 \| 1096 \| 400 \| 776 \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5...	\| 48 \| 12 \| 40 \| 2 \| 4 \| 5 \| 3 \| 8 \| 6 \| 10 \| 78720 \| 2976 \| ¿600 \| 340 \| 164 \| 114 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 50 \| 10 \| 42 \| 2 \| 5 \| 5(2) \| 2 \| 7 \| 5 \| 9 \| 70560 \| 2800 \| ¿600 \| 240 \| 180 \| 104 \|	1
\| n \| n-k \| m \| q \| d \| t \| r’ \| r \| GVR \| Singleton \| pk(bits) \| sign(bits) \| LP \| Dual \| DS \| DA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5 \| 8 \| 57600 \| 8640 \| 130 \| 1096 \| 400 \| 776 \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5...	\| 8-bit \| 12-bit \| 16-bit \| 24-bit \| 36-bit \| \| --- \| --- \| --- \| --- \| --- \| \| 14.19 \| 13.26 \| 13.13 \| 13.84 \| 14.90 \| \| 16.57 \| 17.12 \| 16.94 \| 17.08 \| 17.38 \| \| 21.97 \| 22.76 \| 23.91 \| 25.89 \| 27.53 \| \| 30.93 \| 32.50 \| 33.59 \| 35.36 \| 35.59 \| \| 15.06 \| 15.43 \| 15.64 \| 16.07 \| 16.78 \| \| 63.48 \| 66.97 \| 68.83 \| 73.45 ...	0
\| n \| n-k \| m \| q \| d \| t \| r’ \| r \| GVR \| Singleton \| pk(bits) \| sign(bits) \| LP \| Dual \| DS \| DA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5 \| 8 \| 57600 \| 8640 \| 130 \| 1096 \| 400 \| 776 \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5...	\| 20 \| 10 \| 24 \| 2 \| 2 \| 3 \| 5 \| 8 \| 6 \| 10 \| 24960 \| 3008 \| 190 \| 370 \| 104 \| 226 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 27 \| 9 \| 20 \| 2 \| 3 \| 2 \| 3 \| 5 \| 4 \| 7 \| 23328 \| 1470 \| 170 \| 187 \| 120 \| 129 \| \| 48 \| 12 \| 40 \| 2 \| 4 \| 5 \| 3 \| 8 \| 6 \| 10 \| 78720 \| ...	1
\| n \| n-k \| m \| q \| d \| t \| r’ \| r \| GVR \| Singleton \| pk(bits) \| sign(bits) \| LP \| Dual \| DS \| DA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5 \| 8 \| 57600 \| 8640 \| 130 \| 1096 \| 400 \| 776 \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5...	\| 16.57 \| 17.12 \| 16.94 \| 17.08 \| 17.38 \| \| --- \| --- \| --- \| --- \| --- \| \| 21.97 \| 22.76 \| 23.91 \| 25.89 \| 27.53 \| \| 30.93 \| 32.50 \| 33.59 \| 35.36 \| 35.59 \| \| 15.06 \| 15.43 \| 15.64 \| 16.07 \| 16.78 \| \| 63.48 \| 66.97 \| 68.83 \| 73.45 \| 74.66 \| \| 56.67 \| 71.41 \| 76.50 \| 80.40 \| 82.73 \| \| 59.09 \| 61.17 \| 68.15 \| 69.22 \| 70...	0
\| Method \| N \| E \| C \| \| --- \| --- \| --- \| --- \| \| SPINN-NPencoders \| 80.6 \| 88.2 \| 85.5 \| \| mLSTM \| 81.6 \| 91.6 \| 87.4 \|	\| word-wordattentionandaggregation \| 83.7 \| 92.1 \| 86.7 \| \| --- \| --- \| --- \| --- \| \| Ourmodelwithbi-LSTMencoders \| 84.3 \| 90.6 \| 86.9 \| \| Ourmodelwithbtree-LSTMencoders \| 84.8 \| 93.2 \| 87.4 \|	1
\| Method \| N \| E \| C \| \| --- \| --- \| --- \| --- \| \| SPINN-NPencoders \| 80.6 \| 88.2 \| 85.5 \| \| mLSTM \| 81.6 \| 91.6 \| 87.4 \|	\| Reference \| Lexicon \| LM \| CH \| SW \| \| --- \| --- \| --- \| --- \| --- \| \| Current \| N \| N \| 26.4 \| 17.2 \| \| Current \| N \| CharNG \| 21.8 \| 13.8 \| \| (ensemble) \| Y \| WordNG \| 19.3 \| 12.6 \| \| Current \| Y \| WordNG \| 18.7 \| 11.3 \| \| Current \| Y \| WordRNN \| 17.7 \| 10.2 \|	0
\| Method \| N \| E \| C \| \| --- \| --- \| --- \| --- \| \| SPINN-NPencoders \| 80.6 \| 88.2 \| 85.5 \| \| mLSTM \| 81.6 \| 91.6 \| 87.4 \|	\| word-wordattentionandaggregation \| 83.7 \| 92.1 \| 86.7 \| \| --- \| --- \| --- \| --- \| \| Ourmodelwithbi-LSTMencoders \| 84.3 \| 90.6 \| 86.9 \| \| Ourmodelwithbtree-LSTMencoders \| 84.8 \| 93.2 \| 87.4 \|	1
\| Method \| N \| E \| C \| \| --- \| --- \| --- \| --- \| \| SPINN-NPencoders \| 80.6 \| 88.2 \| 85.5 \| \| mLSTM \| 81.6 \| 91.6 \| 87.4 \|	\| Current \| Y \| WordNG \| 18.7 \| 11.3 \| \| --- \| --- \| --- \| --- \| --- \| \| Current \| Y \| WordRNN \| 17.7 \| 10.2 \|	0
\| Numbers \| Category0 \| Categoryvoid \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Passed \| Failed \| Passed \| Failed \|	\| Twitter \| 12,681 \| 2,228 \| 21,443 \| 4,123 \| \| --- \| --- \| --- \| --- \| --- \| \| Facebook \| 7,586 \| 1,287 \| 21,715 \| 3,579 \| \| Total \| 20,267 \| 3,515 \| 43,158 \| 7,702 \|	1
\| Numbers \| Category0 \| Categoryvoid \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Passed \| Failed \| Passed \| Failed \|	\| Category \| Numberoftweets \| \| --- \| --- \| \| Totaltweets \| 3545 \| \| Tweetsinfavor \| 964 \| \| Tweetsagainst \| 647 \| \| Neutraltweets \| 1934 \|	0
\| Numbers \| Category0 \| Categoryvoid \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Passed \| Failed \| Passed \| Failed \|	\| Twitter \| 12,681 \| 2,228 \| 21,443 \| 4,123 \| \| --- \| --- \| --- \| --- \| --- \| \| Facebook \| 7,586 \| 1,287 \| 21,715 \| 3,579 \| \| Total \| 20,267 \| 3,515 \| 43,158 \| 7,702 \|	1
\| Numbers \| Category0 \| Categoryvoid \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Passed \| Failed \| Passed \| Failed \|	\| Tweetsinfavor \| 964 \| \| --- \| --- \| \| Tweetsagainst \| 647 \| \| Neutraltweets \| 1934 \|	0
\| Name \| NumberofEdges \| NumberofTriangles \| \| --- \| --- \| --- \| \| cit-HepTh-dates \| 38488 \| 1418 \| \| wiki-Vote \| 201524 \| 608389 \| \| email-Enron \| 367662 \| 727044 \|	\| soc-sign-epinions \| 1422420 \| 4910076 \| \| --- \| --- \| --- \| \| ﬂickrEdges \| 4633896 \| 107987357 \| \| web-Google \| 8644102 \| 13391903 \| \| cit-Patents \| 33037894 \| 7515023 \|	1
\| Name \| NumberofEdges \| NumberofTriangles \| \| --- \| --- \| --- \| \| cit-HepTh-dates \| 38488 \| 1418 \| \| wiki-Vote \| 201524 \| 608389 \| \| email-Enron \| 367662 \| 727044 \|	\| Entity \| Ni \| Vi \| Mi \| StructureType \| \| --- \| --- \| --- \| --- \| --- \| \| latlon \| 1624984 \| 1625197 \| 1506465 \| Identity \| \| lat \| 1624984 \| 1625192 \| 1504469 \| Identity \| \| lon \| 1625061 \| 1625725 \| 1504619 \| Identity \| \| place \| 1741337 \| 1741516 \| 1504619 \| Identity \| \| retweetID \| 636455 \| 636644 \| 627163 \| Iden...	0
\| Name \| NumberofEdges \| NumberofTriangles \| \| --- \| --- \| --- \| \| cit-HepTh-dates \| 38488 \| 1418 \| \| wiki-Vote \| 201524 \| 608389 \| \| email-Enron \| 367662 \| 727044 \| \| soc-sign-epinions \| 1422420 \| 4910076 \| \| ﬂickrEdges \| 4633896 \| 107987357 \|	\| web-Google \| 8644102 \| 13391903 \| \| --- \| --- \| --- \| \| cit-Patents \| 33037894 \| 7515023 \|	1
\| Name \| NumberofEdges \| NumberofTriangles \| \| --- \| --- \| --- \| \| cit-HepTh-dates \| 38488 \| 1418 \| \| wiki-Vote \| 201524 \| 608389 \| \| email-Enron \| 367662 \| 727044 \| \| soc-sign-epinions \| 1422420 \| 4910076 \| \| ﬂickrEdges \| 4633896 \| 107987357 \|	\| lat \| 1624984 \| 1625192 \| 1504469 \| Identity \| \| --- \| --- \| --- \| --- \| --- \| \| lon \| 1625061 \| 1625725 \| 1504619 \| Identity \| \| place \| 1741337 \| 1741516 \| 1504619 \| Identity \| \| retweetID \| 636455 \| 636644 \| 627163 \| Identity \| \| reuserID \| 720624 \| 722148 \| 676616 \| Identity \| \| time \| 2020000 \| 2020000 \| 35176 \|...	0
\| \| Omniscient:<br>fulldistances \| Agents’ordinalprefs<br>andfacilitylocations \| Onlyagents’ordinal<br>prefs(lowerbounds) \| \| --- \| --- \| --- \| --- \| \| Total(Sum)SocialChoice \| 1 \| 3 \| 5(3) \| \| MedianSocialChoice \| 1 \| 3 \| 5(5) \| \| MinWeightBipartiteMatching \| 1 \| 3 \| n(3) \| \| EgalitarianBipartiteMatching \| 1 \| 3 \| -(...	\| k-center \| 2 \| 5 \| -(-) \| \| --- \| --- \| --- \| --- \| \| k-median \| 2.675 \| 6.35 \| -(Ω(n)) \|	1
\| \| Omniscient:<br>fulldistances \| Agents’ordinalprefs<br>andfacilitylocations \| Onlyagents’ordinal<br>prefs(lowerbounds) \| \| --- \| --- \| --- \| --- \| \| Total(Sum)SocialChoice \| 1 \| 3 \| 5(3) \| \| MedianSocialChoice \| 1 \| 3 \| 5(5) \| \| MinWeightBipartiteMatching \| 1 \| 3 \| n(3) \| \| EgalitarianBipartiteMatching \| 1 \| 3 \| -(...	\| pair \| ∅ \| ω1 \| ω2 \| ω3 \| ω4 \| Ω \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (1,2) \| 0 \| 0.13975 \| 0.23775 \| 0.232 \| 0.025 \| 0.3655 \| \| (2,3) \| 0 \| 0.1765 \| 0.05 \| 0.10825 \| 0.17 \| 0.49525 \| \| (2,4) \| 0 \| 0.1 \| 0.152 \| 0.138 \| 0.14875 \| 0.46125 \| \| (3,4) \| 0 \| 0.1765 \| 0.05 \| 0.10825 \| 0.17 \| 0.49525 \| \| (4,5) \| ...	0
\| \| Omniscient:<br>fulldistances \| Agents’ordinalprefs<br>andfacilitylocations \| Onlyagents’ordinal<br>prefs(lowerbounds) \| \| --- \| --- \| --- \| --- \| \| Total(Sum)SocialChoice \| 1 \| 3 \| 5(3) \| \| MedianSocialChoice \| 1 \| 3 \| 5(5) \| \| MinWeightBipartiteMatching \| 1 \| 3 \| n(3) \|	\| EgalitarianBipartiteMatching \| 1 \| 3 \| -(2) \| \| --- \| --- \| --- \| --- \| \| FacilityLocation \| 1.488 \| 3.976 \| ∞(∞) \| \| k-center \| 2 \| 5 \| -(-) \| \| k-median \| 2.675 \| 6.35 \| -(Ω(n)) \|	1
\| \| Omniscient:<br>fulldistances \| Agents’ordinalprefs<br>andfacilitylocations \| Onlyagents’ordinal<br>prefs(lowerbounds) \| \| --- \| --- \| --- \| --- \| \| Total(Sum)SocialChoice \| 1 \| 3 \| 5(3) \| \| MedianSocialChoice \| 1 \| 3 \| 5(5) \| \| MinWeightBipartiteMatching \| 1 \| 3 \| n(3) \|	\| (2,4) \| 0 \| 0.1 \| 0.152 \| 0.138 \| 0.14875 \| 0.46125 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (3,4) \| 0 \| 0.1765 \| 0.05 \| 0.10825 \| 0.17 \| 0.49525 \| \| (4,5) \| 0 \| 0.241 \| 0.234 \| 0.152 \| 0.06775 \| 0.30525 \|	0
\| \| 3newslices \| 2newslices \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| #runs \| 1 \| 2 \| 3 \| 4 \| 5 \| \| initial \| 8.73 \| 9.01 \| 8.89 \| 6.54 \| 6.98 \|	\| master<br>mintrack<br>maxtrack \| 6.06<br>5.96<br>6.06 \| 6.22<br>6.16<br>6.24 \| 6.18<br>6.07<br>6.23 \| 6.67<br>6.60<br>6.11 \| 7.10<br>7.02<br>7.15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| total \| 14.9 \| 15.4 \| 15.3 \| 13.4 \| 14.2 \|	1
\| \| 3newslices \| 2newslices \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| #runs \| 1 \| 2 \| 3 \| 4 \| 5 \| \| initial \| 8.73 \| 9.01 \| 8.89 \| 6.54 \| 6.98 \|	\| 8<br>4<br>2 \| 895542<br>1791084<br>3582168 \| 934040<br>1808784<br>3585869 \| \| --- \| --- \| --- \| \| 8<br>4<br>2 \| 3203546<br>6407091<br>7<br>1.2814·10 \| 3219769<br>6427398<br>7<br>1.2827·10 \| \| 8<br>4<br>2 \| 8.928·10<br>9<br>1.7856·10<br>9<br>3.5712·10 \| 8.9394·10<br>9<br>1.7871·10<br>9<br>3.573·10 \| \| 8<br>4<br>2 \| 1....	0
\| \| 3newslices \| 2newslices \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| #runs \| 1 \| 2 \| 3 \| 4 \| 5 \|	\| initial \| 8.73 \| 9.01 \| 8.89 \| 6.54 \| 6.98 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| master<br>mintrack<br>maxtrack \| 6.06<br>5.96<br>6.06 \| 6.22<br>6.16<br>6.24 \| 6.18<br>6.07<br>6.23 \| 6.67<br>6.60<br>6.11 \| 7.10<br>7.02<br>7.15 \| \| total \| 14.9 \| 15.4 \| 15.3 \| 13.4 \| 14.2 \|	1
\| \| 3newslices \| 2newslices \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| #runs \| 1 \| 2 \| 3 \| 4 \| 5 \|	\| 8<br>4<br>2 \| 1.2497·10<br>9<br>2.4995·10<br>9<br>4.9989·10 \| 1.251·10<br>9<br>2.5005·10<br>9<br>4.9995·10 \| \| --- \| --- \| --- \| \| 8 \| 6.4582·10 \| 6.4651·10 \|	0
\| Class \| Guptaet.al \| RGB \| Depth \| RGB-D \| \| --- \| --- \| --- \| --- \| --- \| \| bedroom \| 79 \| 79.06 \| 78.01 \| 82.72 \| \| kitchen \| 74 \| 65.09 \| 60.38 \| 75.47 \| \| livingroom \| 47 \| 73.83 \| 33.64 \| 75.70 \| \| bathroom \| 67 \| 89.66 \| 81.03 \| 96.55 \| \| diningroom \| 47 \| 96.36 \| 50.91 \| 96.36 \| \| oﬃce \| 24 \| 63.16 \| 13.16 \| 7...	\| others \| 15 \| 85.37 \| 39.02 \| 95.12 \| \| --- \| --- \| --- \| --- \| --- \| \| meandiag.cm. \| 47 \| 79.29 \| 48.11 \| 83.81 \| \| avg.accuracy \| 58 \| 77.52 \| 55.81 \| 82.42 \|	1
\| Class \| Guptaet.al \| RGB \| Depth \| RGB-D \| \| --- \| --- \| --- \| --- \| --- \| \| bedroom \| 79 \| 79.06 \| 78.01 \| 82.72 \| \| kitchen \| 74 \| 65.09 \| 60.38 \| 75.47 \| \| livingroom \| 47 \| 73.83 \| 33.64 \| 75.70 \| \| bathroom \| 67 \| 89.66 \| 81.03 \| 96.55 \| \| diningroom \| 47 \| 96.36 \| 50.91 \| 96.36 \| \| oﬃce \| 24 \| 63.16 \| 13.16 \| 7...	\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \| \| chair \| 0.0118 \| 0.0238 \| 0.0444 \| \| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \| \| nightstand \| 0.0080 \| 0.0248 \| 0.2925 \| \|...	0
\| Class \| Guptaet.al \| RGB \| Depth \| RGB-D \| \| --- \| --- \| --- \| --- \| --- \| \| bedroom \| 79 \| 79.06 \| 78.01 \| 82.72 \| \| kitchen \| 74 \| 65.09 \| 60.38 \| 75.47 \| \| livingroom \| 47 \| 73.83 \| 33.64 \| 75.70 \| \| bathroom \| 67 \| 89.66 \| 81.03 \| 96.55 \|	\| diningroom \| 47 \| 96.36 \| 50.91 \| 96.36 \| \| --- \| --- \| --- \| --- \| --- \| \| oﬃce \| 24 \| 63.16 \| 13.16 \| 71.05 \| \| homeoﬃce \| 8.3 \| 70.83 \| 0.00 \| 62.50 \| \| classroom \| 48 \| 69.57 \| 52.17 \| 82.61 \| \| bookstore \| 64 \| 100.00 \| 72.73 \| 100.00 \| \| others \| 15 \| 85.37 \| 39.02 \| 95.12 \| \| meandiag.cm. \| 47 \| 79.29 \| 48.11 ...	1
\| Class \| Guptaet.al \| RGB \| Depth \| RGB-D \| \| --- \| --- \| --- \| --- \| --- \| \| bedroom \| 79 \| 79.06 \| 78.01 \| 82.72 \| \| kitchen \| 74 \| 65.09 \| 60.38 \| 75.47 \| \| livingroom \| 47 \| 73.83 \| 33.64 \| 75.70 \| \| bathroom \| 67 \| 89.66 \| 81.03 \| 96.55 \|	\| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| --- \| --- \| --- \| --- \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \| \| nightstand \| 0.0080 \| 0.0248 \| 0.2925 \| \| sofa \| 0.0068 \| 0.0186 \| 0.0563 \| \| table \| 0.0051 \| 0.0326 \| 0.0340 \| \| toilet \| 0.0119 \| 0.0180 \| 0.0977 \| \| Avg. \| 0.0092 \| 0.0259 \| ...	0
\| GraphMotif \| Static<br>SubgraphsTime \| δ=1hour<br>SubgraphsTimeSpeedUp \| \| --- \| --- \| --- \| \| CollegeMSGM1<br>CollegeMSGM2<br>CollegeMSGM3<br>CollegeMSGM4<br>CollegeMSGM5<br>CollegeMSGM6 \| 87618.8<br>238K43.2<br>1.57K19.3<br>482.18<br>202M9.88K<br>10.9M2.02K \| 3.46K0.54634.5x<br>75.4K2.0421.1x<br>1.06K0.49938.6x<br>...	\| MathOverﬂowM1<br>MathOverﬂowM2<br>MathOverﬂowM3<br>MathOverﬂowM4<br>MathOverﬂowM5<br>MathOverﬂowM6 \| 1.42M2.94K<br>218M11.4K<br>5.53M4.02K<br>285K2.34K<br>—>24hr<br>—>24hr \| 20.0012.94Mx<br>2480.015760Kx<br>40.0014.02Mx<br>00.015156Kx<br>8050.031>2.79Mx<br>1.70K0.062>1.39Mx \| \| --- \| --- \| --- \| \| EnronM1<br>EnronM2<...	1
\| GraphMotif \| Static<br>SubgraphsTime \| δ=1hour<br>SubgraphsTimeSpeedUp \| \| --- \| --- \| --- \| \| CollegeMSGM1<br>CollegeMSGM2<br>CollegeMSGM3<br>CollegeMSGM4<br>CollegeMSGM5<br>CollegeMSGM6 \| 87618.8<br>238K43.2<br>1.57K19.3<br>482.18<br>202M9.88K<br>10.9M2.02K \| 3.46K0.54634.5x<br>75.4K2.0421.1x<br>1.06K0.49938.6x<br>...	\| Graph \| Nodes<br>(Million) \| Edges<br>(Million) \| Outdegrees<br>MaxAvgσ \| \| --- \| --- \| --- \| --- \| \| rmat20 \| 1.05 \| 8.26 \| 1,1818177.40 \| \| road-FLA<br>road-W<br>road-USA \| 1.07<br>6.26<br>23.95 \| 2.71<br>15.12<br>57.71 \| 832.45<br>942.74<br>932.74 \| \| ER20<br>ER23 \| 1.05<br>8.39 \| 4.19<br>33.55 \| 1544.47<br>1034.4...	0
\| GraphMotif \| Static<br>SubgraphsTime \| δ=1hour<br>SubgraphsTimeSpeedUp \| \| --- \| --- \| --- \| \| CollegeMSGM1<br>CollegeMSGM2<br>CollegeMSGM3<br>CollegeMSGM4<br>CollegeMSGM5<br>CollegeMSGM6 \| 87618.8<br>238K43.2<br>1.57K19.3<br>482.18<br>202M9.88K<br>10.9M2.02K \| 3.46K0.54634.5x<br>75.4K2.0421.1x<br>1.06K0.49938.6x<br>...	\| Email-EuM1<br>Email-EuM2<br>Email-EuM3<br>Email-EuM4<br>Email-EuM5<br>Email-EuM6 \| 3.96K33.5<br>367K91.4<br>12.0K40.1<br>5544.99<br>57.7M7.18K<br>7.08M1.54K \| 2581.1429.4x<br>30.1K1.8050.9x<br>4931.1534.7x<br>231.094.57x<br>4.71M13154.9x<br>31.1K2.45627x \| \| --- \| --- \| --- \| \| GraphMotif \| Static<br>SubgraphsTime \| ...	1
\| GraphMotif \| Static<br>SubgraphsTime \| δ=1hour<br>SubgraphsTimeSpeedUp \| \| --- \| --- \| --- \| \| CollegeMSGM1<br>CollegeMSGM2<br>CollegeMSGM3<br>CollegeMSGM4<br>CollegeMSGM5<br>CollegeMSGM6 \| 87618.8<br>238K43.2<br>1.57K19.3<br>482.18<br>202M9.88K<br>10.9M2.02K \| 3.46K0.54634.5x<br>75.4K2.0421.1x<br>1.06K0.49938.6x<br>...	\| road-FLA<br>road-W<br>road-USA \| 1.07<br>6.26<br>23.95 \| 2.71<br>15.12<br>57.71 \| 832.45<br>942.74<br>932.74 \| \| --- \| --- \| --- \| --- \| \| ER20<br>ER23 \| 1.05<br>8.39 \| 4.19<br>33.55 \| 1544.47<br>1034.46 \| \| Graph500<br>(threegraphs) \| 16.78 \| 335.00 \| 924,0002020,900 \|	0
\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	\| -classe1880(attendus=169,ramenes=118.00,corrects=13.00)<br>rappel=0.077precision=0.110f-mesure=0.091 \| \| --- \| \| -classe1890(attendus=197,ramenes=78.00,corrects=9.00)<br>rappel=0.046precision=0.115f-mesure=0.065 \| \| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 \|...	1
\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	0
\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	\| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 \| \| --- \| \| -classe1910(attendus=201,ramenes=67.00,corrects=6.00)<br>rappel=0.030precision=0.090f-mesure=0.045 \| \| -classe1920(attendus=200,ramenes=143.00,corrects=18.00)<br>rappel=0.090precision=0.126f-mesure=0.105 \|...	1
\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	\| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 \| \| --- \| \| -classe1910(attendus=201,ramenes=67.00,corrects=6.00)<br>rappel=0.030precision=0.090f-mesure=0.045 \| \| -classe1920(attendus=200,ramenes=143.00,corrects=18.00)<br>rappel=0.090precision=0.126f-mesure=0.105 \|...	0
\| Algorithms \| Memory(MB) \| \| --- \| --- \| \| VGG \| 2459.8 \| \| SSDGoogleNet \| 320.4 \|	\| SqueezeNet \| 145.3 \| \| --- \| --- \| \| MobileNet \| 172.2 \| \| L-CNN \| 139.5 \|	1
\| Algorithms \| Memory(MB) \| \| --- \| --- \| \| VGG \| 2459.8 \| \| SSDGoogleNet \| 320.4 \|	\| CNN \| Imageresolution \| RunTime(ms) \| \| --- \| --- \| --- \| \| AlexNet \| 227×227 \| 2.3 \| \| VGG16 \| 224×224 \| 10 \| \| Resnet50 \| 224×224 \| 17 \| \| SqueezeNet \| 224×224 \| 2.5 \| \| SqueezeNet \| 448×448 \| 4 \| \| SqueezeNet \| 625×625 \| 6 \|	0
\| Algorithms \| Memory(MB) \| \| --- \| --- \| \| VGG \| 2459.8 \|	\| SSDGoogleNet \| 320.4 \| \| --- \| --- \| \| SqueezeNet \| 145.3 \| \| MobileNet \| 172.2 \| \| L-CNN \| 139.5 \|	1
\| Algorithms \| Memory(MB) \| \| --- \| --- \| \| VGG \| 2459.8 \|	\| SqueezeNet \| 224×224 \| 2.5 \| \| --- \| --- \| --- \| \| SqueezeNet \| 448×448 \| 4 \| \| SqueezeNet \| 625×625 \| 6 \|	0
\| \| SDR(dB) \| SIR(dB) \| SAR(dB) \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| MDLD \| GaussSep \| MDLD \| GaussSep \| MDLD \| GaussSep \|	\| Dev3Female3 \| 6.02 \| 16.93 \| 23.84 \| 22.43 \| 6.17 \| 18.40 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Example3×5 \| 3.91 \| 9.94 \| 17.92 \| 15.21 \| 4.17 \| 11.68 \| \| Example4×8 \| 2.24 \| -18.63 \| 16.4 \| -17.58 \| 2.52 \| 9.39 \|	1
\| \| SDR(dB) \| SIR(dB) \| SAR(dB) \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| MDLD \| GaussSep \| MDLD \| GaussSep \| MDLD \| GaussSep \|	\| \| SDR(dB) \| SIR(dB) \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| WMDLD \| MDLD \| GS \| WMDLD \| MDLD \| GS \| WMDLD \| MDLD \| \| Dev3Female4 \| 11.77 \| 6.02 \| 16.93 \| 22.26 \| 23.84 \| 22.43 \| 12.23 \| 6.17 \| \| Example3×5 \| 8.41 \| 3.91 \| 9.94 \| 17.58 \| 17.92 \| 15.21 \| 9.10 \| 4.17 \| \| Example...	0
\| \| SDR(dB) \| SIR(dB) \| SAR(dB) \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| MDLD \| GaussSep \| MDLD \| GaussSep \| MDLD \| GaussSep \|	\| Dev3Female3 \| 6.02 \| 16.93 \| 23.84 \| 22.43 \| 6.17 \| 18.40 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Example3×5 \| 3.91 \| 9.94 \| 17.92 \| 15.21 \| 4.17 \| 11.68 \| \| Example4×8 \| 2.24 \| -18.63 \| 16.4 \| -17.58 \| 2.52 \| 9.39 \|	1
\| \| SDR(dB) \| SIR(dB) \| SAR(dB) \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| MDLD \| GaussSep \| MDLD \| GaussSep \| MDLD \| GaussSep \|	\| Example3×5 \| 8.41 \| 3.91 \| 9.94 \| 17.58 \| 17.92 \| 15.21 \| 9.10 \| 4.17 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Example4×8 \| 5.29 \| 2.24 \| -18.63 \| 13.72 \| 16.4 \| -17.58 \| 6.23 \| 2.52 \|	0
\| UL<br>GN \| \| --- \| \| UL<br>GN \| \| UL<br>GN \|	\| UL<br>GN \| \| --- \| \| UL<br>GN \|	1
\| UL<br>GN \| \| --- \| \| UL<br>GN \| \| UL<br>GN \|	\| \| \| --- \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \|	0
\| UL<br>GN \| \| --- \| \| UL<br>GN \| \| UL<br>GN \|	\| UL<br>GN \| \| --- \| \| UL<br>GN \|	1
\| UL<br>GN \| \| --- \| \| UL<br>GN \| \| UL<br>GN \|	\| SL<br>DRL \| \| --- \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \|	0
\| Class(Tissuetype) \| T<br>Labely \| \| --- \| --- \| \| class1(BK) \| (1,0,0,0,0) \|	\| class2(LP) \| (0,1,0,0,0) \| \| --- \| --- \| \| class3(FT) \| (0,0,1,0,0) \| \| class4(MT) \| (0,0,0,1,0) \| \| class5(CA) \| (0,0,0,0,1) \|	1
\| Class(Tissuetype) \| T<br>Labely \| \| --- \| --- \| \| class1(BK) \| (1,0,0,0,0) \|	\| TypeofLesion \| Class \| \| --- \| --- \| \| Healthy/NoLesionandBackground \| 0 \| \| NecroticRegion \| 1 \| \| Edema \| 2 \| \| NonEnhancingTumor \| 3 \| \| EnhancingTumor \| 4 \|	0
\| Class(Tissuetype) \| T<br>Labely \| \| --- \| --- \| \| class1(BK) \| (1,0,0,0,0) \| \| class2(LP) \| (0,1,0,0,0) \| \| class3(FT) \| (0,0,1,0,0) \|	\| class4(MT) \| (0,0,0,1,0) \| \| --- \| --- \| \| class5(CA) \| (0,0,0,0,1) \|	1
\| Class(Tissuetype) \| T<br>Labely \| \| --- \| --- \| \| class1(BK) \| (1,0,0,0,0) \| \| class2(LP) \| (0,1,0,0,0) \| \| class3(FT) \| (0,0,1,0,0) \|	\| NonEnhancingTumor \| 3 \| \| --- \| --- \| \| EnhancingTumor \| 4 \|	0
\| \| SubgamePerfect<br>Equilibrium \| PerfectPrediction<br>Equilibrium \| \| --- \| --- \| --- \| \| FormoftheGame \| Extensive \| Extensive \| \| PerfectInformation \| Yes \| Yes \| \| Numberofplayers \| Any \| Any \| \| NewcombChoice \| Twoboxes \| Onebox \| \| PredictionModel<br>accountingfor<br>counterfactuals \| Ad-HocPrediction<br>(“cou...	\| Existence \| Always \| Always \| \| --- \| --- \| --- \| \| Uniqueness \| Always \| Always \| \| Optimality \| - \| Pareto \| \| Corresponding<br>NormalForm \| Nash<br>(subsuming) \| Superrationality(notestablished/<br>conceptuallysimilar) \|	1
\| \| SubgamePerfect<br>Equilibrium \| PerfectPrediction<br>Equilibrium \| \| --- \| --- \| --- \| \| FormoftheGame \| Extensive \| Extensive \| \| PerfectInformation \| Yes \| Yes \| \| Numberofplayers \| Any \| Any \| \| NewcombChoice \| Twoboxes \| Onebox \| \| PredictionModel<br>accountingfor<br>counterfactuals \| Ad-HocPrediction<br>(“cou...	\| Criterion \| MANCaLog \| IC/LT \| SNOP \| CD \| EGT/VM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1.Labels \| Yes \| No \| Yes \| Yes \| No \| \| 2.ExplicitRepresentationofTime \| Yes \| No \| Yes \| No \| Yes \| \| 3.Non-MarkovianTemporalRelationships \| Yes \| No \| No \| No \| No \| \| 4.Uncertainty \| Yes \| Yes \| Yes \| Yes \| Yes \| \| 5.Compe...	0
\| \| SubgamePerfect<br>Equilibrium \| PerfectPrediction<br>Equilibrium \| \| --- \| --- \| --- \| \| FormoftheGame \| Extensive \| Extensive \| \| PerfectInformation \| Yes \| Yes \| \| Numberofplayers \| Any \| Any \| \| NewcombChoice \| Twoboxes \| Onebox \| \| PredictionModel<br>accountingfor<br>counterfactuals \| Ad-HocPrediction<br>(“cou...	\| Uniqueness \| Always \| Always \| \| --- \| --- \| --- \| \| Optimality \| - \| Pareto \| \| Corresponding<br>NormalForm \| Nash<br>(subsuming) \| Superrationality(notestablished/<br>conceptuallysimilar) \|	1
\| \| SubgamePerfect<br>Equilibrium \| PerfectPrediction<br>Equilibrium \| \| --- \| --- \| --- \| \| FormoftheGame \| Extensive \| Extensive \| \| PerfectInformation \| Yes \| Yes \| \| Numberofplayers \| Any \| Any \| \| NewcombChoice \| Twoboxes \| Onebox \| \| PredictionModel<br>accountingfor<br>counterfactuals \| Ad-HocPrediction<br>(“cou...	\| 2.ExplicitRepresentationofTime \| Yes \| No \| Yes \| No \| Yes \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 3.Non-MarkovianTemporalRelationships \| Yes \| No \| No \| No \| No \| \| 4.Uncertainty \| Yes \| Yes \| Yes \| Yes \| Yes \| \| 5.CompetingCascades \| Yes \| No \| No \| Yes \| Yes \| \| 6.Non-monotonicCascades \| Yes \| No \| No \| Yes \| Ye...	0
\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \|	\| chair \| 0.0118 \| 0.0238 \| 0.0444 \| \| --- \| --- \| --- \| --- \| \| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \| \| nightstand \| 0.0080 \| 0.0248 \| 0.2925 \| \| sofa \| 0.0068 \| 0.0186 \| 0.0563 \| \| table \| 0.0051 \| 0.0326 \| 0.0340 \| \| toilet \| 0.0119 \| 0.0180 \|...	1
\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \|	\| category \| ours \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| bathtub \| 0.8348 \| 0.7017 \| 0.7190 \| 0.1644 \| \| bed \| 0.9202 \| 0.7775 \| 0.3963 \| 0.3239 \| \| chair \| 0.9920 \| 0.9700 \| 0.9892 \| 0.8482 \| \| desk \| 0.8203 \| 0.7936 \| 0.8145 \| 0.1068 \| \| dresser \| 0.7678 \| 0.6314 \| 0.7010 \| 0.2166 \| \| monitor \| 0.9473 \| 0.2493 ...	0
\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \| \| chair \| 0.0118 \| 0.0238 \| 0.0444 \| \| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \|	\| nightstand \| 0.0080 \| 0.0248 \| 0.2925 \| \| --- \| --- \| --- \| --- \| \| sofa \| 0.0068 \| 0.0186 \| 0.0563 \| \| table \| 0.0051 \| 0.0326 \| 0.0340 \| \| toilet \| 0.0119 \| 0.0180 \| 0.0977 \| \| Avg. \| 0.0092 \| 0.0259 \| 0.0956 \|	1
\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \| \| chair \| 0.0118 \| 0.0238 \| 0.0444 \| \| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \|	\| table \| 0.8910 \| 0.8377 \| 0.8751 \| 0.7902 \| \| --- \| --- \| --- \| --- \| --- \| \| toilet \| 0.9701 \| 0.8569 \| 0.6943 \| 0.8832 \| \| Avg. \| 0.8811 \| 0.7431 \| 0.7006 \| 0.4596 \|	0
\| Facet \| Numberofmanually<br>deﬁnedclusters \| Hierarchicalclustering \| Non-hierarchicalclustering \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Purity \| I-Purity \| F1 \| Purity \| I-Purity \| F1 \| \| \| \| Battery \| 4 \| 0.864 \| 0.591 \| 0.702 \| 0.864 \| 0.636 \| 0.733 \| \| Memory \| 3 \| 0.643 \| 1.000 \| 0.78...	\| Shutter \| 6 \| 0.643 \| 0.929 \| 0.760 \| 0.643 \| 0.929 \| 0.760 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Average \| 5.13 \| 0.676 \| 0.819 \| 0.725 \| 0.714 \| 0.828 \| 0.753 \| \| Battery \| 3 \| 0.824 \| 0.765 \| 0.793 \| 0.765 \| 0.706 \| 0.734 \| \| Camera \| 3 \| 0.727 \| 0.636 \| 0.679 \| 0.727 \| 0.636 \| 0.679 \| \| Headset \| ...	1
\| Facet \| Numberofmanually<br>deﬁnedclusters \| Hierarchicalclustering \| Non-hierarchicalclustering \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Purity \| I-Purity \| F1 \| Purity \| I-Purity \| F1 \| \| \| \| Battery \| 4 \| 0.864 \| 0.591 \| 0.702 \| 0.864 \| 0.636 \| 0.733 \| \| Memory \| 3 \| 0.643 \| 1.000 \| 0.78...	\| \| 5VirtualLandscapeDatasets \| 7VirtualLandscapeDatasets \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| N-based \| G-based \| I-based \| N-based \| G-based \| I-based \| N-based \| \| \| Accuracy \| 0.9931 \| 0.9950 \| 0.9927 \| 0.9939 \| 0.9950 \| 0.9930 \| 0.9936 \| \| Precision \| 0.6154 \| 0.7080 \| 0.7143 \| 0....	0
\| Facet \| Numberofmanually<br>deﬁnedclusters \| Hierarchicalclustering \| Non-hierarchicalclustering \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Purity \| I-Purity \| F1 \| Purity \| I-Purity \| F1 \| \| \| \| Battery \| 4 \| 0.864 \| 0.591 \| 0.702 \| 0.864 \| 0.636 \| 0.733 \| \| Memory \| 3 \| 0.643 \| 1.000 \| 0.78...	\| Format \| 1 \| 1.000 \| 0.714 \| 0.833 \| 1.000 \| 0.571 \| 0.727 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Design \| 1 \| 1.000 \| 1.000 \| 1.000 \| 1.000 \| 1.000 \| 1.000 \| \| Service \| 1 \| 1.000 \| 0.739 \| 0.850 \| 1.000 \| 0.522 \| 0.686 \| \| Picture \| 4 \| 0.800 \| 0.850 \| 0.824 \| 0.800 \| 0.850 \| 0.824 \| \| Average \| 2.00...	1
\| Facet \| Numberofmanually<br>deﬁnedclusters \| Hierarchicalclustering \| Non-hierarchicalclustering \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Purity \| I-Purity \| F1 \| Purity \| I-Purity \| F1 \| \| \| \| Battery \| 4 \| 0.864 \| 0.591 \| 0.702 \| 0.864 \| 0.636 \| 0.733 \| \| Memory \| 3 \| 0.643 \| 1.000 \| 0.78...	\| R.T(second) \| 2.74 \| 2.67 \| 2.41 \| 3.29 \| 3.30 \| 3.44 \| 4.69 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| M.U(MB) \| 0.42 \| 0.42 \| 0.42 \| 0.42 \| 0.42 \| 0.42 \| 0.42 \|	0
\| Method \| #ofHiddenLayers \| #ofHiddenUnits \| \| --- \| --- \| --- \| \| 1DVector \| One \| 200;400;600;800;1000 \|	\| Two \| [200,200];[400,400];[600,600];[800,800] \| \| \| --- \| --- \| --- \| \| 2DConvolving \| One \| 144;169;196;255;256;289 \| \| Two \| [361,100] \| \|	1
\| Method \| #ofHiddenLayers \| #ofHiddenUnits \| \| --- \| --- \| --- \| \| 1DVector \| One \| 200;400;600;800;1000 \|	\| 2DConvolutional(ﬁlters=64,kernelsize=3,activation=”relu”,padding=”same”) \| \| --- \| \| 2DConvolutional(ﬁlters=64,kernelsize=3,activation=“relu”,padding=“same”) \| \| 2DConvolutional(ﬁlters=64,kernelsize=3,activation=“relu”,padding=“same”) \| \| MaxPooling(poolsize=(2,2),strides=(2,2)) \| \| 2DConvolutional(ﬁlters=128,kernels...	0
\| Method \| #ofHiddenLayers \| #ofHiddenUnits \| \| --- \| --- \| --- \| \| 1DVector \| One \| 200;400;600;800;1000 \|	\| Two \| [200,200];[400,400];[600,600];[800,800] \| \| \| --- \| --- \| --- \| \| 2DConvolving \| One \| 144;169;196;255;256;289 \| \| Two \| [361,100] \| \|	1
\| Method \| #ofHiddenLayers \| #ofHiddenUnits \| \| --- \| --- \| --- \| \| 1DVector \| One \| 200;400;600;800;1000 \|	\| 2DConvolutional(ﬁlters=512,kernelsize=3,activation=“relu”,padding=“same”) \| \| --- \| \| 2DConvolutional(ﬁlters=512,kernelsize=3,activation=“relu”,padding=“same”) \| \| 2DConvolutional(ﬁlters=512,kernelsize=3,activation=“relu”,padding=“same”) \| \| MaxPooling(poolsize=(2,2),strides=(2,2)) \| \| Dense(1024,activation=“relu”) \|...	0
\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.208 \| 0.158 \| 0.167 \| 0.175 \| \| CosLMI \| 0.416 \| 0.395 \| 0.251 \| 0.269 \| \| CosPPMI \| 0.52 \| 0.496 \| 0.378 \| 0.396 \| \| CosSVD-Freq300 \| 0.240 \| 0.214 \| 0.051 \| 0.084 \| \| CosSVD-LMI300 \| 0.418 \| 0.393 \| 0.14...	\| APSynLMI-1000 \| 0.32 \| 0.29 \| 0.259 \| 0.241 \| \| --- \| --- \| --- \| --- \| --- \| \| APSynLMI-500 \| 0.355 \| 0.319 \| 0.261 \| 0.284 \| \| APSynLMI-100 \| 0.388 \| 0.335 \| 0.233 \| 0.27 \| \| APSynPPMI-1000 \| 0.519 \| 0.525 \| 0.337 \| 0.397 \| \| APSynPPMI-500 \| 0.564 \| 0.546 \| 0.361 \| 0.382 \| \| PMIAPSynPPMI-100 \| 0.562 \| 0.553 \| 0.287...	1
\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.208 \| 0.158 \| 0.167 \| 0.175 \| \| CosLMI \| 0.416 \| 0.395 \| 0.251 \| 0.269 \| \| CosPPMI \| 0.52 \| 0.496 \| 0.378 \| 0.396 \| \| CosSVD-Freq300 \| 0.240 \| 0.214 \| 0.051 \| 0.084 \| \| CosSVD-LMI300 \| 0.418 \| 0.393 \| 0.14...	\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.335 \| 0.334 \| 0.03 \| 0.05 \| \| CosLMI \| 0.638 \| 0.663 \| 0.293 \| 0.34 \| \| CosPPMI \| 0.672 \| 0.675 \| 0.441 \| 0.446 \| \| CosSVD-Freq300 \| 0.35 \| 0.363 \| -0.013 \| 0.001 \| \| CosSVD-LMI300 \| 0.604 \| 0.626 \| 0.222 ...	0
\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.208 \| 0.158 \| 0.167 \| 0.175 \|	\| CosLMI \| 0.416 \| 0.395 \| 0.251 \| 0.269 \| \| --- \| --- \| --- \| --- \| --- \| \| CosPPMI \| 0.52 \| 0.496 \| 0.378 \| 0.396 \| \| CosSVD-Freq300 \| 0.240 \| 0.214 \| 0.051 \| 0.084 \| \| CosSVD-LMI300 \| 0.418 \| 0.393 \| 0.141 \| 0.151 \| \| CosSVD-PPMI300 \| 0.550 \| 0.522 \| 0.325 \| 0.323 \| \| APSynLMI-1000 \| 0.32 \| 0.29 \| 0.259 \| 0.241 \| \| ...	1
\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.208 \| 0.158 \| 0.167 \| 0.175 \|	\| APSynLMI-1000 \| 0.609 \| 0.609 \| 0.317 \| 0.36 \| \| --- \| --- \| --- \| --- \| --- \| \| APSynLMI-500 \| 0.599 \| 0.601 \| 0.289 \| 0.344 \| \| APSynLMI-100 \| 0.566 \| 0.574 \| 0.215 \| 0.271 \| \| APSynPPMI-1000 \| 0.692 \| 0.726 \| 0.507 \| 0.568 \| \| APSynPPMI-500 \| 0.699 \| 0.742 \| 0.508 \| 0.571 \| \| APSynPPMI-100 \| 0.66 \| 0.692 \| 0.482 \|...	0
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	1
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \| \| $ \| 4in5 \| Usedinbusiness \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	0
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	1
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| π \| 1in5 \| Commoninmath \| \| --- \| --- \| --- \| \| $ \| 4in5 \| Usedinbusiness \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	0
\| \| 30dB \| 20dB \| 10dB \| \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.03±.046 \| 3.03±.046 \| 3.05±.046 \| \| Linear-ILD \| 2.31±.036 \| 2.32±.036 \| 2.36±.037 \|	\| DNN-IPD \| 0.48±.009 \| 0.48±.009 \| 0.48±.009 \| \| --- \| --- \| --- \| --- \| \| Linear-IPD \| 0.40±.008 \| 0.40±.008 \| 0.41±.008 \|	1
\| \| 30dB \| 20dB \| 10dB \| \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.03±.046 \| 3.03±.046 \| 3.05±.046 \| \| Linear-ILD \| 2.31±.036 \| 2.32±.036 \| 2.36±.037 \|	\| \| 1cm \| 2cm \| 4cm \| 8cm \| \| --- \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.01±.044 \| 3.03±.046 \| 3.10±.047 \| 3.23±.049 \| \| Linear-ILD \| 0.92±.017 \| 2.21±.036 \| 3.18±.049 \| 3.43±.052 \| \| DNN-IPD \| 0.46±.008 \| 0.48±.009 \| 0.50±.009 \| 0.54±.009 \| \| Linear-IPD \| 0.17±.005 \| 0.38±.008 \| 0.53±.009 \| 0.57±.010 \|	0
\| \| 30dB \| 20dB \| 10dB \| \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.03±.046 \| 3.03±.046 \| 3.05±.046 \| \| Linear-ILD \| 2.31±.036 \| 2.32±.036 \| 2.36±.037 \|	\| DNN-IPD \| 0.48±.009 \| 0.48±.009 \| 0.48±.009 \| \| --- \| --- \| --- \| --- \| \| Linear-IPD \| 0.40±.008 \| 0.40±.008 \| 0.41±.008 \|	1
\| \| 30dB \| 20dB \| 10dB \| \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.03±.046 \| 3.03±.046 \| 3.05±.046 \| \| Linear-ILD \| 2.31±.036 \| 2.32±.036 \| 2.36±.037 \|	\| DNN-IPD \| 0.46±.008 \| 0.48±.009 \| 0.50±.009 \| 0.54±.009 \| \| --- \| --- \| --- \| --- \| --- \| \| Linear-IPD \| 0.17±.005 \| 0.38±.008 \| 0.53±.009 \| 0.57±.010 \|	0
\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)115 \| 0.7 \|	\| (S,S)511 \| 0.6 \| \| --- \| --- \| \| (S,S)1223 \| 0.3 \| \| (S,S)2312 \| 0.1 \|	1
\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)115 \| 0.7 \|	\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)835 \| 0.6 \| \| (S,S)358 \| 0.2 \| \| (S,S)1013 \| 0.7 \| \| (S,S)1310 \| 0.3 \|	0
\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)115 \| 0.7 \| \| (S,S)511 \| 0.6 \|	\| (S,S)1223 \| 0.3 \| \| --- \| --- \| \| (S,S)2312 \| 0.1 \|	1
\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)115 \| 0.7 \| \| (S,S)511 \| 0.6 \|	\| (S,S)358 \| 0.2 \| \| --- \| --- \| \| (S,S)1013 \| 0.7 \| \| (S,S)1310 \| 0.3 \|	0
\| Rule1 \| Rule2 \| Rule3 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IG \| CC \| IG \| CC \| IG \| CC \| \| \| Age \| 0 \| 0.04 \| 0 \| 0 \| 0 \| -0.10 \| \| Gender \| 0.06 \| 0.14 \| 0.06 \| 0.13 \| 0 \| -0.06 \|	\| Job \| 0.08 \| -0.18 \| 0.08 \| -0.17 \| 0.04 \| 0.11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Studies \| 0 \| 0.18 \| 0 \| 0.16 \| 0 \| 0.017 \| \| Freq.ofuse \| 0.03 \| 0.05 \| 0.06 \| 0.08 \| 0.02 \| -0.07 \| \| #Accounts \| 0 \| 0.14 \| 0 \| 0.16 \| 0 \| -0.05 \| \| Priv.Concern \| 0 \| 0.13 \| 0 \| 0.16 \| 0 \| 0.04 \|	1
\| Rule1 \| Rule2 \| Rule3 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IG \| CC \| IG \| CC \| IG \| CC \| \| \| Age \| 0 \| 0.04 \| 0 \| 0 \| 0 \| -0.10 \| \| Gender \| 0.06 \| 0.14 \| 0.06 \| 0.13 \| 0 \| -0.06 \|	\| Group \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| 12 \| 13 \| rule \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| 77.80 \| 63.41 \| 51.43 \| 81.72 \| 81.49 \| 76.23 \| 81.41 \| 78.60 \| 81.49 \| 81.49 \| \| 2 \| 14.68 \| 10.87 \| 2.08 \| 16.18 \| 15.95 \| 14.68 \| 16.07 \| 16.07 \| 15.95 \| 15.95 \| \| 3 \| 65.94 \| 41.16 \| 33....	0
\| Rule1 \| Rule2 \| Rule3 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IG \| CC \| IG \| CC \| IG \| CC \| \| \| Age \| 0 \| 0.04 \| 0 \| 0 \| 0 \| -0.10 \| \| Gender \| 0.06 \| 0.14 \| 0.06 \| 0.13 \| 0 \| -0.06 \| \| Job \| 0.08 \| -0.18 \| 0.08 \| -0.17 \| 0.04 \| 0.11 \| \| Studies \| 0 \| 0.18 \| 0 \| 0.16 \| 0 \| 0.017 \|	\| Freq.ofuse \| 0.03 \| 0.05 \| 0.06 \| 0.08 \| 0.02 \| -0.07 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| #Accounts \| 0 \| 0.14 \| 0 \| 0.16 \| 0 \| -0.05 \| \| Priv.Concern \| 0 \| 0.13 \| 0 \| 0.16 \| 0 \| 0.04 \|	1
\| Rule1 \| Rule2 \| Rule3 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IG \| CC \| IG \| CC \| IG \| CC \| \| \| Age \| 0 \| 0.04 \| 0 \| 0 \| 0 \| -0.10 \| \| Gender \| 0.06 \| 0.14 \| 0.06 \| 0.13 \| 0 \| -0.06 \| \| Job \| 0.08 \| -0.18 \| 0.08 \| -0.17 \| 0.04 \| 0.11 \| \| Studies \| 0 \| 0.18 \| 0 \| 0.16 \| 0 \| 0.017 \|	\| 3 \| 65.94 \| 41.16 \| 33.62 \| 72.75 \| 72.75 \| 69.06 \| 68.98 \| 70.48 \| 74.17 \| 72.75 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 4 \| 73.27 \| 62.18 \| 48.71 \| 77.62 \| 77.62 \| 73.66 \| 77.23 \| 77.62 \| 77.62 \| 77.62 \| \| 5 \| 27.75 \| 18.77 \| 17.66 \| 28.86 \| 28.86 \| 21.39 \| 29.87 \| 28.86 \| 28.86 \| 28...	0
\| (30,5,24,2−2−2)<br>371816<br>(38,5,32,2−2−2)<br>4321186∗<br>(44,5,38,2−2−2−2)<br>5326216∗<br>(54,5,48,2−2−2−2)<br>5929236∗<br>(60,5,54,2−2−2−2)<br>6934266∗<br>(70,5,64,2−2−2−2)<br>7537286∗<br>(76,5,70,2−2−2−2)<br>8341306∗<br>(84,5,78,2−2−2−2)<br>8944326∗<br>(90,5,84,2−2−2−2)<br>974834326∗<br>(98,5,92,2−2−2−2−2) \| (36...	\| (38,7,30,2−2−2−2)<br>452220<br>(46,7,38,2−2−2)<br>512522218∗<br>(52,7,44,2−2−2−2−2)<br>572824238∗<br>(58,7,50,2−2−2−2−2)<br>633126258∗<br>(64,7,46,2−2−2−2−2)<br>693428278∗<br>(70,7,62,2−2−2−2−2)<br>7337308∗<br>(76,7,68,2−2−2−2)<br>8140328∗<br>(82,7,74,2−2−2−2)<br>8743348∗<br>(88,7,80,2−2−2−2)<br>9748378∗<br>(98,7,90,...	1
\| (30,5,24,2−2−2)<br>371816<br>(38,5,32,2−2−2)<br>4321186∗<br>(44,5,38,2−2−2−2)<br>5326216∗<br>(54,5,48,2−2−2−2)<br>5929236∗<br>(60,5,54,2−2−2−2)<br>6934266∗<br>(70,5,64,2−2−2−2)<br>7537286∗<br>(76,5,70,2−2−2−2)<br>8341306∗<br>(84,5,78,2−2−2−2)<br>8944326∗<br>(90,5,84,2−2−2−2)<br>974834326∗<br>(98,5,92,2−2−2−2−2) \| (36...	\| \| \| \| \| --- \| --- \| --- \| \| \| \| \| \| d1<br>d2<br>d3 \| 0,27×104480,25×10<br>291761203628673<br>666<br>0,50×100,45×100,30×10 \| 2,58×10752,58×10<br>66<br>4,12×103474,11×10<br>666<br>24,45×1019,62×103,46×10 \| \| total \| 0,80×100,46×100,58×10 \| 31,15×1019,62×1010,16×10 \| \| DBLP \| Netﬂix \| \| \| \| \| \| \| d1<br>d2<br>d...	0
\| (30,5,24,2−2−2)<br>371816<br>(38,5,32,2−2−2)<br>4321186∗<br>(44,5,38,2−2−2−2)<br>5326216∗<br>(54,5,48,2−2−2−2)<br>5929236∗<br>(60,5,54,2−2−2−2)<br>6934266∗<br>(70,5,64,2−2−2−2)<br>7537286∗<br>(76,5,70,2−2−2−2)<br>8341306∗<br>(84,5,78,2−2−2−2)<br>8944326∗<br>(90,5,84,2−2−2−2)<br>974834326∗<br>(98,5,92,2−2−2−2−2) \| (36...	\| (42,8,33,2−2−2−2)<br>49242221<br>(50,8,41,2−2−2−2)<br>5728259∗<br>(58,8,49,2−2−2−2)<br>67332529<br>(68,8,59,2−2−2−2)<br>7135309∗<br>(72,8,63,2−2−2−2)<br>7738329∗<br>(78,8,69,2−2−2−2)<br>8743359∗<br>(88,8,79,2−2−2−2)<br>9346379∗<br>(94,8,85,2−2−2−2)<br>9949399∗<br>(100,8,91,2−2−2−2) \| (44,8,35,2−2−2)<br>512523<br>(52,...	1
\| (30,5,24,2−2−2)<br>371816<br>(38,5,32,2−2−2)<br>4321186∗<br>(44,5,38,2−2−2−2)<br>5326216∗<br>(54,5,48,2−2−2−2)<br>5929236∗<br>(60,5,54,2−2−2−2)<br>6934266∗<br>(70,5,64,2−2−2−2)<br>7537286∗<br>(76,5,70,2−2−2−2)<br>8341306∗<br>(84,5,78,2−2−2−2)<br>8944326∗<br>(90,5,84,2−2−2−2)<br>974834326∗<br>(98,5,92,2−2−2−2−2) \| (36...	\| d1<br>d2<br>d3 \| 0,79×102370,55×10<br>66<br>2,62×10424070,97×10<br>666<br>3,27×101,61×101,61×10 \| 15,54×1025612,47×10<br>966<br>0,19×100,14×1020,30×10<br>966<br>0,20×1044,52×100,92×10 \| \| --- \| --- \| --- \| \| total \| 6,68×101,61×102,13×10 \| 0,39×1044,66×1033,69×10 \|	0
\| \| AverageReward \| TotalReward \| K-DDiﬀerence \| \| --- \| --- \| --- \| --- \| \| Game1 \| 0.77 \| 155.68 \| 11 \| \| Game2 \| 0.74 \| 149.38 \| 8 \| \| Game3 \| 0.84 \| 168.57 \| 15 \|	\| Game4 \| 0.61 \| 122.12 \| -8 \| \| --- \| --- \| --- \| --- \| \| Game5 \| 0.97 \| 194.40 \| 37 \|	1
\| \| AverageReward \| TotalReward \| K-DDiﬀerence \| \| --- \| --- \| --- \| --- \| \| Game1 \| 0.77 \| 155.68 \| 11 \| \| Game2 \| 0.74 \| 149.38 \| 8 \| \| Game3 \| 0.84 \| 168.57 \| 15 \|	\| MeanRank \| \| \| \| --- \| --- \| --- \| \| 210 \| 119 \| 48.5 \| \| 212 \| 87 \| 45.7 \| \| 167 \| 39 \| 51.7 \| \| 200 \| 113 \| 44.3 \| \| 181 \| 93 \| 49.6 \| \| 213 \| 113 \| 52.0 \| \| 188 \| 85 \| 53.5 \| \| MeanRank \| \| \| \| 263 \| 251 \| 75.4 \| \| 401 \| 338 \| 73.0 \| \| 312 \| 193 \| 81.3 \| \| 168 \| 156 \| 81.2 \|	0
\| \| AverageReward \| TotalReward \| K-DDiﬀerence \| \| --- \| --- \| --- \| --- \| \| Game1 \| 0.77 \| 155.68 \| 11 \|	\| Game2 \| 0.74 \| 149.38 \| 8 \| \| --- \| --- \| --- \| --- \| \| Game3 \| 0.84 \| 168.57 \| 15 \| \| Game4 \| 0.61 \| 122.12 \| -8 \| \| Game5 \| 0.97 \| 194.40 \| 37 \|	1
\| \| AverageReward \| TotalReward \| K-DDiﬀerence \| \| --- \| --- \| --- \| --- \| \| Game1 \| 0.77 \| 155.68 \| 11 \|	\| 167 \| 39 \| 51.7 \| \| --- \| --- \| --- \| \| 200 \| 113 \| 44.3 \| \| 181 \| 93 \| 49.6 \| \| 213 \| 113 \| 52.0 \| \| 188 \| 85 \| 53.5 \| \| MeanRank \| \| \| \| 263 \| 251 \| 75.4 \| \| 401 \| 338 \| 73.0 \| \| 312 \| 193 \| 81.3 \| \| 168 \| 156 \| 81.2 \|	0

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premise string	hypothesis string	label int64
\| Entity \| Categorization \| Conditions \| \| --- \| --- \| --- \| \| EndofSπ(j−1),i \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij−1<br>(q∈III)∧(p∈II)j−1i−2<br>(q∈IV)∧(p∈II)j−1i+2 \| \| \| right(3) \| q∈IIj−1<br>(q∈III)∧(p∈I)j−1i−2<br>(q∈IV)∧(p∈I)j−1i+2 \| \|	\| BeginningofSi,π(j+1) \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| --- \| --- \| --- \| \| left(2) \| q∈Ij+1<br>(q∈III)∧(p∈II)j+1i−2<br>(q∈IV)∧(p∈II)j+1i+2 \| \| \| right(3) \| q∈IIj+1<br>(q∈III)∧(p∈I)j+1i−2<br>(q∈IV)∧(p∈I)j+1i+2 \| \|	1
\| Entity \| Categorization \| Conditions \| \| --- \| --- \| --- \| \| EndofSπ(j−1),i \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij−1<br>(q∈III)∧(p∈II)j−1i−2<br>(q∈IV)∧(p∈II)j−1i+2 \| \| \| right(3) \| q∈IIj−1<br>(q∈III)∧(p∈I)j−1i−2<br>(q∈IV)∧(p∈I)j−1i+2 \| \|	\| \| k=3 \| k=4 \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| h \| q \| r \| t \| q \| r \| t \| q \| \| 1 \| 3x−1 \| 3x+3x+1 \| −3x−1 \| x+x+1 \| x+2x+2 \| −x \| x+1 \| \| 2 \| 2x+x+1<br>2<br>14x+3x−1<br>2<br>14x+17x+4 \| x+x+1<br>2<br>7x+5x+1<br>2<br>7x+5x+1 \| −x<br>−7x−2<br>7x+3 \| 4x+2x+1 \| 2x+2x+1 \| −2x \| 2x+x+2<...	0
\| Entity \| Categorization \| Conditions \| \| --- \| --- \| --- \| \| EndofSπ(j−1),i \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij−1<br>(q∈III)∧(p∈II)j−1i−2<br>(q∈IV)∧(p∈II)j−1i+2 \| \|	\| right(3) \| q∈IIj−1<br>(q∈III)∧(p∈I)j−1i−2<br>(q∈IV)∧(p∈I)j−1i+2 \| \| \| --- \| --- \| --- \| \| BeginningofSi,π(j+1) \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij+1<br>(q∈III)∧(p∈II)j+1i−2<br>(q∈IV)∧(p∈II)j+1i+2 \| \| \| right(3) \| q∈IIj+1<br>(q∈III)∧(p∈I)j+1i−2<br>(q∈IV)∧(p∈I)j+1i+2 \| \|	1
\| Entity \| Categorization \| Conditions \| \| --- \| --- \| --- \| \| EndofSπ(j−1),i \| collinear(1) \| q=pj−1i−1<br>q=pj+1i+1 \| \| left(2) \| q∈Ij−1<br>(q∈III)∧(p∈II)j−1i−2<br>(q∈IV)∧(p∈II)j−1i+2 \| \|	\| 2 \| 2x+x+1<br>2<br>14x+3x−1<br>2<br>14x+17x+4 \| x+x+1<br>2<br>7x+5x+1<br>2<br>7x+5x+1 \| −x<br>−7x−2<br>7x+3 \| 4x+2x+1 \| 2x+2x+1 \| −2x \| 2x+x+2<br>2<br>6x+3x+1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 3 \| 3x+2x+2 \| x+x+1 \| −x \| 5x+9x+9<br>2<br>25x+15x+3<br>2<br>25x+25x+7 \| x+2x+2<br>2<br>5x+4x+1<br>2<br>5...	0
\| n \| n-k \| m \| q \| d \| t \| r’ \| r \| GVR \| Singleton \| pk(bits) \| sign(bits) \| LP \| Dual \| DS \| DA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5 \| 8 \| 57600 \| 8640 \| 130 \| 1096 \| 400 \| 776 \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5...	\| 48 \| 12 \| 40 \| 2 \| 4 \| 5 \| 3 \| 8 \| 6 \| 10 \| 78720 \| 2976 \| ¿600 \| 340 \| 164 \| 114 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 50 \| 10 \| 42 \| 2 \| 5 \| 5(2) \| 2 \| 7 \| 5 \| 9 \| 70560 \| 2800 \| ¿600 \| 240 \| 180 \| 104 \|	1
\| n \| n-k \| m \| q \| d \| t \| r’ \| r \| GVR \| Singleton \| pk(bits) \| sign(bits) \| LP \| Dual \| DS \| DA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5 \| 8 \| 57600 \| 8640 \| 130 \| 1096 \| 400 \| 776 \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5...	\| 8-bit \| 12-bit \| 16-bit \| 24-bit \| 36-bit \| \| --- \| --- \| --- \| --- \| --- \| \| 14.19 \| 13.26 \| 13.13 \| 13.84 \| 14.90 \| \| 16.57 \| 17.12 \| 16.94 \| 17.08 \| 17.38 \| \| 21.97 \| 22.76 \| 23.91 \| 25.89 \| 27.53 \| \| 30.93 \| 32.50 \| 33.59 \| 35.36 \| 35.59 \| \| 15.06 \| 15.43 \| 15.64 \| 16.07 \| 16.78 \| \| 63.48 \| 66.97 \| 68.83 \| 73.45 ...	0
\| n \| n-k \| m \| q \| d \| t \| r’ \| r \| GVR \| Singleton \| pk(bits) \| sign(bits) \| LP \| Dual \| DS \| DA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5 \| 8 \| 57600 \| 8640 \| 130 \| 1096 \| 400 \| 776 \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5...	\| 20 \| 10 \| 24 \| 2 \| 2 \| 3 \| 5 \| 8 \| 6 \| 10 \| 24960 \| 3008 \| 190 \| 370 \| 104 \| 226 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 27 \| 9 \| 20 \| 2 \| 3 \| 2 \| 3 \| 5 \| 4 \| 7 \| 23328 \| 1470 \| 170 \| 187 \| 120 \| 129 \| \| 48 \| 12 \| 40 \| 2 \| 4 \| 5 \| 3 \| 8 \| 6 \| 10 \| 78720 \| ...	1
\| n \| n-k \| m \| q \| d \| t \| r’ \| r \| GVR \| Singleton \| pk(bits) \| sign(bits) \| LP \| Dual \| DS \| DA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5 \| 8 \| 57600 \| 8640 \| 130 \| 1096 \| 400 \| 776 \| \| 16 \| 8 \| 18 \| 2 \| 2 \| 2 \| 4 \| 6 \| 5...	\| 16.57 \| 17.12 \| 16.94 \| 17.08 \| 17.38 \| \| --- \| --- \| --- \| --- \| --- \| \| 21.97 \| 22.76 \| 23.91 \| 25.89 \| 27.53 \| \| 30.93 \| 32.50 \| 33.59 \| 35.36 \| 35.59 \| \| 15.06 \| 15.43 \| 15.64 \| 16.07 \| 16.78 \| \| 63.48 \| 66.97 \| 68.83 \| 73.45 \| 74.66 \| \| 56.67 \| 71.41 \| 76.50 \| 80.40 \| 82.73 \| \| 59.09 \| 61.17 \| 68.15 \| 69.22 \| 70...	0
\| Method \| N \| E \| C \| \| --- \| --- \| --- \| --- \| \| SPINN-NPencoders \| 80.6 \| 88.2 \| 85.5 \| \| mLSTM \| 81.6 \| 91.6 \| 87.4 \|	\| word-wordattentionandaggregation \| 83.7 \| 92.1 \| 86.7 \| \| --- \| --- \| --- \| --- \| \| Ourmodelwithbi-LSTMencoders \| 84.3 \| 90.6 \| 86.9 \| \| Ourmodelwithbtree-LSTMencoders \| 84.8 \| 93.2 \| 87.4 \|	1
\| Method \| N \| E \| C \| \| --- \| --- \| --- \| --- \| \| SPINN-NPencoders \| 80.6 \| 88.2 \| 85.5 \| \| mLSTM \| 81.6 \| 91.6 \| 87.4 \|	\| Reference \| Lexicon \| LM \| CH \| SW \| \| --- \| --- \| --- \| --- \| --- \| \| Current \| N \| N \| 26.4 \| 17.2 \| \| Current \| N \| CharNG \| 21.8 \| 13.8 \| \| (ensemble) \| Y \| WordNG \| 19.3 \| 12.6 \| \| Current \| Y \| WordNG \| 18.7 \| 11.3 \| \| Current \| Y \| WordRNN \| 17.7 \| 10.2 \|	0
\| Method \| N \| E \| C \| \| --- \| --- \| --- \| --- \| \| SPINN-NPencoders \| 80.6 \| 88.2 \| 85.5 \| \| mLSTM \| 81.6 \| 91.6 \| 87.4 \|	\| word-wordattentionandaggregation \| 83.7 \| 92.1 \| 86.7 \| \| --- \| --- \| --- \| --- \| \| Ourmodelwithbi-LSTMencoders \| 84.3 \| 90.6 \| 86.9 \| \| Ourmodelwithbtree-LSTMencoders \| 84.8 \| 93.2 \| 87.4 \|	1
\| Method \| N \| E \| C \| \| --- \| --- \| --- \| --- \| \| SPINN-NPencoders \| 80.6 \| 88.2 \| 85.5 \| \| mLSTM \| 81.6 \| 91.6 \| 87.4 \|	\| Current \| Y \| WordNG \| 18.7 \| 11.3 \| \| --- \| --- \| --- \| --- \| --- \| \| Current \| Y \| WordRNN \| 17.7 \| 10.2 \|	0
\| Numbers \| Category0 \| Categoryvoid \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Passed \| Failed \| Passed \| Failed \|	\| Twitter \| 12,681 \| 2,228 \| 21,443 \| 4,123 \| \| --- \| --- \| --- \| --- \| --- \| \| Facebook \| 7,586 \| 1,287 \| 21,715 \| 3,579 \| \| Total \| 20,267 \| 3,515 \| 43,158 \| 7,702 \|	1
\| Numbers \| Category0 \| Categoryvoid \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Passed \| Failed \| Passed \| Failed \|	\| Category \| Numberoftweets \| \| --- \| --- \| \| Totaltweets \| 3545 \| \| Tweetsinfavor \| 964 \| \| Tweetsagainst \| 647 \| \| Neutraltweets \| 1934 \|	0
\| Numbers \| Category0 \| Categoryvoid \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Passed \| Failed \| Passed \| Failed \|	\| Twitter \| 12,681 \| 2,228 \| 21,443 \| 4,123 \| \| --- \| --- \| --- \| --- \| --- \| \| Facebook \| 7,586 \| 1,287 \| 21,715 \| 3,579 \| \| Total \| 20,267 \| 3,515 \| 43,158 \| 7,702 \|	1
\| Numbers \| Category0 \| Categoryvoid \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Passed \| Failed \| Passed \| Failed \|	\| Tweetsinfavor \| 964 \| \| --- \| --- \| \| Tweetsagainst \| 647 \| \| Neutraltweets \| 1934 \|	0
\| Name \| NumberofEdges \| NumberofTriangles \| \| --- \| --- \| --- \| \| cit-HepTh-dates \| 38488 \| 1418 \| \| wiki-Vote \| 201524 \| 608389 \| \| email-Enron \| 367662 \| 727044 \|	\| soc-sign-epinions \| 1422420 \| 4910076 \| \| --- \| --- \| --- \| \| ﬂickrEdges \| 4633896 \| 107987357 \| \| web-Google \| 8644102 \| 13391903 \| \| cit-Patents \| 33037894 \| 7515023 \|	1
\| Name \| NumberofEdges \| NumberofTriangles \| \| --- \| --- \| --- \| \| cit-HepTh-dates \| 38488 \| 1418 \| \| wiki-Vote \| 201524 \| 608389 \| \| email-Enron \| 367662 \| 727044 \|	\| Entity \| Ni \| Vi \| Mi \| StructureType \| \| --- \| --- \| --- \| --- \| --- \| \| latlon \| 1624984 \| 1625197 \| 1506465 \| Identity \| \| lat \| 1624984 \| 1625192 \| 1504469 \| Identity \| \| lon \| 1625061 \| 1625725 \| 1504619 \| Identity \| \| place \| 1741337 \| 1741516 \| 1504619 \| Identity \| \| retweetID \| 636455 \| 636644 \| 627163 \| Iden...	0
\| Name \| NumberofEdges \| NumberofTriangles \| \| --- \| --- \| --- \| \| cit-HepTh-dates \| 38488 \| 1418 \| \| wiki-Vote \| 201524 \| 608389 \| \| email-Enron \| 367662 \| 727044 \| \| soc-sign-epinions \| 1422420 \| 4910076 \| \| ﬂickrEdges \| 4633896 \| 107987357 \|	\| web-Google \| 8644102 \| 13391903 \| \| --- \| --- \| --- \| \| cit-Patents \| 33037894 \| 7515023 \|	1
\| Name \| NumberofEdges \| NumberofTriangles \| \| --- \| --- \| --- \| \| cit-HepTh-dates \| 38488 \| 1418 \| \| wiki-Vote \| 201524 \| 608389 \| \| email-Enron \| 367662 \| 727044 \| \| soc-sign-epinions \| 1422420 \| 4910076 \| \| ﬂickrEdges \| 4633896 \| 107987357 \|	\| lat \| 1624984 \| 1625192 \| 1504469 \| Identity \| \| --- \| --- \| --- \| --- \| --- \| \| lon \| 1625061 \| 1625725 \| 1504619 \| Identity \| \| place \| 1741337 \| 1741516 \| 1504619 \| Identity \| \| retweetID \| 636455 \| 636644 \| 627163 \| Identity \| \| reuserID \| 720624 \| 722148 \| 676616 \| Identity \| \| time \| 2020000 \| 2020000 \| 35176 \|...	0
\| \| Omniscient:<br>fulldistances \| Agents’ordinalprefs<br>andfacilitylocations \| Onlyagents’ordinal<br>prefs(lowerbounds) \| \| --- \| --- \| --- \| --- \| \| Total(Sum)SocialChoice \| 1 \| 3 \| 5(3) \| \| MedianSocialChoice \| 1 \| 3 \| 5(5) \| \| MinWeightBipartiteMatching \| 1 \| 3 \| n(3) \| \| EgalitarianBipartiteMatching \| 1 \| 3 \| -(...	\| k-center \| 2 \| 5 \| -(-) \| \| --- \| --- \| --- \| --- \| \| k-median \| 2.675 \| 6.35 \| -(Ω(n)) \|	1
\| \| Omniscient:<br>fulldistances \| Agents’ordinalprefs<br>andfacilitylocations \| Onlyagents’ordinal<br>prefs(lowerbounds) \| \| --- \| --- \| --- \| --- \| \| Total(Sum)SocialChoice \| 1 \| 3 \| 5(3) \| \| MedianSocialChoice \| 1 \| 3 \| 5(5) \| \| MinWeightBipartiteMatching \| 1 \| 3 \| n(3) \| \| EgalitarianBipartiteMatching \| 1 \| 3 \| -(...	\| pair \| ∅ \| ω1 \| ω2 \| ω3 \| ω4 \| Ω \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (1,2) \| 0 \| 0.13975 \| 0.23775 \| 0.232 \| 0.025 \| 0.3655 \| \| (2,3) \| 0 \| 0.1765 \| 0.05 \| 0.10825 \| 0.17 \| 0.49525 \| \| (2,4) \| 0 \| 0.1 \| 0.152 \| 0.138 \| 0.14875 \| 0.46125 \| \| (3,4) \| 0 \| 0.1765 \| 0.05 \| 0.10825 \| 0.17 \| 0.49525 \| \| (4,5) \| ...	0
\| \| Omniscient:<br>fulldistances \| Agents’ordinalprefs<br>andfacilitylocations \| Onlyagents’ordinal<br>prefs(lowerbounds) \| \| --- \| --- \| --- \| --- \| \| Total(Sum)SocialChoice \| 1 \| 3 \| 5(3) \| \| MedianSocialChoice \| 1 \| 3 \| 5(5) \| \| MinWeightBipartiteMatching \| 1 \| 3 \| n(3) \|	\| EgalitarianBipartiteMatching \| 1 \| 3 \| -(2) \| \| --- \| --- \| --- \| --- \| \| FacilityLocation \| 1.488 \| 3.976 \| ∞(∞) \| \| k-center \| 2 \| 5 \| -(-) \| \| k-median \| 2.675 \| 6.35 \| -(Ω(n)) \|	1
\| \| Omniscient:<br>fulldistances \| Agents’ordinalprefs<br>andfacilitylocations \| Onlyagents’ordinal<br>prefs(lowerbounds) \| \| --- \| --- \| --- \| --- \| \| Total(Sum)SocialChoice \| 1 \| 3 \| 5(3) \| \| MedianSocialChoice \| 1 \| 3 \| 5(5) \| \| MinWeightBipartiteMatching \| 1 \| 3 \| n(3) \|	\| (2,4) \| 0 \| 0.1 \| 0.152 \| 0.138 \| 0.14875 \| 0.46125 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (3,4) \| 0 \| 0.1765 \| 0.05 \| 0.10825 \| 0.17 \| 0.49525 \| \| (4,5) \| 0 \| 0.241 \| 0.234 \| 0.152 \| 0.06775 \| 0.30525 \|	0
\| \| 3newslices \| 2newslices \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| #runs \| 1 \| 2 \| 3 \| 4 \| 5 \| \| initial \| 8.73 \| 9.01 \| 8.89 \| 6.54 \| 6.98 \|	\| master<br>mintrack<br>maxtrack \| 6.06<br>5.96<br>6.06 \| 6.22<br>6.16<br>6.24 \| 6.18<br>6.07<br>6.23 \| 6.67<br>6.60<br>6.11 \| 7.10<br>7.02<br>7.15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| total \| 14.9 \| 15.4 \| 15.3 \| 13.4 \| 14.2 \|	1
\| \| 3newslices \| 2newslices \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| #runs \| 1 \| 2 \| 3 \| 4 \| 5 \| \| initial \| 8.73 \| 9.01 \| 8.89 \| 6.54 \| 6.98 \|	\| 8<br>4<br>2 \| 895542<br>1791084<br>3582168 \| 934040<br>1808784<br>3585869 \| \| --- \| --- \| --- \| \| 8<br>4<br>2 \| 3203546<br>6407091<br>7<br>1.2814·10 \| 3219769<br>6427398<br>7<br>1.2827·10 \| \| 8<br>4<br>2 \| 8.928·10<br>9<br>1.7856·10<br>9<br>3.5712·10 \| 8.9394·10<br>9<br>1.7871·10<br>9<br>3.573·10 \| \| 8<br>4<br>2 \| 1....	0
\| \| 3newslices \| 2newslices \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| #runs \| 1 \| 2 \| 3 \| 4 \| 5 \|	\| initial \| 8.73 \| 9.01 \| 8.89 \| 6.54 \| 6.98 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| master<br>mintrack<br>maxtrack \| 6.06<br>5.96<br>6.06 \| 6.22<br>6.16<br>6.24 \| 6.18<br>6.07<br>6.23 \| 6.67<br>6.60<br>6.11 \| 7.10<br>7.02<br>7.15 \| \| total \| 14.9 \| 15.4 \| 15.3 \| 13.4 \| 14.2 \|	1
\| \| 3newslices \| 2newslices \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| #runs \| 1 \| 2 \| 3 \| 4 \| 5 \|	\| 8<br>4<br>2 \| 1.2497·10<br>9<br>2.4995·10<br>9<br>4.9989·10 \| 1.251·10<br>9<br>2.5005·10<br>9<br>4.9995·10 \| \| --- \| --- \| --- \| \| 8 \| 6.4582·10 \| 6.4651·10 \|	0
\| Class \| Guptaet.al \| RGB \| Depth \| RGB-D \| \| --- \| --- \| --- \| --- \| --- \| \| bedroom \| 79 \| 79.06 \| 78.01 \| 82.72 \| \| kitchen \| 74 \| 65.09 \| 60.38 \| 75.47 \| \| livingroom \| 47 \| 73.83 \| 33.64 \| 75.70 \| \| bathroom \| 67 \| 89.66 \| 81.03 \| 96.55 \| \| diningroom \| 47 \| 96.36 \| 50.91 \| 96.36 \| \| oﬃce \| 24 \| 63.16 \| 13.16 \| 7...	\| others \| 15 \| 85.37 \| 39.02 \| 95.12 \| \| --- \| --- \| --- \| --- \| --- \| \| meandiag.cm. \| 47 \| 79.29 \| 48.11 \| 83.81 \| \| avg.accuracy \| 58 \| 77.52 \| 55.81 \| 82.42 \|	1
\| Class \| Guptaet.al \| RGB \| Depth \| RGB-D \| \| --- \| --- \| --- \| --- \| --- \| \| bedroom \| 79 \| 79.06 \| 78.01 \| 82.72 \| \| kitchen \| 74 \| 65.09 \| 60.38 \| 75.47 \| \| livingroom \| 47 \| 73.83 \| 33.64 \| 75.70 \| \| bathroom \| 67 \| 89.66 \| 81.03 \| 96.55 \| \| diningroom \| 47 \| 96.36 \| 50.91 \| 96.36 \| \| oﬃce \| 24 \| 63.16 \| 13.16 \| 7...	\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \| \| chair \| 0.0118 \| 0.0238 \| 0.0444 \| \| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \| \| nightstand \| 0.0080 \| 0.0248 \| 0.2925 \| \|...	0
\| Class \| Guptaet.al \| RGB \| Depth \| RGB-D \| \| --- \| --- \| --- \| --- \| --- \| \| bedroom \| 79 \| 79.06 \| 78.01 \| 82.72 \| \| kitchen \| 74 \| 65.09 \| 60.38 \| 75.47 \| \| livingroom \| 47 \| 73.83 \| 33.64 \| 75.70 \| \| bathroom \| 67 \| 89.66 \| 81.03 \| 96.55 \|	\| diningroom \| 47 \| 96.36 \| 50.91 \| 96.36 \| \| --- \| --- \| --- \| --- \| --- \| \| oﬃce \| 24 \| 63.16 \| 13.16 \| 71.05 \| \| homeoﬃce \| 8.3 \| 70.83 \| 0.00 \| 62.50 \| \| classroom \| 48 \| 69.57 \| 52.17 \| 82.61 \| \| bookstore \| 64 \| 100.00 \| 72.73 \| 100.00 \| \| others \| 15 \| 85.37 \| 39.02 \| 95.12 \| \| meandiag.cm. \| 47 \| 79.29 \| 48.11 ...	1
\| Class \| Guptaet.al \| RGB \| Depth \| RGB-D \| \| --- \| --- \| --- \| --- \| --- \| \| bedroom \| 79 \| 79.06 \| 78.01 \| 82.72 \| \| kitchen \| 74 \| 65.09 \| 60.38 \| 75.47 \| \| livingroom \| 47 \| 73.83 \| 33.64 \| 75.70 \| \| bathroom \| 67 \| 89.66 \| 81.03 \| 96.55 \|	\| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| --- \| --- \| --- \| --- \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \| \| nightstand \| 0.0080 \| 0.0248 \| 0.2925 \| \| sofa \| 0.0068 \| 0.0186 \| 0.0563 \| \| table \| 0.0051 \| 0.0326 \| 0.0340 \| \| toilet \| 0.0119 \| 0.0180 \| 0.0977 \| \| Avg. \| 0.0092 \| 0.0259 \| ...	0
\| GraphMotif \| Static<br>SubgraphsTime \| δ=1hour<br>SubgraphsTimeSpeedUp \| \| --- \| --- \| --- \| \| CollegeMSGM1<br>CollegeMSGM2<br>CollegeMSGM3<br>CollegeMSGM4<br>CollegeMSGM5<br>CollegeMSGM6 \| 87618.8<br>238K43.2<br>1.57K19.3<br>482.18<br>202M9.88K<br>10.9M2.02K \| 3.46K0.54634.5x<br>75.4K2.0421.1x<br>1.06K0.49938.6x<br>...	\| MathOverﬂowM1<br>MathOverﬂowM2<br>MathOverﬂowM3<br>MathOverﬂowM4<br>MathOverﬂowM5<br>MathOverﬂowM6 \| 1.42M2.94K<br>218M11.4K<br>5.53M4.02K<br>285K2.34K<br>—>24hr<br>—>24hr \| 20.0012.94Mx<br>2480.015760Kx<br>40.0014.02Mx<br>00.015156Kx<br>8050.031>2.79Mx<br>1.70K0.062>1.39Mx \| \| --- \| --- \| --- \| \| EnronM1<br>EnronM2<...	1
\| GraphMotif \| Static<br>SubgraphsTime \| δ=1hour<br>SubgraphsTimeSpeedUp \| \| --- \| --- \| --- \| \| CollegeMSGM1<br>CollegeMSGM2<br>CollegeMSGM3<br>CollegeMSGM4<br>CollegeMSGM5<br>CollegeMSGM6 \| 87618.8<br>238K43.2<br>1.57K19.3<br>482.18<br>202M9.88K<br>10.9M2.02K \| 3.46K0.54634.5x<br>75.4K2.0421.1x<br>1.06K0.49938.6x<br>...	\| Graph \| Nodes<br>(Million) \| Edges<br>(Million) \| Outdegrees<br>MaxAvgσ \| \| --- \| --- \| --- \| --- \| \| rmat20 \| 1.05 \| 8.26 \| 1,1818177.40 \| \| road-FLA<br>road-W<br>road-USA \| 1.07<br>6.26<br>23.95 \| 2.71<br>15.12<br>57.71 \| 832.45<br>942.74<br>932.74 \| \| ER20<br>ER23 \| 1.05<br>8.39 \| 4.19<br>33.55 \| 1544.47<br>1034.4...	0
\| GraphMotif \| Static<br>SubgraphsTime \| δ=1hour<br>SubgraphsTimeSpeedUp \| \| --- \| --- \| --- \| \| CollegeMSGM1<br>CollegeMSGM2<br>CollegeMSGM3<br>CollegeMSGM4<br>CollegeMSGM5<br>CollegeMSGM6 \| 87618.8<br>238K43.2<br>1.57K19.3<br>482.18<br>202M9.88K<br>10.9M2.02K \| 3.46K0.54634.5x<br>75.4K2.0421.1x<br>1.06K0.49938.6x<br>...	\| Email-EuM1<br>Email-EuM2<br>Email-EuM3<br>Email-EuM4<br>Email-EuM5<br>Email-EuM6 \| 3.96K33.5<br>367K91.4<br>12.0K40.1<br>5544.99<br>57.7M7.18K<br>7.08M1.54K \| 2581.1429.4x<br>30.1K1.8050.9x<br>4931.1534.7x<br>231.094.57x<br>4.71M13154.9x<br>31.1K2.45627x \| \| --- \| --- \| --- \| \| GraphMotif \| Static<br>SubgraphsTime \| ...	1
\| GraphMotif \| Static<br>SubgraphsTime \| δ=1hour<br>SubgraphsTimeSpeedUp \| \| --- \| --- \| --- \| \| CollegeMSGM1<br>CollegeMSGM2<br>CollegeMSGM3<br>CollegeMSGM4<br>CollegeMSGM5<br>CollegeMSGM6 \| 87618.8<br>238K43.2<br>1.57K19.3<br>482.18<br>202M9.88K<br>10.9M2.02K \| 3.46K0.54634.5x<br>75.4K2.0421.1x<br>1.06K0.49938.6x<br>...	\| road-FLA<br>road-W<br>road-USA \| 1.07<br>6.26<br>23.95 \| 2.71<br>15.12<br>57.71 \| 832.45<br>942.74<br>932.74 \| \| --- \| --- \| --- \| --- \| \| ER20<br>ER23 \| 1.05<br>8.39 \| 4.19<br>33.55 \| 1544.47<br>1034.46 \| \| Graph500<br>(threegraphs) \| 16.78 \| 335.00 \| 924,0002020,900 \|	0
\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	\| -classe1880(attendus=169,ramenes=118.00,corrects=13.00)<br>rappel=0.077precision=0.110f-mesure=0.091 \| \| --- \| \| -classe1890(attendus=197,ramenes=78.00,corrects=9.00)<br>rappel=0.046precision=0.115f-mesure=0.065 \| \| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 \|...	1
\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	0
\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	\| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 \| \| --- \| \| -classe1910(attendus=201,ramenes=67.00,corrects=6.00)<br>rappel=0.030precision=0.090f-mesure=0.045 \| \| -classe1920(attendus=200,ramenes=143.00,corrects=18.00)<br>rappel=0.090precision=0.126f-mesure=0.105 \|...	1
\| -classe1800(attendus=169,ramenes=102.00,corrects=6.00)<br>rappel=0.036precision=0.059f-mesure=0.044 \| \| --- \| \| -classe1810(attendus=169,ramenes=193.00,corrects=27.00)<br>rappel=0.160precision=0.140f-mesure=0.149 \| \| -classe1820(attendus=169,ramenes=321.00,corrects=41.00)<br>rappel=0.243precision=0.128f-mesure=0.167 ...	\| -classe1900(attendus=203,ramenes=135.00,corrects=16.00)<br>rappel=0.079precision=0.119f-mesure=0.095 \| \| --- \| \| -classe1910(attendus=201,ramenes=67.00,corrects=6.00)<br>rappel=0.030precision=0.090f-mesure=0.045 \| \| -classe1920(attendus=200,ramenes=143.00,corrects=18.00)<br>rappel=0.090precision=0.126f-mesure=0.105 \|...	0
\| Algorithms \| Memory(MB) \| \| --- \| --- \| \| VGG \| 2459.8 \| \| SSDGoogleNet \| 320.4 \|	\| SqueezeNet \| 145.3 \| \| --- \| --- \| \| MobileNet \| 172.2 \| \| L-CNN \| 139.5 \|	1
\| Algorithms \| Memory(MB) \| \| --- \| --- \| \| VGG \| 2459.8 \| \| SSDGoogleNet \| 320.4 \|	\| CNN \| Imageresolution \| RunTime(ms) \| \| --- \| --- \| --- \| \| AlexNet \| 227×227 \| 2.3 \| \| VGG16 \| 224×224 \| 10 \| \| Resnet50 \| 224×224 \| 17 \| \| SqueezeNet \| 224×224 \| 2.5 \| \| SqueezeNet \| 448×448 \| 4 \| \| SqueezeNet \| 625×625 \| 6 \|	0
\| Algorithms \| Memory(MB) \| \| --- \| --- \| \| VGG \| 2459.8 \|	\| SSDGoogleNet \| 320.4 \| \| --- \| --- \| \| SqueezeNet \| 145.3 \| \| MobileNet \| 172.2 \| \| L-CNN \| 139.5 \|	1
\| Algorithms \| Memory(MB) \| \| --- \| --- \| \| VGG \| 2459.8 \|	\| SqueezeNet \| 224×224 \| 2.5 \| \| --- \| --- \| --- \| \| SqueezeNet \| 448×448 \| 4 \| \| SqueezeNet \| 625×625 \| 6 \|	0
\| \| SDR(dB) \| SIR(dB) \| SAR(dB) \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| MDLD \| GaussSep \| MDLD \| GaussSep \| MDLD \| GaussSep \|	\| Dev3Female3 \| 6.02 \| 16.93 \| 23.84 \| 22.43 \| 6.17 \| 18.40 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Example3×5 \| 3.91 \| 9.94 \| 17.92 \| 15.21 \| 4.17 \| 11.68 \| \| Example4×8 \| 2.24 \| -18.63 \| 16.4 \| -17.58 \| 2.52 \| 9.39 \|	1
\| \| SDR(dB) \| SIR(dB) \| SAR(dB) \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| MDLD \| GaussSep \| MDLD \| GaussSep \| MDLD \| GaussSep \|	\| \| SDR(dB) \| SIR(dB) \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| WMDLD \| MDLD \| GS \| WMDLD \| MDLD \| GS \| WMDLD \| MDLD \| \| Dev3Female4 \| 11.77 \| 6.02 \| 16.93 \| 22.26 \| 23.84 \| 22.43 \| 12.23 \| 6.17 \| \| Example3×5 \| 8.41 \| 3.91 \| 9.94 \| 17.58 \| 17.92 \| 15.21 \| 9.10 \| 4.17 \| \| Example...	0
\| \| SDR(dB) \| SIR(dB) \| SAR(dB) \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| MDLD \| GaussSep \| MDLD \| GaussSep \| MDLD \| GaussSep \|	\| Dev3Female3 \| 6.02 \| 16.93 \| 23.84 \| 22.43 \| 6.17 \| 18.40 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Example3×5 \| 3.91 \| 9.94 \| 17.92 \| 15.21 \| 4.17 \| 11.68 \| \| Example4×8 \| 2.24 \| -18.63 \| 16.4 \| -17.58 \| 2.52 \| 9.39 \|	1
\| \| SDR(dB) \| SIR(dB) \| SAR(dB) \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| MDLD \| GaussSep \| MDLD \| GaussSep \| MDLD \| GaussSep \|	\| Example3×5 \| 8.41 \| 3.91 \| 9.94 \| 17.58 \| 17.92 \| 15.21 \| 9.10 \| 4.17 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Example4×8 \| 5.29 \| 2.24 \| -18.63 \| 13.72 \| 16.4 \| -17.58 \| 6.23 \| 2.52 \|	0
\| UL<br>GN \| \| --- \| \| UL<br>GN \| \| UL<br>GN \|	\| UL<br>GN \| \| --- \| \| UL<br>GN \|	1
\| UL<br>GN \| \| --- \| \| UL<br>GN \| \| UL<br>GN \|	\| \| \| --- \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \|	0
\| UL<br>GN \| \| --- \| \| UL<br>GN \| \| UL<br>GN \|	\| UL<br>GN \| \| --- \| \| UL<br>GN \|	1
\| UL<br>GN \| \| --- \| \| UL<br>GN \| \| UL<br>GN \|	\| SL<br>DRL \| \| --- \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \| \| SL<br>DRL \|	0
\| Class(Tissuetype) \| T<br>Labely \| \| --- \| --- \| \| class1(BK) \| (1,0,0,0,0) \|	\| class2(LP) \| (0,1,0,0,0) \| \| --- \| --- \| \| class3(FT) \| (0,0,1,0,0) \| \| class4(MT) \| (0,0,0,1,0) \| \| class5(CA) \| (0,0,0,0,1) \|	1
\| Class(Tissuetype) \| T<br>Labely \| \| --- \| --- \| \| class1(BK) \| (1,0,0,0,0) \|	\| TypeofLesion \| Class \| \| --- \| --- \| \| Healthy/NoLesionandBackground \| 0 \| \| NecroticRegion \| 1 \| \| Edema \| 2 \| \| NonEnhancingTumor \| 3 \| \| EnhancingTumor \| 4 \|	0
\| Class(Tissuetype) \| T<br>Labely \| \| --- \| --- \| \| class1(BK) \| (1,0,0,0,0) \| \| class2(LP) \| (0,1,0,0,0) \| \| class3(FT) \| (0,0,1,0,0) \|	\| class4(MT) \| (0,0,0,1,0) \| \| --- \| --- \| \| class5(CA) \| (0,0,0,0,1) \|	1
\| Class(Tissuetype) \| T<br>Labely \| \| --- \| --- \| \| class1(BK) \| (1,0,0,0,0) \| \| class2(LP) \| (0,1,0,0,0) \| \| class3(FT) \| (0,0,1,0,0) \|	\| NonEnhancingTumor \| 3 \| \| --- \| --- \| \| EnhancingTumor \| 4 \|	0
\| \| SubgamePerfect<br>Equilibrium \| PerfectPrediction<br>Equilibrium \| \| --- \| --- \| --- \| \| FormoftheGame \| Extensive \| Extensive \| \| PerfectInformation \| Yes \| Yes \| \| Numberofplayers \| Any \| Any \| \| NewcombChoice \| Twoboxes \| Onebox \| \| PredictionModel<br>accountingfor<br>counterfactuals \| Ad-HocPrediction<br>(“cou...	\| Existence \| Always \| Always \| \| --- \| --- \| --- \| \| Uniqueness \| Always \| Always \| \| Optimality \| - \| Pareto \| \| Corresponding<br>NormalForm \| Nash<br>(subsuming) \| Superrationality(notestablished/<br>conceptuallysimilar) \|	1
\| \| SubgamePerfect<br>Equilibrium \| PerfectPrediction<br>Equilibrium \| \| --- \| --- \| --- \| \| FormoftheGame \| Extensive \| Extensive \| \| PerfectInformation \| Yes \| Yes \| \| Numberofplayers \| Any \| Any \| \| NewcombChoice \| Twoboxes \| Onebox \| \| PredictionModel<br>accountingfor<br>counterfactuals \| Ad-HocPrediction<br>(“cou...	\| Criterion \| MANCaLog \| IC/LT \| SNOP \| CD \| EGT/VM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1.Labels \| Yes \| No \| Yes \| Yes \| No \| \| 2.ExplicitRepresentationofTime \| Yes \| No \| Yes \| No \| Yes \| \| 3.Non-MarkovianTemporalRelationships \| Yes \| No \| No \| No \| No \| \| 4.Uncertainty \| Yes \| Yes \| Yes \| Yes \| Yes \| \| 5.Compe...	0
\| \| SubgamePerfect<br>Equilibrium \| PerfectPrediction<br>Equilibrium \| \| --- \| --- \| --- \| \| FormoftheGame \| Extensive \| Extensive \| \| PerfectInformation \| Yes \| Yes \| \| Numberofplayers \| Any \| Any \| \| NewcombChoice \| Twoboxes \| Onebox \| \| PredictionModel<br>accountingfor<br>counterfactuals \| Ad-HocPrediction<br>(“cou...	\| Uniqueness \| Always \| Always \| \| --- \| --- \| --- \| \| Optimality \| - \| Pareto \| \| Corresponding<br>NormalForm \| Nash<br>(subsuming) \| Superrationality(notestablished/<br>conceptuallysimilar) \|	1
\| \| SubgamePerfect<br>Equilibrium \| PerfectPrediction<br>Equilibrium \| \| --- \| --- \| --- \| \| FormoftheGame \| Extensive \| Extensive \| \| PerfectInformation \| Yes \| Yes \| \| Numberofplayers \| Any \| Any \| \| NewcombChoice \| Twoboxes \| Onebox \| \| PredictionModel<br>accountingfor<br>counterfactuals \| Ad-HocPrediction<br>(“cou...	\| 2.ExplicitRepresentationofTime \| Yes \| No \| Yes \| No \| Yes \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 3.Non-MarkovianTemporalRelationships \| Yes \| No \| No \| No \| No \| \| 4.Uncertainty \| Yes \| Yes \| Yes \| Yes \| Yes \| \| 5.CompetingCascades \| Yes \| No \| No \| Yes \| Yes \| \| 6.Non-monotonicCascades \| Yes \| No \| No \| Yes \| Ye...	0
\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \|	\| chair \| 0.0118 \| 0.0238 \| 0.0444 \| \| --- \| --- \| --- \| --- \| \| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \| \| nightstand \| 0.0080 \| 0.0248 \| 0.2925 \| \| sofa \| 0.0068 \| 0.0186 \| 0.0563 \| \| table \| 0.0051 \| 0.0326 \| 0.0340 \| \| toilet \| 0.0119 \| 0.0180 \|...	1
\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \|	\| category \| ours \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| bathtub \| 0.8348 \| 0.7017 \| 0.7190 \| 0.1644 \| \| bed \| 0.9202 \| 0.7775 \| 0.3963 \| 0.3239 \| \| chair \| 0.9920 \| 0.9700 \| 0.9892 \| 0.8482 \| \| desk \| 0.8203 \| 0.7936 \| 0.8145 \| 0.1068 \| \| dresser \| 0.7678 \| 0.6314 \| 0.7010 \| 0.2166 \| \| monitor \| 0.9473 \| 0.2493 ...	0
\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \| \| chair \| 0.0118 \| 0.0238 \| 0.0444 \| \| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \|	\| nightstand \| 0.0080 \| 0.0248 \| 0.2925 \| \| --- \| --- \| --- \| --- \| \| sofa \| 0.0068 \| 0.0186 \| 0.0563 \| \| table \| 0.0051 \| 0.0326 \| 0.0340 \| \| toilet \| 0.0119 \| 0.0180 \| 0.0977 \| \| Avg. \| 0.0092 \| 0.0259 \| 0.0956 \|	1
\| category \| ours \| \| \| \| --- \| --- \| --- \| --- \| \| bathtub \| 0.0152 \| 0.0266 \| 0.0621 \| \| bed \| 0.0068 \| 0.0240 \| 0.0617 \| \| chair \| 0.0118 \| 0.0238 \| 0.0444 \| \| desk \| 0.0122 \| 0.0298 \| 0.0731 \| \| dresser \| 0.0038 \| 0.0384 \| 0.1558 \| \| monitor \| 0.0103 \| 0.0220 \| 0.0783 \|	\| table \| 0.8910 \| 0.8377 \| 0.8751 \| 0.7902 \| \| --- \| --- \| --- \| --- \| --- \| \| toilet \| 0.9701 \| 0.8569 \| 0.6943 \| 0.8832 \| \| Avg. \| 0.8811 \| 0.7431 \| 0.7006 \| 0.4596 \|	0
\| Facet \| Numberofmanually<br>deﬁnedclusters \| Hierarchicalclustering \| Non-hierarchicalclustering \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Purity \| I-Purity \| F1 \| Purity \| I-Purity \| F1 \| \| \| \| Battery \| 4 \| 0.864 \| 0.591 \| 0.702 \| 0.864 \| 0.636 \| 0.733 \| \| Memory \| 3 \| 0.643 \| 1.000 \| 0.78...	\| Shutter \| 6 \| 0.643 \| 0.929 \| 0.760 \| 0.643 \| 0.929 \| 0.760 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Average \| 5.13 \| 0.676 \| 0.819 \| 0.725 \| 0.714 \| 0.828 \| 0.753 \| \| Battery \| 3 \| 0.824 \| 0.765 \| 0.793 \| 0.765 \| 0.706 \| 0.734 \| \| Camera \| 3 \| 0.727 \| 0.636 \| 0.679 \| 0.727 \| 0.636 \| 0.679 \| \| Headset \| ...	1
\| Facet \| Numberofmanually<br>deﬁnedclusters \| Hierarchicalclustering \| Non-hierarchicalclustering \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Purity \| I-Purity \| F1 \| Purity \| I-Purity \| F1 \| \| \| \| Battery \| 4 \| 0.864 \| 0.591 \| 0.702 \| 0.864 \| 0.636 \| 0.733 \| \| Memory \| 3 \| 0.643 \| 1.000 \| 0.78...	\| \| 5VirtualLandscapeDatasets \| 7VirtualLandscapeDatasets \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| N-based \| G-based \| I-based \| N-based \| G-based \| I-based \| N-based \| \| \| Accuracy \| 0.9931 \| 0.9950 \| 0.9927 \| 0.9939 \| 0.9950 \| 0.9930 \| 0.9936 \| \| Precision \| 0.6154 \| 0.7080 \| 0.7143 \| 0....	0
\| Facet \| Numberofmanually<br>deﬁnedclusters \| Hierarchicalclustering \| Non-hierarchicalclustering \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Purity \| I-Purity \| F1 \| Purity \| I-Purity \| F1 \| \| \| \| Battery \| 4 \| 0.864 \| 0.591 \| 0.702 \| 0.864 \| 0.636 \| 0.733 \| \| Memory \| 3 \| 0.643 \| 1.000 \| 0.78...	\| Format \| 1 \| 1.000 \| 0.714 \| 0.833 \| 1.000 \| 0.571 \| 0.727 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Design \| 1 \| 1.000 \| 1.000 \| 1.000 \| 1.000 \| 1.000 \| 1.000 \| \| Service \| 1 \| 1.000 \| 0.739 \| 0.850 \| 1.000 \| 0.522 \| 0.686 \| \| Picture \| 4 \| 0.800 \| 0.850 \| 0.824 \| 0.800 \| 0.850 \| 0.824 \| \| Average \| 2.00...	1
\| Facet \| Numberofmanually<br>deﬁnedclusters \| Hierarchicalclustering \| Non-hierarchicalclustering \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Purity \| I-Purity \| F1 \| Purity \| I-Purity \| F1 \| \| \| \| Battery \| 4 \| 0.864 \| 0.591 \| 0.702 \| 0.864 \| 0.636 \| 0.733 \| \| Memory \| 3 \| 0.643 \| 1.000 \| 0.78...	\| R.T(second) \| 2.74 \| 2.67 \| 2.41 \| 3.29 \| 3.30 \| 3.44 \| 4.69 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| M.U(MB) \| 0.42 \| 0.42 \| 0.42 \| 0.42 \| 0.42 \| 0.42 \| 0.42 \|	0
\| Method \| #ofHiddenLayers \| #ofHiddenUnits \| \| --- \| --- \| --- \| \| 1DVector \| One \| 200;400;600;800;1000 \|	\| Two \| [200,200];[400,400];[600,600];[800,800] \| \| \| --- \| --- \| --- \| \| 2DConvolving \| One \| 144;169;196;255;256;289 \| \| Two \| [361,100] \| \|	1
\| Method \| #ofHiddenLayers \| #ofHiddenUnits \| \| --- \| --- \| --- \| \| 1DVector \| One \| 200;400;600;800;1000 \|	\| 2DConvolutional(ﬁlters=64,kernelsize=3,activation=”relu”,padding=”same”) \| \| --- \| \| 2DConvolutional(ﬁlters=64,kernelsize=3,activation=“relu”,padding=“same”) \| \| 2DConvolutional(ﬁlters=64,kernelsize=3,activation=“relu”,padding=“same”) \| \| MaxPooling(poolsize=(2,2),strides=(2,2)) \| \| 2DConvolutional(ﬁlters=128,kernels...	0
\| Method \| #ofHiddenLayers \| #ofHiddenUnits \| \| --- \| --- \| --- \| \| 1DVector \| One \| 200;400;600;800;1000 \|	\| Two \| [200,200];[400,400];[600,600];[800,800] \| \| \| --- \| --- \| --- \| \| 2DConvolving \| One \| 144;169;196;255;256;289 \| \| Two \| [361,100] \| \|	1
\| Method \| #ofHiddenLayers \| #ofHiddenUnits \| \| --- \| --- \| --- \| \| 1DVector \| One \| 200;400;600;800;1000 \|	\| 2DConvolutional(ﬁlters=512,kernelsize=3,activation=“relu”,padding=“same”) \| \| --- \| \| 2DConvolutional(ﬁlters=512,kernelsize=3,activation=“relu”,padding=“same”) \| \| 2DConvolutional(ﬁlters=512,kernelsize=3,activation=“relu”,padding=“same”) \| \| MaxPooling(poolsize=(2,2),strides=(2,2)) \| \| Dense(1024,activation=“relu”) \|...	0
\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.208 \| 0.158 \| 0.167 \| 0.175 \| \| CosLMI \| 0.416 \| 0.395 \| 0.251 \| 0.269 \| \| CosPPMI \| 0.52 \| 0.496 \| 0.378 \| 0.396 \| \| CosSVD-Freq300 \| 0.240 \| 0.214 \| 0.051 \| 0.084 \| \| CosSVD-LMI300 \| 0.418 \| 0.393 \| 0.14...	\| APSynLMI-1000 \| 0.32 \| 0.29 \| 0.259 \| 0.241 \| \| --- \| --- \| --- \| --- \| --- \| \| APSynLMI-500 \| 0.355 \| 0.319 \| 0.261 \| 0.284 \| \| APSynLMI-100 \| 0.388 \| 0.335 \| 0.233 \| 0.27 \| \| APSynPPMI-1000 \| 0.519 \| 0.525 \| 0.337 \| 0.397 \| \| APSynPPMI-500 \| 0.564 \| 0.546 \| 0.361 \| 0.382 \| \| PMIAPSynPPMI-100 \| 0.562 \| 0.553 \| 0.287...	1
\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.208 \| 0.158 \| 0.167 \| 0.175 \| \| CosLMI \| 0.416 \| 0.395 \| 0.251 \| 0.269 \| \| CosPPMI \| 0.52 \| 0.496 \| 0.378 \| 0.396 \| \| CosSVD-Freq300 \| 0.240 \| 0.214 \| 0.051 \| 0.084 \| \| CosSVD-LMI300 \| 0.418 \| 0.393 \| 0.14...	\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.335 \| 0.334 \| 0.03 \| 0.05 \| \| CosLMI \| 0.638 \| 0.663 \| 0.293 \| 0.34 \| \| CosPPMI \| 0.672 \| 0.675 \| 0.441 \| 0.446 \| \| CosSVD-Freq300 \| 0.35 \| 0.363 \| -0.013 \| 0.001 \| \| CosSVD-LMI300 \| 0.604 \| 0.626 \| 0.222 ...	0
\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.208 \| 0.158 \| 0.167 \| 0.175 \|	\| CosLMI \| 0.416 \| 0.395 \| 0.251 \| 0.269 \| \| --- \| --- \| --- \| --- \| --- \| \| CosPPMI \| 0.52 \| 0.496 \| 0.378 \| 0.396 \| \| CosSVD-Freq300 \| 0.240 \| 0.214 \| 0.051 \| 0.084 \| \| CosSVD-LMI300 \| 0.418 \| 0.393 \| 0.141 \| 0.151 \| \| CosSVD-PPMI300 \| 0.550 \| 0.522 \| 0.325 \| 0.323 \| \| APSynLMI-1000 \| 0.32 \| 0.29 \| 0.259 \| 0.241 \| \| ...	1
\| Dataset \| WSim(SIM) \| WSim(REL) \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| Window \| 2 \| 3 \| 2 \| 3 \| \| CosFreq \| 0.208 \| 0.158 \| 0.167 \| 0.175 \|	\| APSynLMI-1000 \| 0.609 \| 0.609 \| 0.317 \| 0.36 \| \| --- \| --- \| --- \| --- \| --- \| \| APSynLMI-500 \| 0.599 \| 0.601 \| 0.289 \| 0.344 \| \| APSynLMI-100 \| 0.566 \| 0.574 \| 0.215 \| 0.271 \| \| APSynPPMI-1000 \| 0.692 \| 0.726 \| 0.507 \| 0.568 \| \| APSynPPMI-500 \| 0.699 \| 0.742 \| 0.508 \| 0.571 \| \| APSynPPMI-100 \| 0.66 \| 0.692 \| 0.482 \|...	0
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	1
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \| \| $ \| 4in5 \| Usedinbusiness \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	0
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	1
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| π \| 1in5 \| Commoninmath \| \| --- \| --- \| --- \| \| $ \| 4in5 \| Usedinbusiness \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	0
\| \| 30dB \| 20dB \| 10dB \| \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.03±.046 \| 3.03±.046 \| 3.05±.046 \| \| Linear-ILD \| 2.31±.036 \| 2.32±.036 \| 2.36±.037 \|	\| DNN-IPD \| 0.48±.009 \| 0.48±.009 \| 0.48±.009 \| \| --- \| --- \| --- \| --- \| \| Linear-IPD \| 0.40±.008 \| 0.40±.008 \| 0.41±.008 \|	1
\| \| 30dB \| 20dB \| 10dB \| \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.03±.046 \| 3.03±.046 \| 3.05±.046 \| \| Linear-ILD \| 2.31±.036 \| 2.32±.036 \| 2.36±.037 \|	\| \| 1cm \| 2cm \| 4cm \| 8cm \| \| --- \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.01±.044 \| 3.03±.046 \| 3.10±.047 \| 3.23±.049 \| \| Linear-ILD \| 0.92±.017 \| 2.21±.036 \| 3.18±.049 \| 3.43±.052 \| \| DNN-IPD \| 0.46±.008 \| 0.48±.009 \| 0.50±.009 \| 0.54±.009 \| \| Linear-IPD \| 0.17±.005 \| 0.38±.008 \| 0.53±.009 \| 0.57±.010 \|	0
\| \| 30dB \| 20dB \| 10dB \| \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.03±.046 \| 3.03±.046 \| 3.05±.046 \| \| Linear-ILD \| 2.31±.036 \| 2.32±.036 \| 2.36±.037 \|	\| DNN-IPD \| 0.48±.009 \| 0.48±.009 \| 0.48±.009 \| \| --- \| --- \| --- \| --- \| \| Linear-IPD \| 0.40±.008 \| 0.40±.008 \| 0.41±.008 \|	1
\| \| 30dB \| 20dB \| 10dB \| \| --- \| --- \| --- \| --- \| \| DNN-ILD \| 3.03±.046 \| 3.03±.046 \| 3.05±.046 \| \| Linear-ILD \| 2.31±.036 \| 2.32±.036 \| 2.36±.037 \|	\| DNN-IPD \| 0.46±.008 \| 0.48±.009 \| 0.50±.009 \| 0.54±.009 \| \| --- \| --- \| --- \| --- \| --- \| \| Linear-IPD \| 0.17±.005 \| 0.38±.008 \| 0.53±.009 \| 0.57±.010 \|	0
\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)115 \| 0.7 \|	\| (S,S)511 \| 0.6 \| \| --- \| --- \| \| (S,S)1223 \| 0.3 \| \| (S,S)2312 \| 0.1 \|	1
\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)115 \| 0.7 \|	\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)835 \| 0.6 \| \| (S,S)358 \| 0.2 \| \| (S,S)1013 \| 0.7 \| \| (S,S)1310 \| 0.3 \|	0
\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)115 \| 0.7 \| \| (S,S)511 \| 0.6 \|	\| (S,S)1223 \| 0.3 \| \| --- \| --- \| \| (S,S)2312 \| 0.1 \|	1
\| Link \| Thedegreeofdependence \| \| --- \| --- \| \| (S,S)115 \| 0.7 \| \| (S,S)511 \| 0.6 \|	\| (S,S)358 \| 0.2 \| \| --- \| --- \| \| (S,S)1013 \| 0.7 \| \| (S,S)1310 \| 0.3 \|	0
\| Rule1 \| Rule2 \| Rule3 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IG \| CC \| IG \| CC \| IG \| CC \| \| \| Age \| 0 \| 0.04 \| 0 \| 0 \| 0 \| -0.10 \| \| Gender \| 0.06 \| 0.14 \| 0.06 \| 0.13 \| 0 \| -0.06 \|	\| Job \| 0.08 \| -0.18 \| 0.08 \| -0.17 \| 0.04 \| 0.11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Studies \| 0 \| 0.18 \| 0 \| 0.16 \| 0 \| 0.017 \| \| Freq.ofuse \| 0.03 \| 0.05 \| 0.06 \| 0.08 \| 0.02 \| -0.07 \| \| #Accounts \| 0 \| 0.14 \| 0 \| 0.16 \| 0 \| -0.05 \| \| Priv.Concern \| 0 \| 0.13 \| 0 \| 0.16 \| 0 \| 0.04 \|	1
\| Rule1 \| Rule2 \| Rule3 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IG \| CC \| IG \| CC \| IG \| CC \| \| \| Age \| 0 \| 0.04 \| 0 \| 0 \| 0 \| -0.10 \| \| Gender \| 0.06 \| 0.14 \| 0.06 \| 0.13 \| 0 \| -0.06 \|	\| Group \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| 12 \| 13 \| rule \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| 77.80 \| 63.41 \| 51.43 \| 81.72 \| 81.49 \| 76.23 \| 81.41 \| 78.60 \| 81.49 \| 81.49 \| \| 2 \| 14.68 \| 10.87 \| 2.08 \| 16.18 \| 15.95 \| 14.68 \| 16.07 \| 16.07 \| 15.95 \| 15.95 \| \| 3 \| 65.94 \| 41.16 \| 33....	0
\| Rule1 \| Rule2 \| Rule3 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IG \| CC \| IG \| CC \| IG \| CC \| \| \| Age \| 0 \| 0.04 \| 0 \| 0 \| 0 \| -0.10 \| \| Gender \| 0.06 \| 0.14 \| 0.06 \| 0.13 \| 0 \| -0.06 \| \| Job \| 0.08 \| -0.18 \| 0.08 \| -0.17 \| 0.04 \| 0.11 \| \| Studies \| 0 \| 0.18 \| 0 \| 0.16 \| 0 \| 0.017 \|	\| Freq.ofuse \| 0.03 \| 0.05 \| 0.06 \| 0.08 \| 0.02 \| -0.07 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| #Accounts \| 0 \| 0.14 \| 0 \| 0.16 \| 0 \| -0.05 \| \| Priv.Concern \| 0 \| 0.13 \| 0 \| 0.16 \| 0 \| 0.04 \|	1
\| Rule1 \| Rule2 \| Rule3 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IG \| CC \| IG \| CC \| IG \| CC \| \| \| Age \| 0 \| 0.04 \| 0 \| 0 \| 0 \| -0.10 \| \| Gender \| 0.06 \| 0.14 \| 0.06 \| 0.13 \| 0 \| -0.06 \| \| Job \| 0.08 \| -0.18 \| 0.08 \| -0.17 \| 0.04 \| 0.11 \| \| Studies \| 0 \| 0.18 \| 0 \| 0.16 \| 0 \| 0.017 \|	\| 3 \| 65.94 \| 41.16 \| 33.62 \| 72.75 \| 72.75 \| 69.06 \| 68.98 \| 70.48 \| 74.17 \| 72.75 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 4 \| 73.27 \| 62.18 \| 48.71 \| 77.62 \| 77.62 \| 73.66 \| 77.23 \| 77.62 \| 77.62 \| 77.62 \| \| 5 \| 27.75 \| 18.77 \| 17.66 \| 28.86 \| 28.86 \| 21.39 \| 29.87 \| 28.86 \| 28.86 \| 28...	0
\| (30,5,24,2−2−2)<br>371816<br>(38,5,32,2−2−2)<br>4321186∗<br>(44,5,38,2−2−2−2)<br>5326216∗<br>(54,5,48,2−2−2−2)<br>5929236∗<br>(60,5,54,2−2−2−2)<br>6934266∗<br>(70,5,64,2−2−2−2)<br>7537286∗<br>(76,5,70,2−2−2−2)<br>8341306∗<br>(84,5,78,2−2−2−2)<br>8944326∗<br>(90,5,84,2−2−2−2)<br>974834326∗<br>(98,5,92,2−2−2−2−2) \| (36...	\| (38,7,30,2−2−2−2)<br>452220<br>(46,7,38,2−2−2)<br>512522218∗<br>(52,7,44,2−2−2−2−2)<br>572824238∗<br>(58,7,50,2−2−2−2−2)<br>633126258∗<br>(64,7,46,2−2−2−2−2)<br>693428278∗<br>(70,7,62,2−2−2−2−2)<br>7337308∗<br>(76,7,68,2−2−2−2)<br>8140328∗<br>(82,7,74,2−2−2−2)<br>8743348∗<br>(88,7,80,2−2−2−2)<br>9748378∗<br>(98,7,90,...	1
\| (30,5,24,2−2−2)<br>371816<br>(38,5,32,2−2−2)<br>4321186∗<br>(44,5,38,2−2−2−2)<br>5326216∗<br>(54,5,48,2−2−2−2)<br>5929236∗<br>(60,5,54,2−2−2−2)<br>6934266∗<br>(70,5,64,2−2−2−2)<br>7537286∗<br>(76,5,70,2−2−2−2)<br>8341306∗<br>(84,5,78,2−2−2−2)<br>8944326∗<br>(90,5,84,2−2−2−2)<br>974834326∗<br>(98,5,92,2−2−2−2−2) \| (36...	\| \| \| \| \| --- \| --- \| --- \| \| \| \| \| \| d1<br>d2<br>d3 \| 0,27×104480,25×10<br>291761203628673<br>666<br>0,50×100,45×100,30×10 \| 2,58×10752,58×10<br>66<br>4,12×103474,11×10<br>666<br>24,45×1019,62×103,46×10 \| \| total \| 0,80×100,46×100,58×10 \| 31,15×1019,62×1010,16×10 \| \| DBLP \| Netﬂix \| \| \| \| \| \| \| d1<br>d2<br>d...	0
\| (30,5,24,2−2−2)<br>371816<br>(38,5,32,2−2−2)<br>4321186∗<br>(44,5,38,2−2−2−2)<br>5326216∗<br>(54,5,48,2−2−2−2)<br>5929236∗<br>(60,5,54,2−2−2−2)<br>6934266∗<br>(70,5,64,2−2−2−2)<br>7537286∗<br>(76,5,70,2−2−2−2)<br>8341306∗<br>(84,5,78,2−2−2−2)<br>8944326∗<br>(90,5,84,2−2−2−2)<br>974834326∗<br>(98,5,92,2−2−2−2−2) \| (36...	\| (42,8,33,2−2−2−2)<br>49242221<br>(50,8,41,2−2−2−2)<br>5728259∗<br>(58,8,49,2−2−2−2)<br>67332529<br>(68,8,59,2−2−2−2)<br>7135309∗<br>(72,8,63,2−2−2−2)<br>7738329∗<br>(78,8,69,2−2−2−2)<br>8743359∗<br>(88,8,79,2−2−2−2)<br>9346379∗<br>(94,8,85,2−2−2−2)<br>9949399∗<br>(100,8,91,2−2−2−2) \| (44,8,35,2−2−2)<br>512523<br>(52,...	1
\| (30,5,24,2−2−2)<br>371816<br>(38,5,32,2−2−2)<br>4321186∗<br>(44,5,38,2−2−2−2)<br>5326216∗<br>(54,5,48,2−2−2−2)<br>5929236∗<br>(60,5,54,2−2−2−2)<br>6934266∗<br>(70,5,64,2−2−2−2)<br>7537286∗<br>(76,5,70,2−2−2−2)<br>8341306∗<br>(84,5,78,2−2−2−2)<br>8944326∗<br>(90,5,84,2−2−2−2)<br>974834326∗<br>(98,5,92,2−2−2−2−2) \| (36...	\| d1<br>d2<br>d3 \| 0,79×102370,55×10<br>66<br>2,62×10424070,97×10<br>666<br>3,27×101,61×101,61×10 \| 15,54×1025612,47×10<br>966<br>0,19×100,14×1020,30×10<br>966<br>0,20×1044,52×100,92×10 \| \| --- \| --- \| --- \| \| total \| 6,68×101,61×102,13×10 \| 0,39×1044,66×1033,69×10 \|	0
\| \| AverageReward \| TotalReward \| K-DDiﬀerence \| \| --- \| --- \| --- \| --- \| \| Game1 \| 0.77 \| 155.68 \| 11 \| \| Game2 \| 0.74 \| 149.38 \| 8 \| \| Game3 \| 0.84 \| 168.57 \| 15 \|	\| Game4 \| 0.61 \| 122.12 \| -8 \| \| --- \| --- \| --- \| --- \| \| Game5 \| 0.97 \| 194.40 \| 37 \|	1
\| \| AverageReward \| TotalReward \| K-DDiﬀerence \| \| --- \| --- \| --- \| --- \| \| Game1 \| 0.77 \| 155.68 \| 11 \| \| Game2 \| 0.74 \| 149.38 \| 8 \| \| Game3 \| 0.84 \| 168.57 \| 15 \|	\| MeanRank \| \| \| \| --- \| --- \| --- \| \| 210 \| 119 \| 48.5 \| \| 212 \| 87 \| 45.7 \| \| 167 \| 39 \| 51.7 \| \| 200 \| 113 \| 44.3 \| \| 181 \| 93 \| 49.6 \| \| 213 \| 113 \| 52.0 \| \| 188 \| 85 \| 53.5 \| \| MeanRank \| \| \| \| 263 \| 251 \| 75.4 \| \| 401 \| 338 \| 73.0 \| \| 312 \| 193 \| 81.3 \| \| 168 \| 156 \| 81.2 \|	0
\| \| AverageReward \| TotalReward \| K-DDiﬀerence \| \| --- \| --- \| --- \| --- \| \| Game1 \| 0.77 \| 155.68 \| 11 \|	\| Game2 \| 0.74 \| 149.38 \| 8 \| \| --- \| --- \| --- \| --- \| \| Game3 \| 0.84 \| 168.57 \| 15 \| \| Game4 \| 0.61 \| 122.12 \| -8 \| \| Game5 \| 0.97 \| 194.40 \| 37 \|	1
\| \| AverageReward \| TotalReward \| K-DDiﬀerence \| \| --- \| --- \| --- \| --- \| \| Game1 \| 0.77 \| 155.68 \| 11 \|	\| 167 \| 39 \| 51.7 \| \| --- \| --- \| --- \| \| 200 \| 113 \| 44.3 \| \| 181 \| 93 \| 49.6 \| \| 213 \| 113 \| 52.0 \| \| 188 \| 85 \| 53.5 \| \| MeanRank \| \| \| \| 263 \| 251 \| 75.4 \| \| 401 \| 338 \| 73.0 \| \| 312 \| 193 \| 81.3 \| \| 168 \| 156 \| 81.2 \|	0