SQuAD2.0

The Stanford Question Answering Dataset

What is SQuAD?

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.

New SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 new, unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuAD2.0 is a challenging natural language understanding task for existing models, and we release SQuAD2.0 to the community as the successor to SQuAD1.1. We are optimistic that this new dataset will encourage the development of reading comprehension systems that know what they don't know.

Explore SQuAD2.0 and model predictionsSQuAD2.0 paper (Rajpurkar & Jia et al. '18)

SQuAD 1.1, the previous version of the SQuAD dataset, contains 100,000+ question-answer pairs on 500+ articles.

Explore SQuAD1.1 and model predictionsSQuAD1.0 paper (Rajpurkar et al. '16)

Getting Started

We've built a few resources to help you get started with the dataset.

Download a copy of the dataset (distributed under the CC BY-SA 4.0 license):

To evaluate your models, we have also made available the evaluation script we will use for official evaluation, along with a sample prediction file that the script will take as input. To run the evaluation, use python evaluate-v2.0.py <path_to_dev-v2.0> <path_to_predictions>.

Once you have a built a model that works to your expectations on the dev set, you submit it to get official scores on the dev and a hidden test set. To preserve the integrity of test results, we do not release the test set to the public. Instead, we require you to submit your model so that we can run it on the test set for you. Here's a tutorial walking you through official evaluation of your model:

Submission Tutorial

Because SQuAD is an ongoing effort, we expect the dataset to evolve.

To keep up to date with major changes to the dataset, please subscribe:

Have Questions?

Ask us questions at our google group or at pranavsr@stanford.edu and robinjia@stanford.edu.

Star

Leaderboard

SQuAD2.0 tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. How will your system compare to humans on this task?

RankModelEMF1
Human Performance

Stanford University

(Rajpurkar & Jia et al. '18)		86.83189.452

1

Nov 16, 2018		AoA + DA + BERT (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

82.37485.310

2

Nov 16, 2018		AoA + DA + BERT (single model)

Joint Laboratory of HIT and iFLYTEK Research

81.17884.251

3

Nov 16, 2018		Candi-Net+BERT (single model)

42Maru NLP Team

80.10682.862

3

Nov 08, 2018		BERT (single model)

Google AI Language

80.00583.061

4

Nov 09, 2018		L6Net + BERT (single model)

Layer 6 AI

79.18182.259

5

Nov 06, 2018		SLQA+BERT (single model)

Alibaba DAMO NLP

http://www.aclweb.org/anthology/P18-1158		77.00380.209

6

Nov 08, 2018		BERT_base_aug (ensemble)

GammaLab

76.72179.611

7

Nov 05, 2018		MIR-MRC(F-Net) (single model)

Kangwon National University, Natural Language Processing Lab. & ForceWin, KP Lab.

74.79177.988

8

Sep 13, 2018		nlnet (single model)

Microsoft Research Asia

74.27277.052

9

Oct 12, 2018		YARCS (ensemble)

IBM Research AI

72.67075.507

10

Oct 13, 2018		RNANetSimple (ensemble)

Anonymous

72.58075.075

11

Sep 17, 2018		Unet (ensemble)

Fudan University & Liulishuo Lab

https://arxiv.org/abs/1810.06638		71.41774.869

11

Nov 12, 2018		BASE(single model)

Hithink RoyalFlush

71.49674.815

12

Aug 28, 2018		SLQA+ (single model)

Alibaba DAMO NLP

http://www.aclweb.org/anthology/P18-1158		71.46274.434

12

Aug 15, 2018		Reinforced Mnemonic Reader + Answer Verifier (single model)

NUDT

https://arxiv.org/abs/1808.05759		71.76774.295

12

Nov 14, 2018		BERT+Answer Verifier (single model)

Pingan Tech Olatop Lab

71.66675.457

13

Nov 15, 2018		PAML (ensemble)

GammaLab

71.24874.016

13

Sep 14, 2018		SAN (ensemble model)

Microsoft Business Applications AI Research

https://arxiv.org/abs/1712.03556		71.31673.704

14

Oct 13, 2018		RNANetSimple (single model)

Anonymous

70.71873.403

15

Sep 14, 2018		Unet (single model)

Fudan University & Liulishuo Lab

69.26272.642

15

Aug 21, 2018		FusionNet++ (ensemble)

Microsoft Business Applications Group AI Research

https://arxiv.org/abs/1711.07341		70.30072.484

15

Sep 26, 2018		Multi-Level Attention Fusion(MLAF) (single model)

Chonbuk National University, Cognitive Computing Lab.

69.47672.857

16

Aug 21, 2018		SAN (single model)

Microsoft Business Applications AI Research

https://arxiv.org/abs/1712.03556		68.65371.439

16

Aug 25, 2018		ARRR (single model)

anonymous

68.65371.124

16

Sep 13, 2018		BiDAF++ with pair2vec (single model)

UW and FAIR

68.02171.583

17

Jul 13, 2018		VS^3-NET (single model)

Kangwon National University in South Korea

67.89770.884

17

Jun 24, 2018		KACTEIL-MRC(GFN-Net) (single model)

Kangwon National University, Natural Language Processing Lab.

68.21370.878

18

Jun 25, 2018		KakaoNet2 (single model)

Kakao NLP Team

65.71969.381

19

Jul 11, 2018		abcNet (single model)

Fudan University & Liulishuo AI Lab

65.25669.206

19

Sep 13, 2018		BiDAF++ (single model)

UW and FAIR

65.65168.866

20

Jun 27, 2018		BSAE AddText (single model)

reciTAL.ai

63.33867.422

21

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		63.32766.633

21

May 30, 2018		BiDAF + Self Attention + ELMo (single model)

Allen Institute for Artificial Intelligence [modified by Stanford]

63.37266.251

22

May 30, 2018		BiDAF + Self Attention (single model)

Allen Institute for Artificial Intelligence [modified by Stanford]

59.33262.305

23

May 30, 2018		BiDAF-No-Answer (single model)

University of Washington [modified by Stanford]

59.17462.093

SQuAD1.1 Leaderboard

Since the release of SQuAD1.0, the community has made rapid progress, with the best models now rivaling human performance on the task. Here are the ExactMatch (EM) and F1 scores evaluated on the test set of v1.1.

RankModelEMF1
Human Performance

Stanford University

(Rajpurkar et al. '16)		82.30491.221

1

Oct 05, 2018		BERT (ensemble)

Google AI Language

https://arxiv.org/abs/1810.04805		87.43393.160

2

Oct 05, 2018		BERT (single model)

Google AI Language

https://arxiv.org/abs/1810.04805		85.08391.835

2

Sep 09, 2018		nlnet (ensemble)

Microsoft Research Asia

85.35691.202

2

Sep 26, 2018		nlnet (ensemble)

Microsoft Research Asia

85.95491.677

3

Jul 11, 2018		QANet (ensemble)

Google Brain & CMU

84.45490.490

4

Jul 08, 2018		r-net (ensemble)

Microsoft Research Asia

84.00390.147

5

Mar 19, 2018		QANet (ensemble)

Google Brain & CMU

83.87789.737

5

Sep 09, 2018		nlnet (single model)

Microsoft Research Asia

83.46890.133

5

Jun 20, 2018		MARS (ensemble)

YUANFUDAO research NLP

83.98289.796

6

Sep 01, 2018		MARS (single model)

YUANFUDAO research NLP

83.18589.547

7

Jan 03, 2018		r-net+ (ensemble)

Microsoft Research Asia

82.65088.493

7

May 09, 2018		MARS (single model)

YUANFUDAO research NLP

82.58788.880

7

Feb 19, 2018		Reinforced Mnemonic Reader + A2D (ensemble model)

Microsoft Research Asia & NUDT

82.84988.764

7

Jan 22, 2018		Hybrid AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

82.48289.281

7

Jun 20, 2018		QANet (single)

Google Brain & CMU

82.47189.306

7

Mar 06, 2018		QANet (ensemble)

Google Brain & CMU

82.74489.045

7

Jun 21, 2018		MARS (single model)

YUANFUDAO research NLP

83.12289.224

8

Jan 05, 2018		SLQA+ (ensemble)

Alibaba iDST NLP

82.44088.607

9

Feb 02, 2018		Reinforced Mnemonic Reader (ensemble model)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798		82.28388.533

9

Feb 27, 2018		QANet (single model)

Google Brain & CMU

82.20988.608

10

Dec 22, 2017		AttentionReader+ (ensemble)

Tencent DPDAC NLP

81.79088.163

11

May 09, 2018		Reinforced Mnemonic Reader + A2D (single model)

Microsoft Research Asia & NUDT

81.53888.130

11

Dec 17, 2017		r-net (ensemble)

Microsoft Research Asia

http://aka.ms/rnet		82.13688.126

12

May 09, 2018		Reinforced Mnemonic Reader + A2D + DA (single model)

Microsoft Research Asia & NUDT

81.40188.122

12

Apr 23, 2018		r-net (single model)

Microsoft Research Asia

81.39188.170

12

Apr 03, 2018		KACTEIL-MRC(GF-Net+) (ensemble)

Kangwon National University, Natural Language Processing Lab.

81.49687.557

12

Feb 27, 2018		QANet (single model)

Google Brain & CMU

80.92987.773

12

Nov 17, 2017		BiDAF + Self Attention + ELMo (ensemble)

Allen Institute for Artificial Intelligence

81.00387.432

12

Feb 19, 2018		Reinforced Mnemonic Reader + A2D (single model)

Microsoft Research Asia & NUDT

80.91987.492

13

Feb 12, 2018		Reinforced Mnemonic Reader + A2D (single model)

Microsoft Research Asia & NUDT

80.48987.454

13

Apr 12, 2018		AVIQA+ (ensemble)

aviqa team

80.61587.311

14

Mar 20, 2018		DNET (ensemble)

QA geeks

80.16486.721

14

Jan 22, 2018		Hybrid AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

80.02787.288

14

Jan 12, 2018		EAZI+ (ensemble)

Yiwise NLP Group

80.42686.912

14

Jan 13, 2018		SLQA+

single model

80.43687.021

14

Jan 04, 2018		{EAZI} (ensemble)

Yiwise NLP Group

80.43686.912

15

Feb 12, 2018		BiDAF + Self Attention + ELMo + A2D (single model)

Microsoft Research Asia & NUDT

79.99686.711

16

Jan 29, 2018		Reinforced Mnemonic Reader (single model)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798		79.54586.654

16

Apr 10, 2018		Unnamed submission by null

80.02786.612

16

Feb 23, 2018		MAMCN+ (single model)

Samsung Research

79.69286.727

16

Jan 03, 2018		r-net+ (single model)

Microsoft Research Asia

79.90186.536

16

Dec 28, 2017		SLQA+ (single model)

Alibaba iDST NLP

79.19986.590

16

Dec 05, 2017		SAN (ensemble model)

Microsoft Business AI Solutions Team

https://arxiv.org/abs/1712.03556		79.60886.496

17

Oct 17, 2017		Interactive AoA Reader+ (ensemble)

Joint Laboratory of HIT and iFLYTEK

79.08386.450

18

Oct 24, 2017		FusionNet (ensemble)

Microsoft Business AI Solutions Team

https://arxiv.org/abs/1711.07341		78.97886.016

18

Jun 01, 2018		MDReader

single model

79.03186.006

18

Feb 01, 2018		Unnamed submission by null

78.99986.151

19

Oct 24, 2018		WDNet (single model)

Beijing Normal University

78.92685.810

19

Oct 22, 2017		DCN+ (ensemble)

Salesforce Research

https://arxiv.org/abs/1711.00106		78.85285.996

20

Mar 29, 2018		KACTEIL-MRC(GF-Net+) (single model)

Kangwon National University, Natural Language Processing Lab.

78.66485.780

20

Nov 03, 2017		BiDAF + Self Attention + ELMo (single model)

Allen Institute for Artificial Intelligence

78.58085.833

21

May 09, 2018		KakaoNet (single model)

Kakao NLP Team

78.40185.724

22

Nov 30, 2017		SLQA(ensemble)

Alibaba iDST NLP

78.32885.682

22

Jan 02, 2018		Conductor-net (ensemble)

CMU

https://arxiv.org/abs/1710.10504		78.43385.517

22

Jun 01, 2018		MDReader0

single model

78.17185.543

22

Sep 18, 2018		BiDAF++ with pair2vec (single model)

UW and FAIR

78.22385.535

22

Jan 03, 2018		MEMEN (single model)

Zhejiang University

https://arxiv.org/abs/1707.09098		78.23485.344

22

Mar 19, 2018		aviqa (ensemble)

aviqa team

78.49685.469

23

Jan 29, 2018		test

single

78.08785.348

24

Jul 25, 2017		Interactive AoA Reader (ensemble)

Joint Laboratory of HIT and iFLYTEK Research

77.84585.297

25

Jan 10, 2018		Unnamed submission by null

77.43685.130

26

Dec 06, 2017		AttentionReader+ (single)

Tencent DPDAC NLP

77.34284.925

26

Sep 18, 2018		BiDAF++ (single model)

UW and FAIR

77.57384.858

26

Dec 13, 2017		RaSoR + TR + LM (single model)

Tel-Aviv University

https://arxiv.org/abs/1712.03609		77.58384.163

26

Apr 10, 2018		Unnamed submission by null

77.48984.735

26

Mar 20, 2018		DNET (single model)

QA geeks

77.64684.905

27

Nov 06, 2017		Conductor-net (ensemble)

CMU

https://arxiv.org/abs/1710.10504		76.99684.630

27

Sep 26, 2018		{gqa} (single model)

FAIR

77.09083.931

27

Dec 21, 2017		Jenga (ensemble)

Facebook AI Research

77.23784.466

27

Jan 23, 2018		MARS (single model)

YUANFUDAO research NLP

76.85984.739

28

Nov 01, 2017		SAN (single model)

Microsoft Business AI Solutions Team

https://arxiv.org/abs/1712.03556		76.82884.396

29

Oct 13, 2017		r-net (single model)

Microsoft Research Asia

http://aka.ms/rnet		76.46184.265

29

Dec 19, 2017		FRC (single model)

in review

76.24084.599

29

May 14, 2018		VS^3-NET (single model)

Kangwon National University in South Korea

76.77584.491

30

Oct 22, 2017		Conductor-net (ensemble)

CMU

76.14683.991

31

Sep 08, 2017		FusionNet (single model)

Microsoft Business AI Solutions team

https://arxiv.org/abs/1711.07341		75.96883.900

31

Oct 18, 2018		KAR (single model)

York University

https://arxiv.org/abs/1809.03449		76.12583.538

32

Jul 14, 2017		smarnet (ensemble)

Eigen Technology & Zhejiang University

75.98983.475

32

Oct 22, 2017		Interactive AoA Reader+ (single model)

Joint Laboratory of HIT and iFLYTEK

75.82183.843

32

Mar 15, 2018		AVIQA-v2 (single model)

aviqa team

75.92683.305

33

Oct 05, 2018		Unnamed submission by null

74.95083.294

33

Aug 18, 2017		RaSoR + TR (single model)

Tel-Aviv University

https://arxiv.org/abs/1712.03609		75.78983.261

34

Oct 23, 2017		DCN+ (single model)

Salesforce Research

https://arxiv.org/abs/1711.00106		75.08783.081

35

Feb 13, 2018		SSR-BiDAF

ensemble model

74.54182.477

35

Nov 01, 2017		Mixed model (ensemble)

Sean

75.26582.769

36

Jan 02, 2018		Conductor-net (single model)

CMU

https://arxiv.org/abs/1710.10504		74.40582.742

36

Nov 17, 2017		two-attention-self-attention (ensemble)

guotong1988

75.22382.716

36

May 21, 2017		MEMEN (ensemble)

Eigen Technology & Zhejiang University

https://arxiv.org/abs/1707.09098		75.37082.658

37

Mar 09, 2017		ReasoNet (ensemble)

MSR Redmond

https://arxiv.org/abs/1609.05284		75.03482.552

38

Aug 14, 2018		eeAttNet (single model)

BBD NLP Team

https://www.bbdservice.com		74.60482.501

38

Jul 10, 2017		DCN+ (single model)

Salesforce Research

https://arxiv.org/abs/1711.00106		74.86682.806

38

Feb 06, 2018		Jenga (single model)

Facebook AI Research

74.37382.845

38

Oct 27, 2017		Unnamed submission by null

74.48982.312

38

Oct 31, 2017		SLQA (single model)

Alibaba iDST NLP

74.48982.815

39

Jul 14, 2017		Mnemonic Reader (ensemble)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798		74.26882.371

40

Dec 23, 2017		S^3-Net (ensemble)

Kangwon National University in South Korea

74.12182.342

41

Jul 25, 2017		Interactive AoA Reader (single model)

Joint Laboratory of HIT and iFLYTEK Research

73.63981.931

41

Jul 29, 2017		SEDT (ensemble model)

CMU

https://arxiv.org/abs/1703.00572		74.09081.761

42

Dec 14, 2017		Jenga (single model)

Facebook AI Research

73.30381.754

42

Nov 06, 2017		Conductor-net (single)

CMU

https://arxiv.org/abs/1710.10504		73.24081.933

42

Apr 22, 2017		SEDT+BiDAF (ensemble)

CMU

https://arxiv.org/abs/1703.00572		73.72381.530

42

Feb 22, 2017		BiDAF (ensemble)

Allen Institute for AI & University of Washington

https://arxiv.org/abs/1611.01603		73.74481.525

42

Jan 24, 2017		Multi-Perspective Matching (ensemble)

IBM Research

https://arxiv.org/abs/1612.04211		73.76581.257

42

Jul 06, 2017		SSAE (ensemble)

Tsinghua University

74.08081.665

43

May 01, 2017		jNet (ensemble)

USTC & National Research Council Canada & York University

https://arxiv.org/abs/1703.04617		73.01081.517

44

Oct 22, 2017		Conductor-net (single)

CMU

72.59081.415

44

Apr 17, 2018		Unnamed submission by null

72.83180.622

44

Nov 16, 2017		two-attention-self-attention (single model)

guotong1988

72.60081.011

44

Apr 12, 2017		T-gating (ensemble)

Peking University

72.75881.001

44

Sep 20, 2017		BiDAF + Self Attention (single model)

Allen Institute for Artificial Intelligence

https://arxiv.org/abs/1710.10723		72.13981.048

45

Dec 15, 2017		S^3-Net (single model)

Kangwon National University in South Korea

71.90881.023

45

Apr 17, 2018		Unnamed submission by null

72.83180.622

46

Mar 03, 2018		AVIQA (single model)

aviqa team

72.48580.550

47

Nov 06, 2017		attention+self-attention (single model)

guotong1988

71.69880.462

48

Nov 01, 2016		Dynamic Coattention Networks (ensemble)

Salesforce Research

https://arxiv.org/abs/1611.01604		71.62580.383

49

Jul 14, 2017		smarnet (single model)

Eigen Technology & Zhejiang University

https://arxiv.org/abs/1710.02772		71.41580.160

49

Apr 13, 2017		QFASE

NUS

71.89879.989

50

Oct 27, 2017		M-NET (single)

UFL

71.01679.835

50

Apr 22, 2018		MAMCN (single model)

Samsung Research

70.98579.939

50

Jul 14, 2017		Mnemonic Reader (single model)

NUDT and Fudan University

https://arxiv.org/abs/1705.02798		70.99580.146

50

May 23, 2018		AttReader (single)

College of Computer & Information Science, SouthWest University, Chongqing, China

71.37379.725

50

Mar 24, 2017		jNet (single model)

USTC & National Research Council Canada & York University

https://arxiv.org/abs/1703.04617		70.60779.821

50

Apr 02, 2017		Ruminating Reader (single model)

New York University

https://arxiv.org/abs/1704.07415		70.63979.456

50

Mar 14, 2017		Document Reader (single model)

Facebook AI Research

https://arxiv.org/abs/1704.00051		70.73379.353

50

Dec 28, 2016		FastQAExt

German Research Center for Artificial Intelligence

https://arxiv.org/abs/1703.04816		70.84978.857

50

May 13, 2017		RaSoR (single model)

Google NY, Tel-Aviv University

https://arxiv.org/abs/1611.01436		70.84978.741

50

Mar 08, 2017		ReasoNet (single model)

MSR Redmond

https://arxiv.org/abs/1609.05284		70.55579.364

51

Apr 14, 2017		Multi-Perspective Matching (single model)

IBM Research

https://arxiv.org/abs/1612.04211		70.38778.784

52

Aug 30, 2017		SimpleBaseline (single model)

Technical University of Vienna

69.60078.236

52

Feb 05, 2018		SSR-BiDAF

single model

69.44378.358

53

Apr 12, 2017		SEDT+BiDAF (single model)

CMU

https://arxiv.org/abs/1703.00572		68.47877.971

54

Jun 25, 2017		PQMN (single model)

KAIST & AIBrain & Crosscert

68.33177.783

55

Apr 12, 2017		T-gating (single model)

Peking University

68.13277.569

56

Nov 28, 2016		BiDAF (single model)

Allen Institute for AI & University of Washington

https://arxiv.org/abs/1611.01603		67.97477.323

56

Feb 22, 2018		Unnamed submission by null

68.47877.220

57

Feb 22, 2018		Unnamed submission by null

68.42577.077

57

Dec 28, 2016		FastQA

German Research Center for Artificial Intelligence

https://arxiv.org/abs/1703.04816		68.43677.070

57

Jul 29, 2017		SEDT (single model)

CMU

https://arxiv.org/abs/1703.00572		68.16377.527

58

Oct 26, 2016		Match-LSTM with Ans-Ptr (Boundary) (ensemble)

Singapore Management University

https://arxiv.org/abs/1608.07905		67.90177.022

58

Jan 22, 2018		FABIR (Single Model)

in review

67.74477.605

59

Sep 19, 2017		AllenNLP BiDAF (single model)

Allen Institute for AI

http://allennlp.org/		67.61877.151

60

Feb 05, 2017		Iterative Co-attention Network

Fudan University

67.50276.786

61

Jan 03, 2018		newtest

single model

66.52775.787

61

Nov 01, 2016		Dynamic Coattention Networks (single model)

Salesforce Research

https://arxiv.org/abs/1611.01604		66.23375.896

62

Feb 24, 2018		Unnamed submission by null

65.99275.469

63

Jan 10, 2018		Unnamed submission by null

64.79674.272

64

Dec 09, 2017		Unnamed submission by ravioncodalab

64.43973.921

64

Oct 26, 2016		Match-LSTM with Bi-Ans-Ptr (Boundary)

Singapore Management University

https://arxiv.org/abs/1608.07905		64.74473.743

65

Feb 19, 2017		Attentive CNN context with LSTM

NLPR, CASIA

63.30673.463

66

Nov 02, 2016		Fine-Grained Gating

Carnegie Mellon University

https://arxiv.org/abs/1611.01724		62.44673.327

66

Sep 21, 2017		OTF dict+spelling (single)

University of Montreal

https://arxiv.org/abs/1706.00286		64.08373.056

67

Sep 21, 2017		OTF spelling (single)

University of Montreal

https://arxiv.org/abs/1706.00286		62.89772.016

68

Sep 21, 2017		OTF spelling+lemma (single)

University of Montreal

https://arxiv.org/abs/1706.00286		62.60471.968

69

Sep 28, 2016		Dynamic Chunk Reader

IBM

https://arxiv.org/abs/1610.09996		62.49970.956

70

Aug 27, 2016		Match-LSTM with Ans-Ptr (Boundary)

Singapore Management University

https://arxiv.org/abs/1608.07905		60.47470.695

71

Sep 11, 2018		Unnamed submission by Will_Wu

59.05869.436

72

Jan 10, 2018		Unnamed submission by null

58.76469.276

73

Aug 27, 2016		Match-LSTM with Ans-Ptr (Sentence)

Singapore Management University

https://arxiv.org/abs/1608.07905		54.50567.748

74

Oct 26, 2018		Unnamed submission by minjoon

52.53362.757