A Virtual Face-off: Replaying the 2020 Livecoding Championship

November 25, 2020 — Jesse Friedman, Software Engineer, Engine Connectivity Engineering

In early October, by what at this point can only be a time-honored tradition, the Livecoding Championship returned in its fifth annual iteration as a special event during the 2020 Wolfram Technology Conference. As in preceding years, the championship offered top Wolfram Language programmers a chance to show off their knowledge, agility, typing speed and documentation-reading skills to an unfailingly adoring audience.

Follow along with this post by watching this recorded video of the 2020 Livecoding Championship livestream!

The virtual setting of this year’s conference posed unique challenges for the competition, but our team took this as an opportunity to try a slightly different format for the event. Past competitors—and readers of last year’s recap blog—will remember that previous years’ questions were rigidly structured, with the judges expecting to see contestants’ code produce a specific, expected output for each. This year’s questions were much more open-ended and in many cases left purposely open to interpretation, allowing contestants to express both their creativity and humor alike. Our lively livestream audience was enlisted to judge, voting in real time on their favorite solutions out of the top five received for each question.

Audience favorite Flip Phillips and veteran livecoding contestant Philip Maymin reprised their roles as nominally overlapping MCs from the Wolfram Summer School competition earlier this year. Flip cohosted the 2019 event as well. This illustrious and industrious duo was also accorded the dubious privilege of question-writing, and I’m told they are still recovering.

In this post, I’ll walk our blog audience through this year’s competition questions and the top solutions received for each.

Question 1: “I Do Not Think It Means What You Think It Means” »

Create a depiction of a mole using any Wolfram Language visualization functionality (e.g. symbolic graphics, graph plotting, equation plotting, etc.).

There are numerous possible moles to choose from here—the SI unit of amount of substance, the mammal, the tunnel-boring machine and probably about

6.022×

23

10

more. Let’s see which moles our contestants went for.

First Place: Adereth

In[]:=

Grid[ImageData[ImageResize[Binarize@Import["https://assets.dragoart.com/images/101719_502/how-to-draw-a-mole-for-kids-step-5_5e4c9ff3b14865.32843667_54930_3_3.gif"],50]],Spacings0]

Out[]=

Adereth chose the mammalian option, downloading a line drawing of a mole from a website and converting it to a 50-pixel-wide bit matrix. An intellectual property court might have considered this a derivative work, but the court of public opinion awarded Adereth first place.

Second Place (Tied): Daniele Ceravolo

In[]:=

WolframAlpha["mole curve"]

Out[]=

Input interpretation

The Mole-like curve (popular curve)

Plot	Hide axes Show ticks

t

(plotted for t from 0 to 92π)

Associated fictional character

The Mole

Equations

Parametric equations

x(t)

194

5

sint+

11

7

+

45

8

sin2t+

19

12

+

1

7

sin3t+

55

27

+

27

7

sin4t+

11

7

+

15

11

sin5t+

47

10

+

23

11

sin6t+

11

7

+

1

2

sin7t+

14

3

+

3

4

sin8t+

8

5

+

2

9

sin9t+

5

3

+

1

10

sin10t+

17

10

+

4

11

sin11t+

8

5

+

1

39

sin12t+

21

11

+

1

6

sin13t+

13

8

+

2

11

sin14t+

8

5

+

1

14

sin15t+

23

5

+

2

7

sin16t+

19

12

+

1

8

sin17t+

47

10

+

2

9

sin18t+

21

13

+

1

37

sin19t+

40

9

-

188

27

θ(91π-t)θ(t-87π)+-

2

11

sin

11

7

-6t-

23

11

sin

11

7

-2t+

124

15

sint+

11

7

+

9

5

sin3t+

11

7

+

11

8

sin4t+

11

7

+

14

11

sin5t+

11

7

-

4079

40

θ(87π-t)θ(t-83π)+-

5

13

sin

20

13

-10t-

5

8

sin

14

9

-8t-

5

4

sin

14

9

-6t-

1

19

sin

20

21

-5t-

10

9

sin

17

11

-4t-

29

2

sin

11

7

-t+

16

5

sin2t+

11

7

+

2

9

sin3t+

14

9

+

1

2

sin7t+

20

13

+

1

4

sin9t+

17

11

+

1

7

sin11t+

14

9

+

1

9

sin12t+

14

3

+

1

8

sin13t+

14

9

+

1

50

sin14t+

40

9

+

1

27

sin15t+

17

10

+

1

36

sin16t+

27

16

+

35

13

θ(83π-t)θ(t-79π)+-

2

9

sin

29

19

-22t-

1

6

sin

14

9

-21t-

1

3

sin

14

9

-19t-

1

9

sin

3

2

-18t-

1

3

sin

14

9

-15t-

1

15

sin

23

15

-14t-

2

9

sin

32

21

-12t-

7

12

sin

14

9

-11t-

5

16

sin

14

9

-9t-

9

11

sin

17

11

-8t-

1

8

sin

17

11

-7t-

24

7

sin

11

7

-2t+

236

15

sint+

11

7

+

19

7

sin3t+

11

7

+

10

7

sin4t+

11

7

+

19

11

sin5t+

11

7

+

11

7

sin6t+

19

12

+

2

3

sin10t+

8

5

+

1

10

sin13t+

5

3

+

3

13

sin16t+

8

5

+

1

11

sin17t+

47

10

+

1

22

sin20t+

22

13

+

1

47

sin23t+

9

2

-

1271

8

θ(79π-t)θ(t-75π)+-

11

17

sin

6

5

-40t-

17

14

sin

14

11

-35t-

11

12

sin

17

12

-31t-

3

7

sin

17

12

-29t-

3

2

sin

17

12

-28t-

1

6

sin

9

7

-27t-

25

13

sin

16

11

-26t-

15

7

sin

17

12

-22t-

49

15

sin

22

15

-19t-

11

9

sin

18

13

-18t-

19

8

sin

16

11

-15t-

164

11

sin

3

2

-10t-

239

16

sin

35

23

-9t-

37

4

sin

11

7

-7t-

71

8

sin

17

11

-4t-

122

7

sin

17

11

-3t-

194

11

sin

11

7

-2t+

389

3

sint+

11

7

+

218

31

sin5t+

13

8

+

43

6

sin6t+

23

14

+

124

25

sin8t+

23

14

+

75

19

sin11t+

22

13

+

25

11

sin12t+

19

11

+

11

5

sin13t+

7

4

+

27

5

sin14t+

28

17

+

12

7

sin16t+

8

5

+

7

10

sin17t+

47

10

+

13

9

sin20t+

19

11

+

1

8

sin21t+

29

7

+

1

6

sin23t+

13

8

+

3

5

sin24t+

25

12

+

21

13

sin25t+

9

5

+

4

7

sin30t+

15

8

+

3

13

sin32t+

21

11

+

14

11

sin33t+

13

7

+

1

35

sin34t+

4

5

+

1

8

sin36t+

12

7

+

1

28

sin37t+

21

5

+

1

21

sin38t+

17

4

+

9

19

sin39t+

11

6

+

2

11

sin41t+

17

11

+

5

6

sin42t+

24

13

+

1

56

sin43t+

23

8

-

491

9

θ(75π-t)θ(t-71π)+-

1

20

sin

29

19

-36t-

2

13

sin

14

9

-34t-

2

11

sin

3

2

-33t-

1

8

sin

3

2

-31t-

1

5

sin

17

11

-29t-

1

21

sin

13

9

-28t-

2

9

sin

14

9

-27t-

1

11

sin

17

12

-26t-

3

11

sin

14

9

-24t-

2

9

sin

14

9

-22t-

1

49

sin

9

7

-21t-

7

20

sin

29

19

-19t-

13

11

sin

14

9

-12t-

16

5

sin

14

9

-9t-

52

21

sin

14

9

-7t-

3

4

sin

14

9

-6t-

49

33

sin

11

7

-5t-

59

9

sin

11

7

-3t+

320

9

sint+

11

7

+

415

23

sin2t+

11

7

+

4

5

sin4t+

47

10

+

28

11

sin8t+

11

7

+sin10t+

11

7

+

3

4

sin11t+

19

12

+

4

11

sin13t+

14

9

+

3

10

sin14t+

8

5

+

2

11

sin15t+

19

12

+

1

15

sin16t+

11

7

+

10

13

sin17t+

33

7

+

1

10

sin18t+

20

13

+

1

11

sin20t+

16

11

+

3

13

sin23t+

8

5

+

1

7

sin25t+

8

5

+

1

18

sin30t+

20

13

+

1

8

sin32t+

17

11

+

1

26

sin35t+

5

3

+

1

16

sin37t+

18

11

+

1627

15

θ(71π-t)θ(t-67π)+-

1

31

sin

13

9

-24t-

1

16

sin

3

2

-23t-

1

36

sin

9

7

-22t-

3

10

sin

28

19

-18t-

1

5

sin

25

17

-17t-

5

16

sin

3

2

-14t-

2

9

sin

31

21

-13t-

2

5

sin

17

11

-12t-

1

34

sin

9

10

-11t-

2

9

sin

3

2

-10t-

12

23

sin

20

13

-9t-

5

12

sin

23

15

-8t-

2

13

sin

13

10

-7t-

45

13

sin

14

9

-6t-

13

12

sin

17

11

-5t-

76

25

sin

14

9

-3t-13sin

11

7

-2t+4sint+

11

7

+

7

4

sin4t+

11

7

+

3

11

sin15t+

13

8

+

1

7

sin16t+

17

10

+

1

9

sin19t+

13

8

+

1

19

sin20t+

7

4

+

47

11

θ(67π-t)θ(t-63π)+-

1

95

sin

7

12

-24t-

1

10

sin

25

17

-23t-

1

8

sin

28

19

-21t-

1

10

sin

3

2

-19t-

1

12

sin

3

2

-17t-

1

17

sin

32

21

-15t-

1

14

sin

17

12

-14t-

1

11

sin

14

9

-13t-

5

13

sin

17

11

-11t-

3

5

sin

14

9

-9t-

2

9

sin

11

8

-8t-

3

2

sin

17

11

-7t-

71

13

sin

14

9

-3t+

73

7

sint+

11

7

+

4

5

sin2t+

11

7

+

2

13

sin4t+

9

7

+

13

12

sin5t+

47

10

+

7

8

sin6t+

11

7

+

1

6

sin10t+

3

2

+

1

95

sin12t+

2

3

+

1

19

sin16t+

23

14

+

1

23

sin20t+

17

11

+

1

59

sin22t+

8

7

-

284

9

θ(63π-t)θ(t-59π)+-

2

9

sin

13

10

-42t-

1

7

sin

4

3

-38t-

1

8

sin

9

7

-37t-

1

8

sin

13

10

-35t-

1

9

sin

9

7

-34t-

1

9

sin

16

13

-32t-

1

3

sin

13

9

-29t-

1

5

sin

11

8

-28t-

1

8

sin

9

7

-26t-

3

5

sin

31

21

-25t-

2

5

sin

34

23

-23t-

18

13

sin

19

13

-21t-

13

10

sin

13

9

-18t-

2

3

sin

14

9

-17t-

2

5

sin

23

15

-15t-

1

4

sin

3

2

-14t-

13

9

sin

3

2

-13t-

9

10

sin

20

13

-11t-

58

25

sin

32

21

-10t-

13

10

sin

26

17

-7t-

69

13

sin

14

9

-6t-

34

11

sin

14

9

-5t-

95

7

sin

14

9

-4t-

31

9

sin

14

9

-2t+

197

6

sint+

11

7

+

87

22

sin3t+

8

5

+

7

3

sin8t+

13

8

+

23

24

sin9t+

13

8

+

6

11

sin12t+

23

14

+

5

7

sin16t+

13

8

+

1

5

sin19t+

29

15

+

2

5

sin20t+

13

8

+

4

11

sin22t+

12

7

+

4

9

sin24t+

28

17

+

1

9

sin27t+

29

15

+

1

37

sin30t+

17

8

+

1

15

sin31t+

19

12

+

1

4

sin33t+

9

5

+

2

9

sin36t+

16

9

+

1

8

sin39t+

19

11

+

1

11

sin41t+

13

7

-

677

13

θ(59π-t)θ(t-55π)+-

1

5

sin

5

9

-3t-

1

13

sin

3

8

-2t-

13

4

sin

1

34

-t+

2

7

sin4t+

11

10

+

1583

36

θ(55π-t)θ(t-51π)+

16

5

sint+

5

13

+

4

7

sin2t+

5

6

+

121

20

θ(51π-t)θ(t-47π)+-

9

19

sin

17

11

-27t-

1

11

sin

3

14

-16t-

17

8

sin

17

12

-10t-

94

9

sin

3

7

-2t+

463

12

sint+

85

42

+

37

4

sin3t+

9

2

+

59

17

sin4t+

59

13

+

20

11

sin5t+

68

15

+

52

11

sin6t+

37

11

+

20

11

sin7t+

42

11

+

17

9

sin8t+

40

11

+

7

12

sin9t+

35

17

+

15

7

sin11t+

11

16

+

14

11

sin12t+

27

11

+

3

8

sin13t+

15

4

+

10

13

sin14t+

39

10

+

7

10

sin15t+

27

8

+

5

16

sin17t+

25

9

+

1

5

sin18t+

33

7

+

5

9

sin19t+

42

11

+

3

7

sin20t+

1

2

+

1

9

sin21t+

23

17

+

1

5

sin22t+

3

8

+

1

8

sin23t+

37

8

+

5

16

sin24t+

8

5

+

2

7

sin25t+

31

9

+

2

9

sin26t+

12

5

+

2

9

sin28t+

7

5

-

332

9

θ(47π-t)θ(t-43π)+-

1

6

sin

5

14

-14t-

3

10

sin

3

7

-9t-

3

4

sin

7

5

-7t-

1

8

sin

13

14

-5t-

6

5

sin

1

3

-3t-

347

10

sin

63

62

-t+

35

17

sin2t+

4

7

+

15

11

sin4t+

13

7

+

1

5

sin6t+

37

36

+

2

9

sin8t+

15

8

+

3

8

sin10t+

21

8

+

1

11

sin11t+

4

11

+

2

7

sin12t+

34

9

+

1

10

sin13t+

23

7

+

1

12

sin15t+

5

9

+

1

17

sin(16t+4)+

1

9

sin17t+

42

13

-

2931

14

θ(43π-t)θ(t-39π)+-

3

11

sin

16

13

-17t-

3

10

sin

9

13

-13t-

109

15

sin

5

11

-4t+

754

11

sint+

16

7

+

130

7

sin2t+

3

4

+

39

19

sin3t+

23

5

+

41

10

sin5t+

14

15

+

33

14

sin6t+

8

9

+

29

9

sin7t+

18

11

+

89

30

sin8t+

32

11

+

3

2

sin9t+

9

2

+

6

7

sin10t+

4

9

+

5

4

sin11t+

7

3

+

13

11

sin12t+

35

9

+

7

8

sin14t+

17

9

+

11

10

sin15t+

35

11

+

9

13

sin16t+

213

53

+

1

4

sin18t+

41

12

+

1

8

sin19t+

18

17

+

3

5

sin20t+

43

13

+

3

14

sin21t+

40

9

-

1935

14

θ(39π-t)θ(t-35π)+-

41

8

sin

19

14

-4t-

7

2

sin

7

13

-3t-

156

11

sin

5

14

-2t+

432

7

sint+

27

7

+

30

13

sin5t+

27

10

+

1

2

sin6t+

16

7

+

16

13

sin7t+

3

5

+

20

21

sin8t+

27

8

+

5

6

sin9t+

28

11

+

5

3

sin10t+

13

7

+

2

5

sin11t+

29

15

+

5

16

sin12t+

15

14

+

27

26

sin13t+

29

12

+

5

9

sin14t+

11

8

+

4

9

sin15t+

11

21

+

7

13

sin16t+

29

9

+

4

9

sin17t+

9

14

+

2

5

sin18t+

29

11

+

5

13

sin19t+

8

15

+

1

3

sin20t+

23

6

+

1

13

sin21t+

29

10

+

3

10

sin22t+

40

11

+

190

3

θ(35π-t)θ(t-31π)+-

9

13

sin

6

7

-10t-2sin

13

12

-3t+

145

8

sint+

10

7

+

48

7

sin2t+

16

13

+

2

7

sin4t+

9

17

+

8

5

sin5t+

28

19

+

4

5

sin6t+

43

11

+

9

14

sin7t+

21

13

+

12

11

sin8t+

65

14

+

1

12

sin9t+

21

22

+

8

11

sin11t+

2

7

+

5

13

sin12t+

2

7

+

5

16

sin13t+

34

9

+

2

9

sin14t+

24

7

+

5

14

sin15t+

23

5

+

4

11

sin16t+

21

5

+

5

14

sin17t+

47

11

+

761

6

θ(31π-t)θ(t-27π)+-

32

19

sin

2

13

-6t+

121

12

sint+

51

19

+

174

35

sin2t+

76

17

+

20

9

sin3t+

53

13

+

29

13

sin4t+

29

19

+

17

6

sin5t+

37

19

+

3

13

sin7t+

13

8

+

1

3

sin8t+

345

86

+

8

11

sin9t+

5

3

+

7

8

sin10t+

23

7

+

1604

17

θ(27π-t)θ(t-23π)+-

9

11

sin

13

11

-11t-

63

5

sin

1

10

-3t+

29

2

sint+

91

30

+

1015

78

sin2t+

11

4

+

13

3

sin4t+

4

5

+

16

9

sin5t+

1

2

+

5

4

sin6t+

7

12

+

2

7

sin7t+

30

7

+

2

3

sin8t+

8

7

+

3

11

sin9t+

4

9

+

3

4

sin10t+

2

3

+

7

13

sin12t+

26

11

+

241

9

θ(23π-t)θ(t-19π)+-

3

4

sin

7

10

-3t-

113

14

sin

3

7

-t+

1

20

sin2t+

4

3

+

1

8

sin4t+

14

5

+

339

8

θ(19π-t)θ(t-15π)+-

1

4

sin

9

7

-32t-

1

23

sin

10

7

-28t-

2

5

sin

5

4

-26t-

13

12

sin

4

5

-11t-

163

11

sin

21

20

-2t+

76

17

sint+

38

11

+

123

7

sin3t+

15

8

+

83

15

sin4t+

73

37

+

8

3

sin5t+

11

10

+

29

13

sin6t+

27

13

+

32

21

sin7t+

23

10

+

6

5

sin8t+

1

10

+

5

6

sin9t+

7

8

+

3

11

sin10t+

46

13

+

19

13

sin12t+

13

6

+

7

8

sin13t+

17

10

+

6

11

sin14t+

17

5

+

8

11

sin15t+

8

9

+

2

5

sin16t+

15

7

+

5

9

sin17t+

2

9

+

4

15

sin18t+

13

5

+

6

11

sin19t+

1

2

+

10

13

sin20t+

31

8

+

9

14

sin21t+

23

13

+

3

7

sin22t+

54

13

+

1

8

sin23t+

13

5

+

1

12

sin24t+

65

16

+

1

5

sin25t+

9

7

+

1

4

sin27t+

5

4

+

1

12

sin29t+

19

7

+

1

6

sin30t+

46

11

+

1

5

sin31t+

19

9

+

2

11

sin33t+

35

13

+

1

8

sin34t+

5

11

-

164

3

θ(15π-t)θ(t-11π)+-

3

13

sin

5

7

-17t-

2

7

sin

7

10

-16t-

2

7

sin

17

11

-13t-

2

7

sin

3

8

-12t-

39

8

sin

7

5

-t+

178

11

sin2t+

16

11

+

104

23

sin3t+

10

7

+

15

4

sin4t+

9

2

+

9

11

sin5t+

5

2

+

69

34

sin6t+

50

11

+

6

5

sin7t+

5

3

+

17

11

sin8t+

23

5

+

3

5

sin9t+

29

10

+

17

13

sin10t+

17

4

+

3

11

sin11t+

31

10

+

4

11

sin14t+

21

5

+

7

15

sin15t+

38

9

-

749

13

θ(11π-t)θ(t-7π)+-

8

17

sin

5

4

-22t-

5

7

sin

5

11

-16t-

5

11

sin

1

7

-14t-2sin

1

2

-13t-

12

7

sin

1

10

-10t-

39

5

sin

5

4

-4t-

61

6

sin

9

7

-3t-

53

3

sin

19

14

-2t+

5

7

sin(19t)+

920

11

sint+

27

10

+

38

7

sin5t+

88

29

+

316

63

sin6t+

35

11

+

49

25

sin7t+

66

19

+

31

9

sin8t+

21

8

+

19

7

sin9t+

21

11

+

17

13

sin11t+

19

11

+

7

11

sin12t+

6

17

+

5

12

sin15t+

29

30

+

7

10

sin17t+

43

14

+

2

7

sin18t+

8

9

+

1

2

sin20t+

17

7

+

1

3

sin21t+

51

11

+

3

13

sin23t+

17

5

+

3

8

sin24t+

15

4

+

3

11

sin25t+

11

3

+

5

13

sin26t+

160

53

+

1

15

sin27t+

2

5

+

2

7

sin28t+

7

3

+

3

7

sin29t+

13

6

-

341

6

θ(7π-t)θ(t-3π)+-

11

10

sin

14

9

-11t-

7

9

sin

7

11

-10t-

11

17

sin

17

11

-7t-

6

7

sin

1

11

-6t-

19

14

sin

28

19

-5t-

53

18

sin

21

20

-4t+

679

15

sint+

20

9

+

31

3

sin2t+

22

5

+

31

11

sin3t+

37

12

+

20

9

sin8t+

27

8

+

7

6

sin9t+

19

8

+

6

5

sin12t+

28

9

+

7

9

sin13t+

9

5

+

2

5

sin14t+

20

9

+

3

7

sin15t+

15

8

+

3

13

sin16t+

33

14

-42θ(3π-t)θ(t+π)θ

sgnsin

t

2

y(t)-

1

13

sin

17

11

-19t-

1

7

sin

14

9

-17t-

1

5

sin

14

9

-15t-

1

9

sin

17

11

-13t-

1

12

sin

17

11

-12t-

1

17

sin

3

2

-10t-

1

7

sin

17

11

-9t-

7

9

sin

14

9

-7t-

31

16

sin

11

7

-5t-

23

6

sin

11

7

-3t-

7

10

sin

14

9

-2t+

39

10

sint+

11

7

+

20

21

sin4t+

14

9

+

7

9

sin6t+

14

9

+

2

11

sin8t+

14

9

+

1

34

sin11t+

13

9

+

1

20

sin14t+

37

25

+

1

9

sin16t+

14

9

+

1

15

sin18t+

14

9

+

769

9

θ(91π-t)θ(t-87π)+-

19

15

sin

11

7

-2t+

86

17

sint+

11

7

+

3

7

sin3t+

11

7

+

2

11

sin4t+

33

7

+

3

10

sin5t+

11

7

+

1

10

sin6t+

47

10

+

7839

49

θ(87π-t)θ(t-83π)+-

1

9

sin

10

7

-16t-

1

11

sin

13

9

-15t-

1

22

sin

11

8

-14t-

1

6

sin

28

19

-13t-

1

10

sin

10

7

-12t-

1

8

sin

7

5

-11t-

1

9

sin

7

5

-10t-

1

97

sin

2

7

-9t-

3

13

sin

14

9

-8t-

1

8

sin

20

13

-7t-

1

2

sin

14

9

-6t-

25

14

sin

17

11

-4t-

439

73

sin

14

9

-2t-

137

9

sin

11

7

-t+

1

4

sin3t+

4

3

+

1

2

sin5t+

29

19

+

1091

8

θ(83π-t)θ(t-79π)+-

6

19

sin

17

11

-21t-

1

5

sin

20

13

-17t-

1

4

sin

23

15

-16t-

1

6

sin

14

9

-15t-

4

13

sin

20

13

-11t-

6

17

sin

14

9

-10t-

5

7

sin

14

9

-7t-

16

13

sin

14

9

-6t+

107

7

sint+

11

7

+

5

6

sin2t+

19

12

+

38

9

sin3t+

11

7

+

7

10

sin4t+

33

7

+

5

8

sin5t+

11

7

+

11

23

sin8t+

8

5

+

10

9

sin9t+

19

12

+

1

13

sin12t+

14

9

+

1

42

sin13t+

13

7

+

3

11

sin14t+

8

5

+

1

16

sin18t+

5

3

+

1

7

sin19t+

18

11

+

1

9

sin22t+

19

12

+

1

13

sin23t+

14

3

+

330

7

θ(79π-t)θ(t-75π)+-

3

13

sin

13

10

-43t-

1

4

sin

7

5

-41t-

1

4

sin

5

4

-35t-

5

8

sin

19

14

-31t-

9

14

sin

14

9

-27t-

11

14

sin

7

5

-25t-

27

26

sin

4

3

-19t-3sin

3

2

-13t-

41

10

sin

25

17

-11t-

71

18

sin

10

7

-10t-

23

2

sin

32

21

-5t+

679

20

sint+

11

7

+

244

7

sin2t+

11

7

+

59

8

sin3t+

19

12

+

197

4

sin4t+

19

12

+

48

5

sin6t+

20

13

+

19

15

sin7t+

3

2

+

62

5

sin8t+

8

5

+

97

14

sin9t+

18

11

+

39

14

sin12t+

19

12

+

98

97

sin14t+

13

8

+

22

7

sin15t+

12

7

+

23

8

sin16t+

28

17

+

5

12

sin17t+

13

8

+

18

11

sin18t+

14

9

+

3

11

sin20t+

9

10

+

7

6

sin21t+

12

7

+

8

11

sin22t+

11

7

+

1

8

sin23t+

19

11

+

5

7

sin24t+

32

21

+

4

11

sin26t+

17

13

+

9

10

sin28t+

23

13

+

5

12

sin29t+

25

12

+

3

10

sin30t+

3

2

+

3

11

sin32t+

10

7

+

1

6

sin33t+

17

8

+

5

8

sin34t+

17

10

+

1

6

sin36t+

3

4

+

1

21

sin37t+

1

11

+

4

7

sin38t+

5

3

+

1

6

sin39t+

29

15

+

3

11

sin40t+

14

9

+

5

13

sin42t+

12

7

-

4705

21

θ(75π-t)θ(t-71π)+-

1

16

sin

3

2

-37t-

1

5

sin

14

9

-34t-

4

11

sin

14

9

-30t-

16

33

sin

17

11

-23t-

3

13

sin

14

9

-22t-

3

13

sin

11

7

-18t-

2

7

sin

11

7

-13t-

88

7

sin

11

7

-t+

73

7

sin2t+

11

7

+

185

11

sin3t+

11

7

+

12

7

sin4t+

8

5

+

5

2

sin5t+

19

12

+

11

4

sin6t+

19

12

+

6

11

sin7t+

47

10

+

10

19

sin8t+

8

5

+

7

9

sin9t+

19

12

+

1

3

sin10t+

33

7

+

25

13

sin11t+

11

7

+

5

8

sin12t+

47

10

+

3

7

sin14t+

19

12

+

7

15

sin15t+

8

5

+

5

11

sin16t+

33

7

+

1

4

sin17t+

21

13

+

30

31

sin19t+

19

12

+

1

12

sin20t+

33

7

+

1

8

sin21t+

28

19

+

2

9

sin24t+

11

7

+

1

40

sin25t+

13

8

+

1

4

sin26t+

8

5

+

1

13

sin27t+

11

7

+

1

10

sin28t+

75

16

+

1

19

sin29t+

23

15

+

2

9

sin31t+

11

7

+

1

9

sin32t+

61

13

+

2

11

sin33t+

8

5

+

1

37

sin35t+

16

9

+

1

22

sin36t+

8

5

+

96

7

θ(71π-t)θ(t-67π)+-

1

25

sin

15

11

-24t-

1

4

sin

29

19

-23t-

1

10

sin

16

11

-19t-

3

11

sin

25

17

-18t-

3

10

sin

3

2

-17t-

7

13

sin

35

23

-13t-

8

9

sin

3

2

-12t-

4

5

sin

20

13

-11t-

5

8

sin

3

2

-8t-

13

6

sin

17

11

-7t-

8

7

sin

20

13

-6t+

173

15

sint+

11

7

+

96

5

sin2t+

11

7

+

361

60

sin3t+

33

7

+

39

10

sin4t+

11

7

+

1

41

sin5t+

35

12

+

10

19

sin9t+

47

10

+

7

9

sin10t+

19

12

+

1

10

sin14t+

13

9

+

1

38

sin15t+

41

9

+

4

13

sin16t+

21

13

+

2

9

sin20t+

5

3

+

1

9

sin21t+

7

4

+

1

9

sin22t+

11

7

-

571

5

θ(67π-t)θ(t-63π)+-

1

26

sin

31

21

-24t-

1

37

sin

16

11

-23t-

1

96

sin

7

5

-20t-

1

21

sin

11

8

-19t-

1

11

sin

19

13

-15t-

1

11

sin

11

7

-10t-

1

7

sin

29

19

-8t-

4

11

sin

14

9

-4t+

28

9

sint+

47

10

+

152

9

sin2t+

11

7

+

13

7

sin3t+

21

13

+

3

8

sin6t+

13

8

+

4

7

sin7t+

13

8

+

1

6

sin9t+

3

2

+

1

13

sin12t+

14

9

+

1

16

sin13t+

19

11

+

1

21

sin14t+

12

7

+

1

12

sin16t+

19

12

+

1

24

sin18t+

17

10

-

614

5

θ(63π-t)θ(t-59π)+-

1

10

sin

11

8

-42t-

1

12

sin

11

8

-41t-

1

10

sin

17

12

-39t-

3

14

sin

13

9

-37t-

1

8

sin

11

7

-33t-

5

14

sin

17

12

-29t-

2

13

sin

14

11

-26t-

1

16

sin

5

6

-22t-

94

93

sin

16

11

-19t-

7

9

sin

19

13

-14t-

33

13

sin

20

13

-11t-

11

5

sin

17

11

-9t-

5

11

sin

3

2

-8t-

86

13

sin

20

13

-6t-

49

10

sin

17

11

-5t-

48

11

sin

17

11

-2t+

266

15

sint+

11

7

+

117

8

sin3t+

19

12

+

136

7

sin4t+

8

5

+

5

3

sin7t+

33

7

+

8

9

sin10t+

18

11

+

5

8

sin12t+

13

8

+

7

10

sin13t+

61

13

+

1

4

sin15t+

32

7

+

1

6

sin16t+

17

13

+

5

13

sin17t+

33

7

+

10

13

sin18t+

13

8

+

12

7

sin20t+

5

3

+

1

39

sin21t+

19

8

+

4

9

sin23t+

7

4

+

1

11

sin24t+

9

7

+

11

17

sin25t+

12

7

+

2

5

sin27t+

12

7

+

6

11

sin28t+

5

3

+

2

9

sin30t+

13

8

+

1

15

sin31t+

37

8

+

5

14

sin32t+

27

16

+

1

17

sin34t+

13

9

+

1

17

sin35t+

13

6

+

1

19

sin36t+

13

9

+

2

9

sin38t+

19

11

+

1

13

sin40t+

28

17

-

2859

22

θ(59π-t)θ(t-55π)+-

2

9

sin

7

15

-4t+

37

11

sint+

32

7

+

1

9

sin2t+

39

11

+

3

7

sin3t+

35

18

+

3954

59

θ(55π-t)θ(t-51π)+-

1

10

sin

8

11

-2t-

33

10

sin

23

17

-t+

875

13

θ(51π-t)θ(t-47π)+-

1

5

sin

8

7

-24t-

9

19

sin

1

2

-21t-

20

11

sin

38

25

-4t-

140

13

sin

17

18

-2t+

382

11

sint+

10

11

+

63

16

sin3t+

28

9

+

23

14

sin5t+

27

7

+

53

18

sin6t+

7

10

+

35

13

sin7t+

3

10

+

4

3

sin8t+

1

10

+

19

9

sin9t+

1

16

+

5

2

sin10t+

14

5

+

22

9

sin11t+

35

8

+

5

13

sin12t+

9

7

+

5

6

sin13t+

24

7

+

84

83

sin14t+

39

11

+

24

25

sin15t+

37

9

+

7

13

sin16t+

17

4

+

4

9

sin17t+

5

6

+

3

11

sin18t+

41

12

+

2

7

sin19t+

3

10

+

2

11

sin20t+

42

11

+

1

9

sin22t+

3

2

+

9

17

sin23t+

5

4

+

1

21

sin25t+

12

5

+

2

9

sin26t+

7

2

+

9

17

sin27t+

39

19

+

2

7

sin28t+

32

7

-

659

10

θ(47π-t)θ(t-43π)+-

3

14

sin

3

7

-12t-

1

5

sin

7

15

-11t-

20

13

sin

3

4

-5t-

8

11

sin

7

6

-4t-

23

8

sin

6

5

-3t+

249

13

sint+

7

2

+

167

21

sin2t+

37

16

+

32

33

sin6t+

16

9

+

12

25

sin(7t+1)+

7

13

sin8t+

12

5

+

1

7

sin9t+

21

5

+

1

3

sin10t+

15

4

+

1

12

sin13t+

1

8

+

2

13

sin14t+

15

8

+

1

14

sin15t+

11

23

+

1

9

sin16t+

33

14

+

1

8

sin17t+

3

11

-

1177

7

θ(43π-t)θ(t-39π)+-

1

14

sin

22

23

-21t-

6

17

sin

12

11

-20t-

2

9

sin

9

7

-18t-

4

9

sin

1

3

-17t-

2

7

sin

8

13

-15t-

27

13

sin

13

11

-5t-

35

9

sin

3

11

-4t+

729

10

sint+

37

9

+

148

7

sin2t+

35

17

+

94

11

sin3t+

41

9

+

17

6

sin6t+

6

11

+

95

94

sin7t+

8

5

+

3

7

sin8t+

32

7

+

5

4

sin9t+

2

9

+

3

5

sin10t+

29

11

+

8

9

sin11t+

14

3

+

8

11

sin12t+

1

2

+

9

11

sin13t+

24

11

+

10

21

sin14t+

43

12

+

4

9

sin16t+

52

15

+

1

5

sin19t+

43

11

-

379

6

θ(39π-t)θ(t-35π)+-

4

9

sin

9

13

-21t-

1

9

sin

12

11

-16t-

1

7

sin

5

7

-11t-

54

7

sin

7

9

-2t-

5116

93

sin

10

13

-t+

102

7

sin3t+

17

6

+

8

3

sin4t+

9

7

+

16

7

sin5t+

40

11

+

17

6

sin6t+

20

9

+

9

11

sin7t+

127

42

+

107

36

sin8t+

9

4

+sin9t+

32

9

+

3

2

sin10t+

41

20

+

30

31

sin12t+

27

11

+

14

11

sin13t+

81

41

+

1

7

sin14t+

47

12

+

1

3

sin15t+

22

9

+

3

8

sin17t+

25

17

+

2

11

sin18t+

10

11

+

1

3

sin19t+

34

9

+

2

7

sin20t+

19

11

+

1

6

sin22t+

37

8

-

529

13

θ(35π-t)θ(t-31π)+-

1

7

sin

9

10

-17t-

1

6

sin

2

7

-14t-

7

10

sin

3

8

-11t-

148

7

sin

5

7

-t+

68

7

sin2t+

2

5

+

59

11

sin3t+

48

11

+

20

7

sin4t+

13

7

+

23

12

sin5t+

1

20

+

6

11

sin6t+

5

3

+

7

9

sin7t+

23

10

+

2

3

sin8t+

15

8

+

3

4

sin9t+

29

7

+

4

15

sin10t+

5

7

+

3

10

sin12t+

64

63

+

1

4

sin13t+

11

6

+

6

17

sin15t+

25

6

+

1

9

sin16t+

11

16

+

255

7

θ(31π-t)θ(t-27π)+-

1

2

sin

17

12

-9t-

52

21

sin

1

5

-3t+

157

11

sint+

91

23

+

47

8

sin2t+

41

9

+

16

11

sin4t+

40

9

+

25

9

sin5t+

25

7

+

28

27

sin6t+

1

17

+

11

9

sin7t+

11

3

+

6

7

sin8t+

6

5

+

2

9

sin10t+

1

13

+

569

11

θ(27π-t)θ(t-23π)+-

3

10

sin

5

9

-10t-

7

20

sin

1

7

-7t-

31

7

sin

5

13

-4t-

751

50

sin

13

11

-t+

155

9

sin2t+

28

9

+

103

12

sin3t+

51

14

+

11

7

sin5t+

32

7

+

4

5

sin6t+

4

7

+

7

10

sin8t+

37

11

+

3

5

sin9t+

20

9

+

3

10

sin11t+

58

19

+

3

8

sin12t+

6

11

+

917

8

θ(23π-t)θ(t-19π)+

98

9

sint+

31

7

+

2

11

sin2t+

19

11

+

8

17

sin3t+

29

7

+

1

19

sin4t+

10

7

+

1230

11

θ(19π-t)θ(t-15π)+-

1

7

sin

5

7

-31t-

2

13

sin

4

13

-28t-

1

10

sin

1

6

-25t-

2

7

sin

11

12

-23t-

2

5

sin

1

55

-14t-

9

19

sin

9

10

-13t-

9

19

sin

1

14

-9t-

19

12

sin

35

23

-6t-

98

15

sin

32

33

-5t-

39

8

sin

17

12

-3t+

93

17

sint+

41

12

+

215

17

sin2t+

15

11

+

52

21

sin4t+

31

7

+

31

11

sin7t+

15

8

+

9

7

sin8t+

5

3

+

2

5

sin10t+

29

12

+

8

7

sin11t+

29

15

+

2

3

sin12t+

1

10

+

3

8

sin15t+

5

3

+

7

10

sin16t+

5

16

+

3

11

sin17t+

59

16

+

1

2

sin18t+

13

6

+

4

9

sin19t+

45

17

+

3

13

sin20t+

1

63

+

6

13

sin21t+

38

9

+

5

16

sin22t+

32

21

+

1

72

sin24t+

18

7

+

1

18

sin26t+

20

9

+

1

17

sin27t+

13

10

+

1

11

sin29t+

22

5

+

1

6

sin30t+

13

12

+

1

11

sin32t+

9

4

+

1

33

sin33t+

5

13

+

1

8

sin34t+

13

7

+

359

14

θ(15π-t)θ(t-11π)+-

7

13

sin

16

11

-10t-

11

17

sin

19

14

-9t-

7

15

sin

1

3

-8t-

7

9

sin

1

19

-7t-

16

5

sin

25

17

-5t+

50

7

sint+

47

10

+

7

15

sin2t+

11

4

+

11

7

sin3t+

11

9

+

7

5

sin4t+

4

3

+

3

2

sin6t+

39

40

+

1

21

sin11t+

7

12

+

1

10

sin12t+

28

9

+

2

5

sin13t+

37

9

+

1

3

sin14t+

50

17

+

3

11

sin15t+

7

2

+

1

4

sin16t+

79

40

+

1

7

sin17t+

50

13

+

799

8

θ(11π-t)θ(t-7π)+-

1

4

sin

1

9

-26t-

1

11

sin

9

7

-24t-

6

19

sin

3

8

-23t-

87

43

sin

3

7

-8t-

18

5

sin

1

5

-7t-

41

10

sin

2

5

-5t-

47

10

sin

6

5

-3t+

20

13

sin(11t)+

1284

17

sint+

57

13

+

17

3

sin2t+

19

11

+

215

24

sin4t+

9

11

+

67

22

sin6t+

43

11

+

4

15

sin9t+

14

5

+

4

5

sin10t+

19

11

+

8

5

sin12t+

41

12

+

9

13

sin13t+

18

13

+

5

12

sin14t+

46

15

+

4

5

sin15t+

45

11

+

2

5

sin16t+

34

11

+

11

15

sin17t+

1

8

+

3

8

sin18t+

24

7

+

1

12

sin19t+

1

6

+

5

13

sin20t+

7

13

+

1

6

sin21t+

2

5

+

5

12

sin(22t+2)+

4

13

sin25t+

20

13

+

3

13

sin27t+

35

8

+

2

9

sin28t+

23

24

+

1

7

sin29t+

1

2

+

811

13

θ(7π-t)θ(t-3π)+-

1

5

sin

9

7

-13t-

3

4

sin

7

11

-8t+

205

11

sint+

1

5

+

49

8

sin2t+

13

14

+

19

11

sin3t+

22

9

+

11

8

sin4t+

53

12

+

21

20

sin5t+

1

16

+

9

7

sin6t+

9

11

+

20

11

sin7t+

31

14

+

15

7

sin9t+

41

9

+

3

2

sin10t+

38

11

+

14

11

sin11t+

47

19

+

2

11

sin12t+

25

9

+

11

15

sin14t+

42

43

+

3

5

sin15t+

17

12

+

5

14

sin16t+

24

13

+

827

5

θ(3π-t)θ(t+π)θ

sgnsin

t

2

θ(x) is the Heaviside step function »

sgn(x) is the sign of x »

Properties

fictional character curves

|

League of SI Superheroes curves

At first glance, Daniele may appear simply to be querying the built-in popular curves data in Wolfram|Alpha for a parametric curve representing a superhero named “the Mole.” In fact, Daniele is querying the built-in popular curves data in Wolfram|Alpha for a parametric curve representing a superhero named “the Mole” created to represent the SI unit of amount of substance by a STEM outreach program of the US National Institute of Standards and Technology. Hence, presumably, the positive response by our always-knowledgeable livestream audience.

Second Place (Tied): Mike S

In[]:=

Out[]=

Mike S presents an intriguing machine learning–based solution, training a neural network to predict the color of each pixel in an image of a mole. The code Mike submitted is unfortunately incomplete, so we can’t see how the net was trained, but some amateur sleuthing turns up a suspiciously similar example from the documentation for the

NetTrain

function, demonstrated here with a different mole image.

Convert a test image into a training set, in which pixel positions

(x,y)

are mapped to color values

(r,g,b)

:

In[]:=

img=

desmans and moles

SPECIES SPECIFICATION



image



Out[]=

In[]:=

dims=Reverse@ImageDimensions[img];rules=Flatten@MapIndexed[(2(#2-1.)/(dims-1)-1.)#1&,ImageData@img,{2}];

In[]:=

RandomChoice[rules]

Out[]=

{-0.571429,-0.0738255}{0.188235,0.145098,0.0745098}

Create a network to predict the color based on pixel position:

In[]:=

chain=NetChain[{100,Ramp,250,Ramp,10,LogisticSigmoid,3}]

Out[]=

NetChain



uniniti

aliz

ed

Input port:	array
Output port:	vector (size: 3)



Train the network:

In[]:=

trained=NetTrain[chain,rules,MaxTrainingRounds100];

Use the network to predict the entire original image:

In[]:=

{h,v}=Range[-1,1,2./#]&/@(dims);coords=Tuples[{h,v}];Image[Partition[trained@coords,Length[v]]]

Out[]=

Second Place (Tied): HP2000

In[]:=

mol=MoleculePlot[Molecule["C"]]*6.02^23

Out[]=

8.52549×

17

10

HP2000 shows us a symbolic representation of (approximately) one mole of methane. It’s worth pointing out that a

Molecule

expression can be used for more than just generating pretty diagrams—a

MoleculeValue

can tell us that a mole of methane has a mass of about 16 grams, along with a host of other interesting properties of the molecule:

In[]:=

Molecule["C"]

Out[]=

Molecule

Formula: C

H

4

Atoms:

5

Bonds:

4



In[]:=

MoleculeValue[%,{"MolarMass","ElementMassFraction","Eccentricity"}]

Out[]=



16.043

g/mol

,

carbon



74.87

%

,

hydrogen



25.13

%

,0.00152979

Third Place: Total Annihilation

In[]:=

Plot[PDF[CauchyDistribution[0,1],x]^0.1,{x,-10,10},PlotRangeFull]

Out[]=

Total Annihilation captioned their solution “a cross-section of a skin tag.” No further elucidation is needed, I think.

Question 2: “The Most Ambiguous Word in the World” »

Find the English word or words that are the most “ambiguous”—and yes, this question is itself ambiguous!

Let’s see what our contestants came up with in response to this vaguely self-congratulatory question.

First Place: Total Annihilation

In[]:=

MaximalBy[WordData/@WordData["*","Lookup"],Length][[1,1,1]]

Out[]=

break

Total Annihilation’s solution quite reasonably takes “most ambiguous” to mean “greatest number of definitions.” And “break” indeed has a whopping 76 definitions:

In[]:=

WordData["break","Definitions"]//Length

Out[]=

76

Here’s just a sampling; check Wolfram|Alpha for the full list:

In[]:=

Multicolumn[RandomSample[WordData["break","Definitions"],20],2,BaseStyle{Magnification.7}]

Out[]=

{break,Verb,Impoverish}reduce to bankruptcy	{break,Verb,Outstrip}surpass in excellence
{break,Verb,Lick}find the solution or key to	{break,Noun,Occurrent}some abrupt occurrence that interrupts an ongoing activity
{break,Noun,OpenFrame}any frame in which a bowler fails to make a strike or spare	{break,Noun,Modification}an abrupt change in the tone or register of the voice (as at puberty or due to emotion)
{break,Noun,Crevice}(geology) a crack in the earth's crust resulting from the displacement of one side with respect to the other	{break,Verb,Appear}emerge from the surface of a body of water
{break,Noun,Hurt}breaking of hard tissue such as bone	{break,Noun,Interval}a time interval during which there is a temporary cessation of something
{break,Verb,Invalidate}invalidate by judicial action	{break,Verb,Penetrate}pierce or penetrate
{break,Verb,Modify}destroy the completeness of a set of related items	{break,Verb,Crumble}go to pieces
{break,Verb,ChangeVoice}change suddenly from one tone quality or register to another	{break,Verb,ChangeIntegrity}become separated into pieces or fragments
{break,Verb,Damage}render inoperable or ineffective	{break,Verb,Injure}fracture a bone of
{break,Verb,ChangeDirection}change directions suddenly	{break,Verb,Disrespect}act in disregard of laws, rules, contracts, or promises

Second Place: Nik Murzin

In[]:=

Select[WordList[],ContainsAll[Characters[#],Characters@"ambiguous"]&]

Out[]=

{ambiguous,ambiguously,bigamous,disambiguation,unambiguous,unambiguously}

Nik’s solution selects the English words that contain all the letters in the word “ambiguous.” Nik notes that their favorite of these is “bigamous”:

In[]:=

WordDefinition["bigamous"]

Out[]=

{of illegal marriage to a second person while legally married to a first}

Substituting

ContainsExactly

for

ContainsAll

, we can see that “bigamous” is also the only other word containing exactly the same letters as “ambiguous”:

In[]:=

Select[WordList[],ContainsExactly[Characters[#],Characters@"ambiguous"]&]

Out[]=

{ambiguous,bigamous}

Third Place: Wolfram Bob Repository

In[]:=

{AbsoluteTiming[TakeLargestBy[WordList[],Length[WordDefinition[#]]&,1]],ResourceFunction["BirdSay"]["Nice Answer Bob"]}

Out[]=

{5.50255,{break}},

Nice Answer Bob



The curiously named contestant “Wolfram Bob Repository” (known to friends as “WBR”) uses a similar interpretation to Total Annihilation, with a BirdSay thrown in for good measure. Why, then, is WBR’s solution more than twice as fast as Total Annihilation’s (repeated below)?

In[]:=

AbsoluteTiming[MaximalBy[WordData/@WordData["*","Lookup"],Length][[1,1,1]]]

Out[]=

{12.7702,break}

The answer lies in WBR’s obtaining a master list of words from the function

WordList

instead of

WordData

—the former is much smaller and is largely a subset of the latter:

In[]:=

Length/@{WordList[],WordData[]}

Out[]=

{39176,149191}

(Note that WordData[] is essentially equivalent to the form

WordData["*","Lookup"]

used by Total Annihilation.)

WordData contains many less-common words, particularly obscure compound words:

In[]:=

RandomSample[Complement[WordData[],WordList[]],20]

Out[]=

{radiochlorine,olive-colored,saliferous,Fraxinus,class Charophyceae,historical paper,Sauropterygia,ship's officer,disembarrassment,Farley maidenhair,GRAPO,FM,tongue fern,thick-branched,sciaenid fish,x-axis,immunochemistry,titre,Procrustean,Scourge of God}

Total Annihilation’s solution is much faster when iterating over the shorter WordList dictionary. In fact, it’s even faster than WBR’s version:

In[]:=

AbsoluteTiming[MaximalBy[WordData/@WordList[],Length][[1,1,1]]]

Out[]=

{3.69494,break}

This is because WBR uses the code

WordDefinition

["word"] to get a list of textual definitions for each word, while Total Annihilation uses WordData["word"]. The latter returns a list of word senses, each corresponding to a definition, and is about two times faster:

In[]:=

Short[WordDefinition["break"],3]//RepeatedTimingShort[WordData["break"],3]//RepeatedTiming

Out[]=

{0.00180876,{an escape from jail,any frame in which a bowler fails to make a strike or spare,a sudden dash,71,come to an end,interjection}}

Out[]=

{0.000887401,{{break,Noun,Flight},{break,Noun,OpenFrame},{break,Noun,Dash},71,{break,Verb,Finish},{break,Interjection}}}

WordData can be used to get the definition of a given word sense:

In[]:=

WordData[{"break","Verb","Ruin"},"Definitions"]

Out[]=

{{break,Verb,Ruin}cause the failure or ruin of}

Fourth Place (Tied): Hakan K

In[]:=

SemanticInterpretation["ambigous",AmbiguityFunctionAll]

Out[]=

AmbiguityList

ambiguous

,

ambiguous



Hakan K’s solution wittily makes use of the

AmbiguityFunction

option to the

Interpreter

and

SemanticInterpretation

functions. These functions interpret free-form text with natural language processing technology similar to that used by Wolfram|Alpha:

In[]:=

SemanticInterpretation["wolfram"]

Out[]=

Stephen Wolfram

The AmbiguityFunction option tells these functions how to handle multiple ambiguous results. The default setting is Automatic, which tells the function to simply return the first result. By specifying AmbiguityFunction  All, we ask instead for a list of all possible interpretations:

In[]:=

SemanticInterpretation["wolfram",AmbiguityFunctionAll]

Out[]=

AmbiguityList

Stephen Wolfram

,

tungsten

,

tungsten

,

wolfram.com

,

Wolfram Research

,

Wolfram

,

wolfram

,

Wolfram



In the case of the string “ambiguous,” we got two different results: an entity representing the word and another representing a MathWorld topic:

In[]:=

SemanticInterpretation["ambigous",AmbiguityFunctionAll]

Out[]=

AmbiguityList

ambiguous

,

ambiguous



In[]:=

%//InputForm

Out[]=

AmbiguityList[{Entity[Word, ambiguous],
Entity[MathWorld, Ambiguous]}]

Fourth Place (Tied): Mike S

In[]:=

wd=WordData[];First[ReverseSortBy[#Length[WordDefinition[#]]&/@wd,Last]]

Out[]=

break76

Mike S takes the same interpretation as Total Annihilation and WBR, with the inclusion of a helpful count of the number of definitions associated with the winning word.

Question 3: “30-for-30” »

Generate “30” using cellular automaton rule 30.

This question invites us to derive the number 30, or some variation thereof, from the rule 30 elementary cellular automaton.

First Place: Robert Mendelsohn

In[]:=

c=Flatten@DeleteCases[WebImageSearch["rule 30 shell","Images",MaxItems30],$Failed];ImageCollage[ConformImages[c[[1;;Length[c]]]]]

Out[]=

Robert uses the

WebImageSearch

function to obtain up to 30 images corresponding to the query “rule 30 shell” (in reference to the cellular automaton–like pigmentation patterns found on some mollusc shells). WebImageSearch curiously returned only seven distinct images here, not the 30 Robert requested, as can be seen more clearly with borders around each image:

In[]:=

ImageCollage[ImagePad[#,5,Red]&/@ConformImages[c[[1;;Length[c]]]]]

Out[]=

The voting audience seemed to agree that in competition, it’s the thought that counts.

Second Place: Wolfram Bob Repository

In[]:=

{Style["Click Me",40],Table[ResourceFunction["DynamicCellularAutomaton"][30,{i,30}],{i,30}]}

WBR uses the DynamicCellularAutomaton resource function to create 30 interactive plots of rule 30, each 30 columns wide and showing up to 30 rows. Each plot can be clicked to edit the initial conditions in the first row.

Third Place: Mike S

In[]:=

Total[CellularAutomaton[30,{0,0,0,1,0,0,0},9],3]

Out[]=

30

Mike evolves rule 30 on a fixed seven-column grid and finds that the number of black cells on the grid after nine steps totals to 30. Nice and simple!

In[]:=

CellularAutomaton[30,{0,0,0,1,0,0,0},9]//ArrayPlot

Out[]=

Fourth Place: Nik Murzin

In[]:=

ResourceFunction["LogoQRCode"]["thirty",Rasterize@ArrayPlot[CellularAutomaton[30,{{1},0},50]]]

Out[]=

Nik uses the LogoQRCode resource function to create a QR code for the string “thirty,” with the center of the barcode containing a plot of rule 30. Leave your phone in your pocket; we can easily verify the contents of the barcode with

BarcodeRecognize

:

In[]:=

BarcodeRecognize[%]

Out[]=

thirty

Fifth Place: HP2000

In[]:=

img=ArrayPlot[CellularAutomaton[30,{{1},0},30]];GeoGraphicsGeoStyling["OutlineMap",PatternFilling[img,{85,48}]],Polygon

United States

COUNTRY



Out[]=

HP2000 shows us a map of the United States, tiled over with array plots of rule 30. It’s unclear where “30” appears in this visualization, as there are fewer than 30 copies of the plot. Perhaps HP2000 intends the map to depict the US in the period from May 29, 1848, to September 9, 1850, during which the country consisted of 30 states.

Question 4: “WWWD: What Would Wolfram Do?” »

Write a function that will give the user advice on any yes/no question, based on both geolocation and current time.

The Wolfram Language does not yet have built-in divination functionality, so contestants were left to implement their own. Let’s see how they did.

First Place: Sander

In[]:=

SeedRandom[StringJoin[ToString/@{Here,Now}]];Row@{ResourceFunction["PlayingCardGraphic"][RandomInteger[{1,52}]]," which means ",RandomChoice[{"Yes","No"}]}

Out[]=

which means No

Sander seeds the Wolfram Language random number generator with the user’s current spatiotemporal coordinates (taking stock of the

Here

and

Now

, so to speak), draws a card (using the PlayingCardGraphic resource function) and associates it with a yes/no answer. A careful reader might notice that nothing in Sander’s code guarantees that each card will always be paired with the same yes/no value, and indeed, one can elicit a contradiction after a few dozen tries:

In[]:=

SeedRandom[StringJoin[ToString/@{Here,Now}]];Row@{ResourceFunction["PlayingCardGraphic"][RandomInteger[{1,52}]]," which means ",RandomChoice[{"Yes","No"}]}

Out[]=

which means Yes

But nothing in the question mandated that solutions produce rational output, and in my experience, oracles rarely do.

Second Place: Robert Mendelsohn

In[]:=

ListAnimate[Table[ImageRestyle[Rasterize[{DateString[],InputField["What do you need help with?"],InputField["Where are you located?"]}],wRasterize[{WebImageSearch["Starry Night"],Table[RandomWord[],{20}]}]],{w,0,1,0.2}]]

Out[]=

Robert’s solution is elaborate and mystifying. It starts by creating an image based on the current date and a pair of

InputField

expressions:

In[]:=

Rasterize[{DateString[],InputField["What do you need help with?"],InputField["Where are you located?"]}]

Out[]=

It’s unclear why Robert is using InputField here. They may have intended instead to use

Input

, in order to prompt the user with a dialog box:

In[]:=

img1=Rasterize[{DateString[],Input["What do you need help with?"],Input["Where are you located?"]}]

Out[]=

Robert then searches the web for some images of van Gogh’s The Starry Night, and picks 20 random dictionary words. These too are rasterized into a single image:

In[]:=

img2=Rasterize[{WebImageSearch["Starry Night"],Table[RandomWord[],{20}]}]

Out[]=

Finally, the

ImageRestyle

function is used to transfer the style of the second image (The Starry Night and the words) onto the first (date and user inputs) using machine learning. This is done six times, with different weighting each time, producing an eerie slideshow:

In[]:=

ListAnimate[Table[ImageRestyle[img1,wimg2,TargetDevice"GPU"],{w,0,1,0.2}]]

Out[]=

Interpretation of this result is an exercise left to the reader.

Third Place: Nik Murzin

In[]:=

If[Mod[Mean[First@Here]+DateValue[Now,"SecondExact"],2]<0,"Yes","No"]

Out[]=

No

Nik’s solution performs some arithmetic based on the user’s latitude and longitude and the number of fractional seconds elapsed in the current minute. The results are rather pessimistic:

In[]:=

Table[Block[{Now=FromAbsoluteTime[RandomReal[10^12]]},If[Mod[Mean[First@Here]+DateValue[Now,"SecondExact"],2]<0,"Yes","No"]],100]

Out[]=

{No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No,No}

Nik’s code has the test

Mod

[x,2]<0. The return value of Mod given these arguments is always non-negative, so the test will always return

False

:

In[]:=

Plot[Mod[x,2],{x,-5,5}]

Out[]=

Nik is well within his rights to make a philosophical statement here, but those in the glass-not-totally-empty crowd may wish to change the test to Mod[...]<1 for a more balanced output:

In[]:=

Table[Block[{Now=FromAbsoluteTime[RandomReal[10^12]]},If[Mod[Mean[First@Here]+DateValue[Now,"SecondExact"],2]<1,"Yes","No"]],100]%//Counts

Out[]=

{Yes,No,Yes,No,Yes,No,No,Yes,Yes,Yes,Yes,No,Yes,Yes,Yes,Yes,Yes,No,No,Yes,Yes,Yes,No,No,No,No,Yes,No,No,No,No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,No,No,Yes,Yes,No,Yes,No,Yes,Yes,No,Yes,No,Yes,Yes,Yes,Yes,No,No,No,No,No,No,Yes,Yes,Yes,No,No,No,No,Yes,No,Yes,Yes,No,Yes,Yes,Yes,No,Yes,No,Yes,No,No,No,No,Yes,Yes,Yes,No,Yes,Yes,No,Yes,Yes,Yes,Yes,No,No,No,No}

Out[]=

Yes51,No49

Out[]=

No

Fourth Place: Total Annihilation

In[]:=

If[EvenQ[Hash[{$GeoLocation,Now}]],"Yes","No"]

Out[]=

No

Total Annihilation’s solution succinctly calculates an integer hash of the user’s location and the current timestamp and checks whether the result is even:

In[]:=

Table[Block[{Now=FromAbsoluteTime[RandomReal[10^12]]},If[EvenQ[Hash[{$GeoLocation,Now}]],"Yes","No"]],10000]//Counts

Out[]=

Yes4954,No5046

Fifth Place: Adereth

In[]:=

{"As I see it, yes.","Ask again later.","Better not tell you now.","Cannot predict now.","Concentrate and ask again.","Don't count on it.","It is certain.","It is decidedly so."}[[1+Mod[Hash@{GeoLocation[],Now},8]]]

Out[]=

Don't count on it.

Adereth goes the extra mile and includes a set of Magic 8-Ball answers for some extra dimension. They use a similar approach to Total Annihilation, basing the result on a hash of the user’s location and the present time.

Question 5: “Eye Find the Settings Optical” »

Generate or replicate an optical illusion. These might get you started on getting an idea for your illusions: Wolfram|Alpha illusions search, the Illusions Index, eyetricks.net and Illusion of the Year.

First Place: Nik Murzin

In[]:=

animateCircle[n_]:=Animate[Graphics[Flatten@{Disk[],White,Map[(phase=#*2π/n;line={Cos[phase],Sin[phase]};{Line[{-line,line}],Disk[Sin[t+phase]*line,0.05]})&,Range[n]]},PlotRange{{-1.1,1.1},{-1.1,1.1}}],{t,0,2π}]animateCircle[15]

Out[]=

t

Nik’s illusion demonstrates cycloid motion, wherein a set of points moving in phased sinusoidal motion along the diameter lines of a large circle trace out a smaller circle. We can examine how the effect is constructed by changing the number of visible points:

In[]:=

Manipulate[With[{n=15},Animate[Graphics[Flatten@{Disk[],White,Map[(phase=#*2π/n;line={Cos[phase],Sin[phase]};{Line[{-line,line}],Disk[Sin[t+phase]*line,0.05]})&,Range[points]]},PlotRange{{-1.1,1.1},{-1.1,1.1}}],{t,0,2π}]],{points,1,15,1}]

Out[]=

points

t

Second Place: Robert Mendelsohn

In[]:=

ManipulateGraphics{If[layer1,{Rotate[Scale[Table[Rotate[{If[show,{c,Rectangle[{0,0},{.6,1}]},{}],{y,Scale[Disk[{0,.5},.3],{.9,1.67}]},{z,Scale[Disk[{.6,.5},.3],{.9,1.67}]}},x,{0.3,-3}],{x,0,2Pi,Pi/9}],1.78],0]},{}]},

,{Black,Disk[{0.3,-3},0.45]},{White,FontSize12,Text["A",{0.34,-3}]},Ifhide,{},{If[layer1,{Rotate[Scale[Table[Rotate[{If[show,{c,Rectangle[{15,0},{15.6,1}]},{}],{y,Scale[Disk[{15,.5},.3],{.9,1.67}]},{z,Scale[Disk[{15.6,.5},.3],{.9,1.67}]}},x,{15.3,-3}],{x,0,2Pi,Pi/9}],1.78],0]},{}]},

,{Black,Disk[{15.3,-3},0.45]},{White,FontSize12,Text["B",{15.38,-3}]},ImageSize600,PlotRange{{-7,23},{-11,5}},Row[{Control[{{y,Blue,"left color"},{Red,Orange,Yellow,Green,Blue,Purple}}],Spacer[30],Control[{{z,Yellow,"right color"},{Red,Orange,Yellow,Green,Blue,Purple}}],Spacer[30],Control[{{show,True,"show hourglass"},{True,False}}],Spacer[30],Control[{{c,Black,"hourglass color"},{Black,Gray,Brown,Red}}]}],Row[{Control[{{layer1,True,"show outside layer"},{True,False}}],Spacer[30],Control[{{layer2,True,"show next layer"},{True,False}}],Spacer[30],Control[{{hide,False,"hide second figure"},{True,False}}]}],ContentSize{620,340}

Out[]=

left color

right color

show hourglass

hourglass color

show outside layer

show next layer

hide second figure

Robert’s highly elaborate solution creates the “rotating snakes” illusion—stare at the center of one circle and the outer rings appear to move.

Third Place: Sander

In[]:=

Graphics[Table[{Pink,Disk[{x,y},0.4],Thickness[0.01],Black,Circle[{x,y},0.4,{(x+y)Pi/4,(x+y)Pi/4+Pi}],White,Circle[{x,y},0.4,{(x+y)Pi/4+Pi,(x+y)Pi/4+2Pi}]},{x,0,15},{y,0,15}],BackgroundDarker[Yellow,0.1]]

Out[]=

Sander’s solution is another peripheral drift illusion, similar to the rotating snakes.

Fourth Place: Adereth

In[]:=

With[{n=150},ArrayPlot[Table[Mod[i*j,n],{i,n},{j,n}],ColorFunction"Rainbow"]]

Out[]=

Adereth has produced a discrete Cartesian plot where the value of each point is the product of its coordinates modulo the plot range. Symmetry emerges when the function is plotted discretely in the integer domain, but a continuous plot reveals it as “illusory”:

In[]:=

With[{n=15},DensityPlot[Mod[i*j,n],{i,1,n},{j,1,n},ColorFunction"Rainbow",PlotLegendsAutomatic,Meshn-2,ExclusionsNone,PlotPoints100]]

Out[]=

This author notes some interesting patterns reminiscent of “magic eye” autostereograms to be had by changing

i×j

to

j

i

:

In[]:=

With[{n=150},Manipulate[ArrayPlot[Table[Mod[i^j,m],{i,n},{j,n}],ColorFunction"Rainbow",PlotLegendsAutomatic],{{m,n},1,n,1}]]

Out[]=

m

Fifth Place: Mike S

In[]:=

Manipulate[Graphics[{Black,Table[Rectangle[{x,y},{x+1,y+1}],{x,0,20,2},{y,0,19,2}],Table[Rectangle[{x+1,y+1},{x+2,y+2}],{x,0,19,2},{y,0,17,2}],GrayLevel[1-gl],,Table[{If[x2,{},Disk[{x+.15,y+.15},.15]],Disk[{x+.85,y+.85},.15]},{x,2,8,2},{y,4,8,2}],Table[{If[y2,{},Disk[{x+1.15,y+1.15},.15]],Disk[{x+If[y8,1.15,1.85],y+1.85},.15]},{x,2,8,2},{y,2,8,2}],Table[{Disk[{x+.15,y+If[x10,.15,.85]},.15],If[x18,{},Disk[{x+.85,y+.15},.15]]},{x,10,18,2},{y,4,8,2}],Table[{If[y2,{},Disk[{x+1.85,y+1.15},.15]],Disk[{x+If[y8,1.85,1.15],y+1.85},.15]},{x,10,17,2},{y,2,8,2}],Table[{Disk[{x+.85,y+.15},.15],If[x2,{},Disk[{x+.15,y+.85},.15]]},{x,2,8,2},{y,10,15,2}],Table[{If[y14,{},Disk[{x+1.15,y+1.85},.15]],Disk[{x+1.85,y+1.15},.15]},{x,2,8,2},{y,10,15,2}],Table[{Disk[{x+.15,y+If[x10,.85,.15]},.15],If[x18,{},Disk[{x+.85,y+.85},.15]]},{x,10,18,2},{y,10,15,2}],Table[{Disk[{x+1.15,y+1.15},.15],If[y14,{},Disk[{x+1.85,y+1.85},.15]]},{x,10,17,2},{y,10,15,2}],Table[{Disk[{x+1.85,8+1.5},.15]},{x,2,8,2}],Table[{Disk[{x+1.15,8+1.5},.15]},{x,10,16,2}]}/.If[figure"disks",{},Disk[c_,r_]Rectangle[c-r/2,c+r/2]],ImageSize480],{{gl,.1,"gray level"},1,0},{figure,{"disks","squares"}}]

Out[]=

gray level

figure

disks

squares

Mike’s solution is based on the code of the “warped squares” illusion from the Wolfram Demonstrations Project. The checkerboard squares appear to deform toward the center of the grid, but fading out the added disks with the “gray level” slider shows the grid to, in fact, be regular.

Question 6: “∀ Poetry: Rhyme || Reason” »

Generate any kind of structured poem. It doesn’t have to make sense, but it does have to follow the right form.

The Wolfram Function Repository contains some poetically leaning resource functions, like WordWeave and RandomEnglishHaiku, but our contestants were instructed to create their solution from scratch. Let’s see what they came up with.

First Place: Sander

In[]:=

w=WordData[];w=Select[w,StringLength[#]>5&];w=RandomSample/@GatherBy[w,StringTake[#,-3]&];w=RandomSample[w];w=Select[w,Length[#]>2&];{w[[1,1]],w[[2,1]],w[[1,2]],w[[2,2]]," ",w[[3,1]],w[[4,1]],w[[3,2]],w[[4,2]]," ",w[[5,1]],w[[6,1]],w[[5,2]],w[[6,2]]," ",w[[7,1]],w[[7,2]]}//Column

Out[]=

lallygag

Euopean hoopoe

sight gag

wood hoopoe

saddlebow

monochromat

down-bow

diplomat

rubbish heap

First of May

muckheap

queen of the May

frappe

genus Menippe

Sander generates a pseudo-Shakespearean sonnet, discarding the conventional syllabic scheme but retaining the rhyme pattern ABAB-CDCD-EFEF-GG. Determining whether two English words rhyme based solely on their spelling is a nontrivial task, but Sander’s quick-and-dirty approach to compare the final three letters of each word appears to work quite well in this case.

Second Place: Robert Mendelsohn

In[]:=

{Module[{poetrynotes=Table[RandomInteger[{1,24}],{17}]},EmitSound[SoundNote[#,1,"Piano"]&/@poetrynotes]],"A tone poem for you"}

Out[]=

{Null,A tone poem for you}

Robert creates a “tone poem” by stringing together random piano notes between C#4 and C6. The resulting composition may be the first piece in history eligible for a Pulitzer Prize in both music and poetry.

Third Place (Tied): Wolfram Bob Repository

In[]:=

With[{w=RandomChoice[WordList[],5]},Column[{StringRiffle[Flatten@{w,First[ResourceFunction["Rhymes"][#]]&/@w}],ResourceFunction["PartyParrot"]["Dab","SimpleAnimation"]}]]

Out[]=

playfellow depredation morals unilateralist profaneness go-slow corp mora trilateralist gameness

WBR describes this as a “poem with attitude.” The code picks five random words and uses the Rhymes resource function (by fellow contestant Mike S) to find other words that rhyme with each of the five original words. A distinctly hip parrot caps off the piece.

Third Place (Tied): Nik Murzin

In[]:=

Column@Table[With[{s=IntegerName@RandomInteger[10]},RandomChoice@ResourceFunction["Rhymes"]@s],10]

Out[]=

sloop of war

ticks

que

dmz

zeroes

question

myelosuppression

blue pea

ocean state

shticks

Nik creates an “integer poem” by finding rhymes for the English names of 10 random integers in the range

{0,10}

. This is easier to understand if we pair each number with its rhyme:

In[]:=

Grid@Table[With[{s=IntegerName@RandomInteger[10]},{s,RandomChoice@ResourceFunction["Rhymes"]@s}],10]

Out[]=

seven	evans
nine	gas line
five	backhand drive
five	chive
one	hyun
ten	setting hen
nine	forpine
two	pursue
four	cry out for
eight	stagflate

Third Place (Tied): Total Annihilation

In[]:=

rhymes=GroupBy[WordData[],Replace[WordData[#,"PhoneticForm"],s_StringLast[StringSplit[#,"'"]]]&];

In[]:=

With[{x=Reverse@SortBy[rhymes,Length]},Column@Riffle[x[[1,;;7]],x[[2,;;7]]]]

Part

:Cannot take positions 1 through 7 in {men's,pj's,where's}.

Out[]=

⋯1⋯

large output

show less

show all

set size limit...

Total Annihilation’s solution actually does not function as written, but the audience was sufficiently captivated by the unintentional poetry of the error message to award third place nevertheless. It appears that Total Annihilation was trying to group rhyming words by splitting their IPA phonetic spellings at the “ ˈ ” character, which indicates a stressed syllable. I’ve attempted to finish what Total Annihilation started:

In[]:=

phoneticForms=AssociationMap[WordData[#,"PhoneticForm"]&,WordData[]]//DeleteMissing;

In[]:=

rhymes=Keys/@GroupBy[phoneticForms/.Style[s_String,___]s,Last[StringSplit[#,"ˈ"]]&]

Out[]=

ə{a},ɒrdvˌɒrk{aardvark},æb{ab,cab,crab,dab,drab,fab,flab,gab,grab,jab,lab,nab,prefab,scab,slab,stab,tab},

⋯14270⋯

,oʊdiˌæk{zodiac},ɒmbi{zombie},ɛkˌoʊ{zydeco}

large output

show less

show all

set size limit...

In[]:=

With[{x=ReverseSortBy[rhymes,Length]},Column@Riffle[x[[1,;;7]],x[[2,;;7]]]]

Out[]=

abbreviation

absentee

abdication

addressee

aberration

adoptee

abomination

agree

abrogation

amputee

acceleration

appointee

acclimation

b

Notice that the words in this list follow an AB-AB rhyming pattern.

Question 7: “Same Difference” »

Compare two cities in different counties, states or countries (you can choose) whose names are different but that are both the same length as your username.

So begins the final question in the competition. Let’s see what criteria our contestants used to compare cities.

First Place: Kyleaf

In[]:=

Sort[CityData[#,"Population"]&/@CityData["London"]]

Out[]=



687

people

,

1048

people

,

1869

people

,

8126

people

,

10056

people

,

366151

people

,

8173941

people



Kyleaf searched the Wolfram Knowledgebase for cities named “London” and queried the population of each. Since Kyleaf used the

CityData

function to obtain a list of "City" entities, we can also ask for other properties, such as the country and administrative division each London is located in:

In[]:=

londons=CityData["London"];DeleteMissing/@EntityValue[londons,{"Country","AdministrativeDivision","Population"},"EntityAssociation"]//SortBy[Last]//Dataset

Out[]=

London	 Kiribati , 687 people 
London	 United States , Arkansas, United States , 1051 people 
London	 United States , California, United States , 1869 people 
London	 United States , Kentucky, United States , 8006 people 
London	 United States , Ohio, United States , 10138 people 
London	 Canada , Ontario, Canada , 383822 people 
London	 United Kingdom , Greater London, England, United Kingdom , 8673713 people 

Second Place: FJRA

In[]:=

allcities=CityData[];allnames=CityData[#,"Name"]&/@allcities;allpicks=Pick[allcities,StringLength[#]===StringLength["FJRA"]&/@allnames];cities=RandomChoice/@RandomChoice[RandomSample/@GroupBy[allpicks,#[[-1,-1]]&],2];GeoListPlot[cities,GeoLabelsAutomatic]

Out[]=

FJRA picks two "City" entities with four-letter names and plots their positions on a map with

GeoListPlot

. In order to satisfy the “different countries” constraint, FJRA takes advantage of the internal structure of the

Entity

expressions representing cities:

In[]:=

Soke

CITY

//InputForm

Out[]=

Entity[City, {Soke, Aydin, Turkey}]

Since the last element of the last argument to Entity indicates the country, FJRA can group the entities by this element and pick a city from each of two different entries in the resulting association, ensuring that the cities are in different countries:

In[]:=

grouped=GroupBy[allpicks,#[[-1,-1]]&];Short[#,.5]&/@grouped[[-5;;]]

Out[]=

SaintLucia

Choc

,Svalbard

Svea

,SouthKorea

Yŏsu

,

Kumi

,

Kuri

,

Osan

,

Asan

,

Wabu

,

Kŏje

,

Naju

,

Yŏju

,

Puyŏ

,

Puan

,

Inje

,

Paju

,Philippines

Imus

,

Lipa

,

Naga

,

Jolo

,

Daet

,

Bago

,

Mati

,

Bais

,

Naic

,

Bugo

,224,

Bato

,

Idio

,

Lono

,

Atok

,

Bara

,

Tara

,

Apud

,

Piña

,

Vigo

,UnitedKingdom

York

,

Bath

,

Hove

,

Bury

,

Sale

,

Hyde

,

Rhyl

,

Ryde

,

Leek

,

Ware

,22,

Pant

,

Elie

,

Maud

,

Croy

,

Brae

,

Stow

,

Yarm

,

Uley

,

Roos

,

Bray



In[]:=

RandomChoice/@RandomSample[grouped,2]

Out[]=

NewZealand

Ohai

,Macedonia

Stip



Third Place (Tied): HP2000

In[]:=

a=Cases[Table[RandomEntity["Country"]["Name"],100],n_/;StringLength[n]6];b=Entity["Country",#]&/@a;GeoGraphicsGeoPath

Champaign

CITY

,#&/@b,GeoProjection"Mercator"

Out[]=

HP2000 has plotted geodesic lines between Champaign, Illinois, and a set of six-letter countries. We can see the countries that were chosen in the a variable:

In[]:=

a

Out[]=

{Malawi,Angola,Rwanda,Uganda,Brazil,Brazil,Mexico,Serbia,Kuwait,Uganda,Belize,Kosovo,Taiwan,Taiwan,Turkey,Brazil,Angola,Uganda}

Note that Brazil and Taiwan both occur twice in the list. This is because HP2000 generated their list with one hundred independent invocations of

RandomEntity

. We could instead make a single call to RandomEntity using the second argument to indicate the number of entities we want to return:

In[]:=

Short[countryNames=RandomEntity["Country",100],.4]

Out[]=



Guernsey

,

Saint Pierre and Miquelon

,

Argentina

,

China

,

Thailand

,

Brunei

,

Northern Mariana Islands

,86,

Montenegro

,

Nepal

,

Guinea

,

Kazakhstan

,

Egypt

,

Venezuela

,

Lithuania



In[]:=

Cases[EntityValue[countryNames,"Name"],n_/;StringLength[n]6]

Out[]=

{Brunei,Kosovo,Bhutan,Brazil,Serbia,Rwanda,Jersey,Malawi,Kuwait,Israel,Greece,Sweden,Monaco,Norway,Guinea}

But there’s really no reason to restrict us to one hundred random countries out of the ~250 in the world. We can easily ask for the names of all available countries and filter that list:

In[]:=

Cases[EntityValue["Country","Name"],n_/;StringLength[n]6]

Out[]=

{Angola,Belize,Bhutan,Brazil,Brunei,Canada,Cyprus,France,Gambia,Greece,Guinea,Guyana,Israel,Jersey,Jordan,Kosovo,Kuwait,Latvia,Malawi,Mexico,Monaco,Norway,Panama,Poland,Russia,Rwanda,Serbia,Sweden,Taiwan,Turkey,Tuvalu,Uganda,Zambia}

We can even be declarative and define an entity class containing only the countries with six-letter names:

In[]:=

class=FilteredEntityClass["Country",EntityFunction[e,StringLength[e["Name"]]6]];countries=EntityList[class]

Out[]=



Angola

,

Belize

,

Bhutan

,

Brazil

,

Brunei

,

Canada

,

Cyprus

,

France

,

Gambia

,

Greece

,

Guinea

,

Guyana

,

Israel

,

Jersey

,

Jordan

,

Kosovo

,

Kuwait

,

Latvia

,

Malawi

,

Mexico

,

Monaco

,

Norway

,

Panama

,

Poland

,

Russia

,

Rwanda

,

Serbia

,

Sweden

,

Taiwan

,

Turkey

,

Tuvalu

,

Uganda

,

Zambia



Third Place (Tied): Daniel Martin

In[]:=

l=$Username//StringLength;c=CountryData[];c2=EntityValue[c,"Name"];c3=RandomChoice[Select[Transpose[{c,c2}],StringLength[#[[2]]]l&],2][[All,1]];SortBy[c3,Echo[Times@@ImageDimensions@Echo@EntityValue[#,"Flag"]]&]

»

10795

»

10880

Out[]=



Turkmenistan

,

Sint Maarten



Daniel has chosen a pair of 12-letter countries and is sorting them by the pixel count (

width×height

) of their flags. As it happens, the "Flag" property returns a vector

Graphics

object rather than an

Image

, so

ImageDimensions

simply returns the resolution at which the graphic will render in a notebook:

In[]:=

flag=EntityValue

Sint Maarten

COUNTRY

,"Flag"flag//Head

Out[]=

Graphics

In[]:=

Options[flag]//Lookup[ImageSize]ImageDimensions[flag]

Out[]=

{{128},{85}}

Out[]=

{128,85}

"FlagImage" can be used to obtain a raster image of the flag:

In[]:=

EntityValue

Sint Maarten

COUNTRY

,"FlagImage"%//Head

Out[]=

Image

Fourth Place: Sander

In[]:=

allcities=CityData[{All,"Netherlands"}];

In[]:=

allcities//Length

Out[]=

545

In[]:=

allcities[[-1]]["Name"]

Out[]=

Tongeren

In[]:=

allcities=CityData[{All,"Netherlands"}];allcities=Quiet[Select[allcities,StringLength[EntityValue[#,"Name"]]6&]][[;;2]];{allcities[[1]]["Association"],allcities[[2]]["Association"]}//Dataset

Out[]=

administrative region	Flevoland, Netherlands
number of aggravated assaults	—
rate of aggravated assault	—
aggregate home value	—
aggregate home value, householder 15 to 24 years	—
aggregate home value, householder 25 to 34 years	—
aggregate home value, householder 35 to 64 years	—
aggregate home value, householder 65 years and over	—
aggregate household income	—
airport codes	—
98 total ›
administrative region	Gelderland, Netherlands
number of aggravated assaults	—
rate of aggravated assault	—
aggregate home value	—
aggregate home value, householder 15 to 24 years	—
aggregate home value, householder 25 to 34 years	—
aggregate home value, householder 35 to 64 years	—
aggregate home value, householder 65 years and over	—
aggregate household income	—
airport codes	 Deelen 
98 total ›

Sander lists all the cities in the Netherlands and selects two with six-letter names. Sander then obtains an association containing all entity properties of each city. The result is a bit easier to read if we ask for only properties with non-missing values:

In[]:=

{allcities[[1]]["NonMissingPropertyAssociation"],allcities[[2]]["NonMissingPropertyAssociation"]}//Dataset

Out[]=

administrative region

Flevoland, Netherlands

coordinates

{52.367,5.2167}

country

Netherlands

elevation

-1.×

1

10

ft

entity classes



Netherlands

,

{{Flevoland, Netherlands}, AdministrativeDivision}

,

{{Almere, Flevoland, Netherlands}, AdministrativeDivision}

,

{Europe/Amsterdam, InternetTimeZone}

,

large population



has polygon?

False

latitude

52.367

°

longitude

5.2167

°

total magnetic field strength

49.3536µT

name

Almere

15 total ›

administrative region

Gelderland, Netherlands

airport codes



Deelen



coordinates

{51.99,5.91}

country

Netherlands

elevation

1.0×

2

10

ft

entity classes



Netherlands

,

{{Gelderland, Netherlands}, AdministrativeDivision}

,

{{Arnhem, Gelderland, Netherlands}, AdministrativeDivision}

,

{Europe/Amsterdam, InternetTimeZone}

,

large population



has polygon?

False

latitude

51.99

°

longitude

5.91

°

total magnetic field strength

49.2851µT

16 total ›

I bet you didn’t know that Flevoland’s geomagnetic field is 68.5 nanoteslas stronger than Gelderland’s!

And Now, the Final Results » After each question, the top five solutions were awarded points based on audience vote, with the first- through fifth-place solutions accruing four, three, two, one and one points, respectively. When all was said and done, the leaderboard looked like this:

Sander Huisman, a professor at the University of Twente in the Netherlands, kept his third-place position from last year. Nik Murzin, livecoding champion at the Wolfram Summer School event in July, cinched second place at this event. Finally, Robert Mendelsohn, first-time livecoding challenger, squeezed past the competition to land in the champion spot.

Congratulations to this year’s winners, and thanks to all of our contestants and live audience members for joining us in this exciting event. If you, the reader, are intrigued by any of these questions or think you can improve on a competitor’s solution, we’d love to see your approach in a post in Wolfram Community. See you at next year’s Livecoding Championship!

Special thanks to Scott Fischer for collating with uncannily quick speed all of the competition questions and the corresponding solutions from each contestant.

The 2020 Wolfram Technology Conference wasn’t quite the same, but it was still exciting and enjoyable with events like this one. Check out the Wolfram Innovator Awards, the One-Liner Competition and more.