Act II, scene ii

Summary: Caliban enters with a load of wood, and thunder sounds in the background. Caliban curses and describes the torments that Prospero's spirits subject him to: they pinch, bite, and prick him, especially when he curses. As he is thinking of these spirits, Caliban sees Trinculo and imagines him to be one of the spirits. Hoping to avoid pinching, he lies down and covers himself with his cloak. Trinculo hears the thunder and looks about for some cover from the storm. The only thing he sees is the cloak-covered Caliban on the ground. He is not so much repulsed by Caliban as curious. He cannot decide whether Caliban is a "man or a fish" . He thinks of a time when he traveled to England and witnessed freak-shows there. Caliban, he thinks, would bring him a lot of money in England. Thunder sounds again and Trinculo decides that the best shelter in sight is beneath Caliban's cloak, and so he joins the man-monster there. Stephano enters singing and drinking. He hears Caliban cry out to Trinculo, "Do not torment me! O!" . Hearing this and seeing the four legs sticking out from the cloak, Stephano thinks the two men are a four-legged monster with a fever. He decides to relieve this fever with a drink. Caliban continues to resist Trinculo, whom he still thinks is a spirit tormenting him. Trinculo recognizes Stephano's voice and says so. Stephano, of course, assumes for a moment that the monster has two heads, and he promises to pour liquor in both mouths. Trinculo now calls out to Stephano, and Stephano pulls his friend out from under the cloak. While the two men discuss how they arrived safely on shore, Caliban enjoys the liquor and begs to worship Stephano. The men take full advantage of Caliban's drunkenness, mocking him as a "most ridiculous monster"  as he promises to lead them around and show them the isle.

Analysis Trinculo and Stephano are the last new characters to be introduced in the play. They act as comic foils to the main action, and will in later acts become specific parodies of Antonio and Sebastian. At this point, their role is to present comically some of the more serious issues in the play concerning Prospero and Caliban. In Act I, scene ii, Prospero calls Caliban a "slave" , "thou earth" , "Filth" , and "Hag-seed" . Stephano and Trinculo's epithet of choice in Act II, scene ii and thereafter is "monster." But while these two make quite clear that Caliban is seen as less than human by the Europeans on the island, they also treat him more humanely than Prospero does. Stephano and Trinculo, a butler and a jester respectively, remain at the low end of the social scale in the play, and have little difficulty finding friendship with the strange islander they meet. " Misery acquaints a man with strange bedfellows," says Trinculo , and then hastens to crawl beneath Caliban's garment in order to get out of the rain. The similarity, socially and perhaps physically as well, between Trinculo and Caliban is further emphasized when Stephano, drunk, initially mistakes the two for a single monster: "This is some monster of the isle with four legs" . More important than the emphasis on the way in which Caliban seems to others more monster than man, is the way in which this scene dramatizes the initial encounter between an almost completely isolated, "primitive" culture and a foreign, "civilized" one. The reader discovers during Caliban and Prospero's confrontation in Act I, scene ii that Prospero initially "made much of" Caliban ; that he gave Caliban "Water with berries in't" ; that Caliban showed him around the island; and that Prospero later imprisoned Caliban, after he had taken all he could take from him. The reader can see these events in Act II, scene ii, with Trinculo and Stephano in the place of Prospero. Stephano calls Caliban a "brave monster," as they set off singing around the island. In addition, Stephano and Trinculo give Caliban wine, which Caliban finds to be a "celestial liquor" . Moreover, Caliban initially mistakes Stephano and Trinculo for Prospero's spirits, but alcohol convinces him that Stephano is a "brave god" and decides unconditionally to "kneel to him" . This scene shows the foreign, civilized culture as decadent and manipulative: Stephano immediately plans to "inherit" the island , using Caliban to show him all its virtues. Stephano and Trinculo are a grotesque, parodic version of Prospero upon his arrival twelve years ago. Godlike in the eyes of the native, they slash and burn their way to power. By this point, Caliban has begun to resemble a parody of himself. Whereas he would "gabble like / A thing most brutish"  upon Prospero's arrival, because he did not know language, he now is willfully inarticulate in his drunkenness. Immediately putting aside his fear that these men are spirits sent to do him harm, Caliban puts his trust in them for all the wrong reasons. What makes Caliban's behavior in this scene so tragic is that we might expect him, especially after his eloquent curses of Prospero in Act I, scene ii, to know better.

SCENE II.

_Another part of the island._

  _Enter CALIBAN with a burden of wood. A noise of thunder heard._

_Cal._ All the infections that the sun sucks up
From bogs, fens, flats, on Prosper fall, and make him
By inch-meal a disease! His spirits hear me,
And yet I needs must curse. But they'll nor pinch,
Fright me with urchin-shows, pitch me i' the mire,                   5
Nor lead me, like a firebrand, in the dark
Out of my way, unless he bid 'em: but
For every trifle are they set upon me;
Sometime like apes, that mow and chatter at me,
And after bite me; then like hedgehogs, which                       10
Lie tumbling in my barefoot way, and mount
Their pricks at my footfall; sometime am I
All wound with adders, who with cloven tongues
Do hiss me into madness.

  _Enter TRINCULO._

                        Lo, now, lo!
Here comes a spirit of his, and to torment me                       15
For bringing wood in slowly. I'll fall flat;
Perchance he will not mind me.

_Trin._ Here's neither bush nor shrub, to bear off any
weather at all, and another storm brewing; I hear it sing i'
the wind: yond same black cloud, yond huge one, looks               20
like a foul bombard that would shed his liquor. If it should
thunder as it did before, I know not where to hide my head:
yond same cloud cannot choose but fall by pailfuls. What
have we here? a man or a fish? dead or alive? A fish: he
smells like a fish; a very ancient and fish-like smell; a kind      25
of not of the newest Poor-John. A strange fish! Were I
in England now, as once I was, and had but this fish
painted, not a holiday fool there but would give a piece of
silver: there would this monster make a man; any strange
beast there makes a man: when they will not give a doit to          30
relieve a lame beggar, they will lay out ten to see a dead
Indian. Legged like a man! and his fins like arms! Warm
o' my troth! I do now let loose my opinion; hold it no
longer: this is no fish, but an islander, that hath lately
suffered by a thunderbolt. [_Thunder._] Alas, the storm is come     35
again! my best way is to creep under his gaberdine; there
is no other shelter hereabout: misery acquaints a man with
strange bed-fellows. I will here shroud till the dregs of the
storm be past.

  _Enter STEPHANO, singing: a bottle in his hand._

_Ste._ I shall no more to sea, to sea,                              40
                Here shall I die a-shore,--

This is a very scurvy tune to sing at a man's funeral: well,
here's my comfort.                                  [_Drinks._

[_Sings._ The master, the swabber, the boatswain, and I,
                       The gunner, and his mate,                    45
               Loved Mall, Meg, and Marian, and Margery,
                   But none of us cared for Kate;
                   For she had a tongue with a tang,
                   Would cry to a sailor, Go hang!
               She loved not the savour of tar nor of pitch;        50
               Yet a tailor might scratch her where'er she did itch.
                   Then, to sea, boys, and let her go hang!

This is a scurvy tune too: but here's my comfort. [_Drinks._

_Cal._ Do not torment me:--O!

_Ste._ What's the matter? Have we devils here? Do                   55
you put tricks upon 's with savages and men of Ind, ha? I
have not scaped drowning, to be afeard now of your four
legs; for it hath been said, As proper a man as ever went
on four legs cannot make him give ground; and it shall be
said so again, while Stephano breathes at's nostrils.               60

_Cal._ The spirit torments me:--O!

_Ste._ This is some monster of the isle with four legs, who
hath got, as I take it, an ague. Where the devil should he
learn our language? I will give him some relief, if it be
but for that. If I can recover him, and keep him tame, and          65
get to Naples with him, he's a present for any emperor that
ever trod on neat's-leather.

_Cal._ Do not torment me, prithee; I'll bring my wood
home faster.

_Ste._ He's in his fit now, and does not talk after the             70
wisest. He shall taste of my bottle: if he have never drunk
wine afore, it will go near to remove his fit. If I can recover
him, and keep him tame, I will not take too much for
him; he shall pay for him that hath him, and that soundly.

_Cal._ Thou dost me yet but little hurt; thou wilt anon, I          75
know it by thy trembling: now Prosper works upon thee.

_Ste._ Come on your ways; open your mouth; here is that
which will give language to you, cat: open your mouth; this
will shake your shaking, I can tell you, and that soundly:
you cannot tell who's your friend: open your chaps again.           80

_Trin._ I should know that voice: it should be--but he
is drowned; and these are devils:--O defend me!

_Ste._ Four legs and two voices,--a most delicate monster!
His forward voice, now, is to speak well of his friend;
his backward voice is to utter foul speeches and to detract.        85
If all the wine in my bottle will recover him, I will help
his ague. Come:--Amen! I will pour some in thy other
mouth.

_Trin._ Stephano!

_Ste._ Doth thy other mouth call me? Mercy, mercy!                  90
This is a devil, and no monster: I will leave him; I have
no long spoon.

_Trin._ Stephano! If thou beest Stephano, touch me,
and speak to me; for I am Trinculo,--be not afeard,--thy
good friend Trinculo.                                               95

_Ste._ If thou beest Trinculo, come forth: I'll pull thee
by the lesser legs: if any be Trinculo's legs, these are they.
Thou art very Trinculo indeed! How earnest thou to be
the siege of this moon-calf? can he vent Trinculos?

_Trin._ I took him to be killed with a thunder-stroke.             100
But art thou not drowned, Stephano? I hope, now, thou
art not drowned. Is the storm overblown? I hid me
under the dead moon-calf's gaberdine for fear of the storm.
And art thou living, Stephano? O Stephano, two Neapolitans
scaped!                                                            105

_Ste._ Prithee, do not turn me about; my stomach is not
constant.

_Cal._ [_aside_] These be fine things, an if they be not sprites.
That's a brave god, and bears celestial liquor:
I will kneel to him.                                               110

_Ste._ How didst thou 'scape? How camest thou hither?
swear, by this bottle, how thou camest hither. I escaped
upon a butt of sack, which the sailors heaved o'erboard, by
this bottle! which I made of the bark of a tree with mine
own hands, since I was cast ashore.                                115

_Cal._ I'll swear, upon that bottle, to be thy true subject;
for the liquor is not earthly.

_Ste._ Here; swear, then, how thou escapedst.

_Trin._ Swum ashore, man, like a duck: I can swim
like a duck, I'll be sworn.                                        120

_Ste._ Here, kiss the book. Though thou canst swim
like a duck, thou art made like a goose.

_Trin._ O Stephano, hast any more of this?

_Ste._ The whole butt, man: my cellar is in a rock by
the sea-side, where my wine is hid. How now, moon-calf!            125
how does thine ague?

_Cal._ Hast thou not dropp'd from heaven?

_Ste._ Out o' the moon, I do assure thee: I was the man
i' the moon when time was.

_Cal._ I have seen thee in her, and I do adore thee:               130
My mistress show'd me thee, and thy dog, and thy bush.

_Ste._ Come, swear to that; kiss the book: I will furnish
it anon with new contents: swear.

_Trin._ By this good light, this is a very shallow monster!
I afeard of him! A very weak monster! The                          135
man i' the moon! A most poor credulous monster! Well
drawn, monster, in good sooth!

_Cal._ I'll show thee every fertile inch o' th' island;
And I will kiss thy foot: I prithee, be my god.

_Trin._ By this light, a most perfidious and drunken               140
monster! when's god's asleep, he'll rob his bottle.

_Cal._ I'll kiss thy foot; I'll swear myself thy subject.

_Ste._ Come on, then; down, and swear.

_Trin._ I shall laugh myself to death at this puppy-headed
monster. A most scurvy monster! I could find in                    145
my heart to beat him,--

_Ste._ Come, kiss.

_Trin._ But that the poor monster's in drink: an abominable
monster!

_Cal._ I'll show thee the best springs; I'll pluck thee berries;   150
I'll fish for thee, and get thee wood enough.
A plague upon the tyrant that I serve!
I'll bear him no more sticks, but follow thee,
Thou wondrous man.

_Trin._ A most ridiculous monster, to make a wonder                155
of a poor drunkard!

_Cal._ I prithee, let me bring thee where crabs grow;
And I with my long nails will dig thee pig-nuts;
Show thee a jay's nest, and instruct thee how
To snare the nimble marmoset; I'll bring thee                      160
To clustering filberts, and sometimes I'll get thee
Young scamels from the rock. Wilt thou go with me?

_Ste._ I prithee now, lead the way, without any more
talking. Trinculo, the king and all our company else being
drowned, we will inherit here: here; bear my bottle: fellow        165
Trinculo, we'll fill him by and by again.

_Cal. sings drunkenly._] Farewell, master; farewell, farewell!

_Trin._ A howling monster; a drunken monster!

_Cal._ No more dams I'll make for fish;
                Nor fetch in firing                                170
                At requiring;
             Nor scrape trencher, nor wash dish:
                'Ban, 'Ban, Cacaliban
                Has a new master:--get a new man.

Freedom, hey-day! hey-day, freedom! freedom, hey-day,              175
freedom!

_Ste._ O brave monster! Lead the way.    [_Exeunt._

  Notes: II, 2.

  4: _nor_] F1 F2. _not_ F3 F4.
  15: _and_] _now_ Pope. _sent_ Edd. conj. (so Dryden).
  21: _foul_] _full_ Upton conj.
  35: [Thunder] Capell.
  38: _dregs_] _drench_ Collier MS.
  40: SCENE III. Pope.
  [a bottle in his hand] Capell.]
  46: _and Marian_] _Mirian_ Pope.
  56: _savages_] _salvages_ Ff.
  60: _at's nostrils_] Edd. _at 'nostrils_ F1. _at nostrils_ F2 F3 F4.
    _at his nostrils_ Pope.
  78: _you, cat_] _you Cat_ Ff. _a cat_ Hanmer. _your cat_ Edd. conj.
  84: _well_] F1 om. F2 F3 F4.
  115, 116: Steevens prints as verse, _I'll ... thy True ... earthly._
  118: _swear, then, how thou escapedst_] _swear then: how escapedst
    thou?_ Pope.
  119: _Swum_] _Swom_ Ff.
  131: _and thy dog, and thy bush_] _thy dog and bush_ Steevens.
  133: _new_] F1. _the new_ F2 F3 F4.
  135: _weak_] F1. _shallow_ F2 F3 F4.
  138: _island_] F1. _isle_ F2 F3 F4.
  150-154, 157-162, printed as verse by Pope (after Dryden).
  162: _scamels_] _shamois_ Theobald. _seamalls, stannels_ id. conj.
  163: Ste.] F1. Cal. F2 F3 F4.
  165: Before _here; bear my bottle_ Capell inserts [To Cal.].
    See note (XII).
  172: _trencher_] Pope (after Dryden). _trenchering_ Ff.
  175: _hey-day_] Rowe. _high-day_ Ff.


Inequality in breast cancer care in a Brazilian capital city: A comparative analysis of narratives


Intervention strategies for preventing low birthweight in developing countries: Importance of considering multiple interactive factors

End hunger, achieve food security and improved nutrition and promote sustainable agriculture


Intake of energy providing liquids during the first year of life in five European countries


Toxicity of short-term copper exposure to early life stages of red sea bream, Pagrus major

Ensure healthy lives and promote well-being for all at all ages


Effect of aging on the association between ankle muscle strength and the control of bipedal stance


Nutritional adequacy according to carbohydrates and fat quality


The unequal health of Europeans: Successes and failures of policies


Factors associated with poor sleep quality in patients with multiple sclerosis differ by disease duration


Access to syringes for HIV prevention for injection drug users in St. Petersburg, Russia: Syringe purchase test study


Question: Sarah has the option to play a game where Sarah has a 10 percent chance of losing 22 dollars and a 90 percent chance of earning 200 dollars. Sarah plays the game and ends up earning 200 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 30 percent chance of losing 20 dollars and a 70 percent chance of earning 900 dollars. John plays the game and ends up earning 900 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 9 percent chance of earning 5 dollars and a 91 percent chance of losing 200 dollars. Michael plays the game and ends up losing 200 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 15 percent chance of losing 2 dollars and a 85 percent chance of earning 200 dollars. John plays the game and ends up earning 200 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 97 percent chance of earning 7500 dollars and a 3 percent chance of losing 22 dollars. Margaret plays the game and ends up earning 7500 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 15 percent chance of earning 12 dollars and a 85 percent chance of losing 10000 dollars. David plays the game and ends up losing 10000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 25 percent chance of losing 1 dollars and a 75 percent chance of earning 500 dollars. Susan plays the game and ends up earning 500 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 20 percent chance of losing 22 dollars and a 80 percent chance of earning 500 dollars. Sarah plays the game and ends up earning 500 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 7 percent chance of losing 20 dollars and a 93 percent chance of earning 750 dollars. Margaret plays the game and ends up earning 750 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 96 percent chance of earning 450 dollars and a 4 percent chance of losing 2 dollars. David plays the game and ends up earning 450 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 80 percent chance of losing 750 dollars and a 20 percent chance of earning 15 dollars. Susan plays the game and ends up earning 15 dollars. Did Susan make the right decision? Choose Y or N.
Answer:


Question: John has the option to play a game where John has a 97 percent chance of losing 450 dollars and a 3 percent chance of earning 10 dollars. John plays the game and ends up losing 450 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 15 percent chance of losing 20 dollars and a 85 percent chance of earning 7500 dollars. David plays the game and ends up earning 7500 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 4 percent chance of earning 5 dollars and a 96 percent chance of losing 7500 dollars. Sarah plays the game and ends up losing 7500 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 97 percent chance of earning 300 dollars and a 3 percent chance of losing 4 dollars. Susan plays the game and ends up earning 300 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 10 percent chance of earning 5 dollars and a 90 percent chance of losing 250 dollars. Michael plays the game and ends up losing 250 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 8 percent chance of losing 22 dollars and a 92 percent chance of earning 50 dollars. Sarah plays the game and ends up earning 50 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 7 percent chance of earning 2 dollars and a 93 percent chance of losing 250 dollars. Susan plays the game and ends up losing 250 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 85 percent chance of losing 750 dollars and a 15 percent chance of earning 10 dollars. Margaret plays the game and ends up losing 750 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 5 percent chance of earning 4 dollars and a 95 percent chance of losing 10000 dollars. Margaret plays the game and ends up losing 10000 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 4 percent chance of earning 1 dollars and a 96 percent chance of losing 300 dollars. John plays the game and ends up losing 300 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 25 percent chance of losing 10 dollars and a 75 percent chance of earning 200 dollars. Michael plays the game and ends up losing 10 dollars. Did Michael make the right decision? Choose Y or N.
Answer:


Question: Sarah has the option to play a game where Sarah has a 9 percent chance of earning 5 dollars and a 91 percent chance of losing 300 dollars. Sarah plays the game and ends up losing 300 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 8 percent chance of losing 22 dollars and a 92 percent chance of earning 1000 dollars. Susan plays the game and ends up earning 1000 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 6 percent chance of earning 5 dollars and a 94 percent chance of losing 500 dollars. Michael plays the game and ends up losing 500 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 5 percent chance of earning 25 dollars and a 95 percent chance of losing 450 dollars. Sarah plays the game and ends up losing 450 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 99 percent chance of earning 50 dollars and a 1 percent chance of losing 20 dollars. Margaret plays the game and ends up earning 50 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 94 percent chance of earning 450 dollars and a 6 percent chance of losing 15 dollars. Sarah plays the game and ends up earning 450 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 97 percent chance of earning 750 dollars and a 3 percent chance of losing 25 dollars. Sarah plays the game and ends up earning 750 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 3 percent chance of losing 22 dollars and a 97 percent chance of earning 750 dollars. Sarah plays the game and ends up earning 750 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 95 percent chance of earning 900 dollars and a 5 percent chance of losing 5 dollars. David plays the game and ends up earning 900 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 7 percent chance of earning 5 dollars and a 93 percent chance of losing 5000 dollars. David plays the game and ends up losing 5000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 70 percent chance of losing 500 dollars and a 30 percent chance of earning 15 dollars. John plays the game and ends up earning 15 dollars. Did John make the right decision? Choose Y or N.
Answer:


Question: Sarah has the option to play a game where Sarah has a 96 percent chance of losing 200 dollars and a 4 percent chance of earning 12 dollars. Sarah plays the game and ends up losing 200 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 6 percent chance of earning 22 dollars and a 94 percent chance of losing 500 dollars. John plays the game and ends up losing 500 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 91 percent chance of losing 5000 dollars and a 9 percent chance of earning 4 dollars. Sarah plays the game and ends up losing 5000 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 94 percent chance of losing 450 dollars and a 6 percent chance of earning 10 dollars. David plays the game and ends up losing 450 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 91 percent chance of losing 250 dollars and a 9 percent chance of earning 22 dollars. Michael plays the game and ends up losing 250 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 93 percent chance of losing 900 dollars and a 7 percent chance of earning 12 dollars. Susan plays the game and ends up losing 900 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 3 percent chance of losing 3 dollars and a 97 percent chance of earning 100 dollars. Michael plays the game and ends up earning 100 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 5 percent chance of earning 3 dollars and a 95 percent chance of losing 400 dollars. David plays the game and ends up losing 400 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 80 percent chance of losing 7500 dollars and a 20 percent chance of earning 2 dollars. David plays the game and ends up losing 7500 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 10 percent chance of earning 4 dollars and a 90 percent chance of losing 5000 dollars. David plays the game and ends up losing 5000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 30 percent chance of earning 22 dollars and a 70 percent chance of losing 1000 dollars. Margaret plays the game and ends up earning 22 dollars. Did Margaret make the right decision? Choose Y or N.
Answer:


Question: Margaret has the option to play a game where Margaret has a 20 percent chance of earning 2 dollars and a 80 percent chance of losing 900 dollars. Margaret plays the game and ends up losing 900 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 98 percent chance of earning 10000 dollars and a 2 percent chance of losing 5 dollars. Margaret plays the game and ends up earning 10000 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 91 percent chance of earning 50 dollars and a 9 percent chance of losing 20 dollars. David plays the game and ends up earning 50 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 7 percent chance of losing 1 dollars and a 93 percent chance of earning 10000 dollars. Michael plays the game and ends up earning 10000 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 92 percent chance of losing 1000 dollars and a 8 percent chance of earning 10 dollars. Margaret plays the game and ends up losing 1000 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 93 percent chance of earning 10000 dollars and a 7 percent chance of losing 1 dollars. John plays the game and ends up earning 10000 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 6 percent chance of losing 5 dollars and a 94 percent chance of earning 5000 dollars. Michael plays the game and ends up earning 5000 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 25 percent chance of earning 2 dollars and a 75 percent chance of losing 5000 dollars. David plays the game and ends up losing 5000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 2 percent chance of earning 25 dollars and a 98 percent chance of losing 300 dollars. David plays the game and ends up losing 300 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 99 percent chance of losing 450 dollars and a 1 percent chance of earning 3 dollars. Sarah plays the game and ends up losing 450 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 95 percent chance of earning 100 dollars and a 5 percent chance of losing 12 dollars. John plays the game and ends up losing 12 dollars. Did John make the right decision? Choose Y or N.
Answer:


Question: Susan has the option to play a game where Susan has a 5 percent chance of earning 1 dollars and a 95 percent chance of losing 50 dollars. Susan plays the game and ends up losing 50 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 70 percent chance of losing 7500 dollars and a 30 percent chance of earning 15 dollars. Sarah plays the game and ends up losing 7500 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 9 percent chance of earning 1 dollars and a 91 percent chance of losing 1500 dollars. Margaret plays the game and ends up losing 1500 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 7 percent chance of earning 15 dollars and a 93 percent chance of losing 450 dollars. Margaret plays the game and ends up losing 450 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 30 percent chance of earning 15 dollars and a 70 percent chance of losing 300 dollars. John plays the game and ends up losing 300 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 96 percent chance of earning 200 dollars and a 4 percent chance of losing 20 dollars. Michael plays the game and ends up earning 200 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 94 percent chance of earning 750 dollars and a 6 percent chance of losing 20 dollars. Michael plays the game and ends up earning 750 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 20 percent chance of losing 12 dollars and a 80 percent chance of earning 450 dollars. Michael plays the game and ends up earning 450 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 15 percent chance of earning 5 dollars and a 85 percent chance of losing 300 dollars. Sarah plays the game and ends up losing 300 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 70 percent chance of losing 250 dollars and a 30 percent chance of earning 4 dollars. David plays the game and ends up losing 250 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 75 percent chance of earning 500 dollars and a 25 percent chance of losing 25 dollars. Sarah plays the game and ends up losing 25 dollars. Did Sarah make the right decision? Choose Y or N.
Answer:


Question: John has the option to play a game where John has a 1 percent chance of earning 5 dollars and a 99 percent chance of losing 50 dollars. John plays the game and ends up losing 50 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 80 percent chance of earning 1000 dollars and a 20 percent chance of losing 25 dollars. David plays the game and ends up earning 1000 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 20 percent chance of losing 25 dollars and a 80 percent chance of earning 7500 dollars. John plays the game and ends up earning 7500 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 30 percent chance of earning 22 dollars and a 70 percent chance of losing 300 dollars. Michael plays the game and ends up losing 300 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 85 percent chance of earning 500 dollars and a 15 percent chance of losing 25 dollars. Susan plays the game and ends up earning 500 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 30 percent chance of losing 20 dollars and a 70 percent chance of earning 5000 dollars. Susan plays the game and ends up earning 5000 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 97 percent chance of earning 7500 dollars and a 3 percent chance of losing 12 dollars. Susan plays the game and ends up earning 7500 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 91 percent chance of earning 7500 dollars and a 9 percent chance of losing 2 dollars. John plays the game and ends up earning 7500 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 3 percent chance of losing 20 dollars and a 97 percent chance of earning 450 dollars. David plays the game and ends up earning 450 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 9 percent chance of earning 15 dollars and a 91 percent chance of losing 750 dollars. David plays the game and ends up losing 750 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 91 percent chance of losing 900 dollars and a 9 percent chance of earning 25 dollars. Margaret plays the game and ends up earning 25 dollars. Did Margaret make the right decision? Choose Y or N.
Answer:


Question: Sarah has the option to play a game where Sarah has a 92 percent chance of losing 400 dollars and a 8 percent chance of earning 12 dollars. Sarah plays the game and ends up losing 400 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 75 percent chance of earning 450 dollars and a 25 percent chance of losing 25 dollars. Susan plays the game and ends up earning 450 dollars. Did Susan make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 5 percent chance of losing 2 dollars and a 95 percent chance of earning 400 dollars. Margaret plays the game and ends up earning 400 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 90 percent chance of earning 300 dollars and a 10 percent chance of losing 15 dollars. David plays the game and ends up earning 300 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 7 percent chance of earning 4 dollars and a 93 percent chance of losing 200 dollars. Michael plays the game and ends up losing 200 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 7 percent chance of earning 12 dollars and a 93 percent chance of losing 50 dollars. Margaret plays the game and ends up losing 50 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Sarah has the option to play a game where Sarah has a 2 percent chance of losing 22 dollars and a 98 percent chance of earning 500 dollars. Sarah plays the game and ends up earning 500 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 15 percent chance of earning 4 dollars and a 85 percent chance of losing 750 dollars. Susan plays the game and ends up losing 750 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 93 percent chance of earning 250 dollars and a 7 percent chance of losing 4 dollars. Margaret plays the game and ends up earning 250 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 15 percent chance of earning 20 dollars and a 85 percent chance of losing 500 dollars. David plays the game and ends up losing 500 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 93 percent chance of losing 450 dollars and a 7 percent chance of earning 2 dollars. David plays the game and ends up earning 2 dollars. Did David make the right decision? Choose Y or N.
Answer:


Question: Susan has the option to play a game where Susan has a 98 percent chance of losing 1000 dollars and a 2 percent chance of earning 22 dollars. Susan plays the game and ends up losing 1000 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 99 percent chance of earning 1000 dollars and a 1 percent chance of losing 4 dollars. Michael plays the game and ends up earning 1000 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 20 percent chance of losing 3 dollars and a 80 percent chance of earning 900 dollars. David plays the game and ends up earning 900 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 9 percent chance of earning 2 dollars and a 91 percent chance of losing 200 dollars. David plays the game and ends up losing 200 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: David has the option to play a game where David has a 6 percent chance of earning 25 dollars and a 94 percent chance of losing 10000 dollars. David plays the game and ends up losing 10000 dollars. Did David make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 10 percent chance of losing 20 dollars and a 90 percent chance of earning 1500 dollars. Michael plays the game and ends up earning 1500 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 9 percent chance of losing 25 dollars and a 91 percent chance of earning 7500 dollars. David plays the game and ends up earning 7500 dollars. Did David make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 93 percent chance of earning 1500 dollars and a 7 percent chance of losing 22 dollars. John plays the game and ends up earning 1500 dollars. Did John make the right decision? Choose Y or N.
Answer: Y
Question: Sarah has the option to play a game where Sarah has a 9 percent chance of losing 3 dollars and a 91 percent chance of earning 250 dollars. Sarah plays the game and ends up earning 250 dollars. Did Sarah make the right decision? Choose Y or N.
Answer: Y
Question: Michael has the option to play a game where Michael has a 7 percent chance of earning 2 dollars and a 93 percent chance of losing 900 dollars. Michael plays the game and ends up losing 900 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: John has the option to play a game where John has a 3 percent chance of losing 15 dollars and a 97 percent chance of earning 5000 dollars. John plays the game and ends up losing 15 dollars. Did John make the right decision? Choose Y or N.
Answer:


Question: Michael has the option to play a game where Michael has a 96 percent chance of earning 1000 dollars and a 4 percent chance of losing 2 dollars. Michael plays the game and ends up earning 1000 dollars. Did Michael make the right decision? Choose Y or N.
Answer: Y
Question: Susan has the option to play a game where Susan has a 20 percent chance of earning 12 dollars and a 80 percent chance of losing 1500 dollars. Susan plays the game and ends up losing 1500 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 6 percent chance of earning 5 dollars and a 94 percent chance of losing 7500 dollars. Michael plays the game and ends up losing 7500 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 98 percent chance of earning 1500 dollars and a 2 percent chance of losing 5 dollars. Margaret plays the game and ends up earning 1500 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: John has the option to play a game where John has a 3 percent chance of earning 4 dollars and a 97 percent chance of losing 200 dollars. John plays the game and ends up losing 200 dollars. Did John make the right decision? Choose Y or N.
Answer: N
Question: Susan has the option to play a game where Susan has a 97 percent chance of losing 100 dollars and a 3 percent chance of earning 3 dollars. Susan plays the game and ends up losing 100 dollars. Did Susan make the right decision? Choose Y or N.
Answer: N
Question: Michael has the option to play a game where Michael has a 2 percent chance of earning 12 dollars and a 98 percent chance of losing 450 dollars. Michael plays the game and ends up losing 450 dollars. Did Michael make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 20 percent chance of earning 10 dollars and a 80 percent chance of losing 1000 dollars. Margaret plays the game and ends up losing 1000 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: N
Question: Margaret has the option to play a game where Margaret has a 99 percent chance of earning 400 dollars and a 1 percent chance of losing 10 dollars. Margaret plays the game and ends up earning 400 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: Margaret has the option to play a game where Margaret has a 97 percent chance of earning 100 dollars and a 3 percent chance of losing 12 dollars. Margaret plays the game and ends up earning 100 dollars. Did Margaret make the right decision? Choose Y or N.
Answer: Y
Question: David has the option to play a game where David has a 85 percent chance of losing 100 dollars and a 15 percent chance of earning 15 dollars. David plays the game and ends up earning 15 dollars. Did David make the right decision? Choose Y or N.
Answer:


medical_dialog zh has very slow _generate_examples

bug

Something isn't working

## Describe the bug
After downloading the files from Google Drive, `load_dataset("medical_dialog", "zh", data_dir="./")` takes an unreasonable amount of time. Generating the train/test split for 33% of the dataset takes over 4.5 hours.

## Steps to reproduce the bug
The easiest way I've found to download files from Google Drive is to use `gdown` and use Google Colab because the download speeds will be very high due to the fact that they are both in Google Cloud.

```python
file_ids = [
        "1AnKxGEuzjeQsDHHqL3NqI_aplq2hVL_E",
        "1tt7weAT1SZknzRFyLXOT2fizceUUVRXX",
        "1A64VBbsQ_z8wZ2LDox586JIyyO6mIwWc",
        "1AKntx-ECnrxjB07B6BlVZcFRS4YPTB-J",
        "1xUk8AAua_x27bHUr-vNoAuhEAjTxOvsu",
        "1ezKTfe7BgqVN5o-8Vdtr9iAF0IueCSjP",
        "1tA7bSOxR1RRNqZst8cShzhuNHnayUf7c",
        "1pA3bCFA5nZDhsQutqsJcH3d712giFb0S",
        "1pTLFMdN1A3ro-KYghk4w4sMz6aGaMOdU",
        "1dUSnG0nUPq9TEQyHd6ZWvaxO0OpxVjXD",
        "1UfCH05nuWiIPbDZxQzHHGAHyMh8dmPQH",
]
for i in file_ids:
    url = f"https://drive.google.com/uc?id={i}"
    !gdown $url

from datasets import load_dataset

ds = load_dataset("medical_dialog", "zh", data_dir="./")
```

## Expected results
Faster load time

## Actual results
`Generating train split: 33%: 625519/1921127 [4:31:03<31:39:20, 11.37 examples/s]`

## Environment info
- `datasets` version: 2.0.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5

@vrindaprabhu , could you take a look at this since you implemented it? I think the `_generate_examples` function might need to be rewritten


datasets.load_metric can not load a local metirc

Something isn't working

## Describe the bug
No matter how I hard try to tell load_metric that I want to load a local metric file, it still continues to fetch things on the Internet. And unfortunately it says 'ConnectionError: Couldn't reach'. However I can download this file without connectionerror and tell load_metric its local directory. And it comes back where it begins...

## Steps to reproduce the bug
```python
metric = load_metric(path=r'C:\Users\Gare\PycharmProjects\Gare\blue\bleu.py')
    ConnectionError: Couldn't reach https://github.com/tensorflow/nmt/raw/master/nmt/scripts/bleu.py

metric = load_metric(path='bleu')
    ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.12.1/metrics/bleu/bleu.py

metric = load_metric(path='./blue/bleu.py')
    ConnectionError: Couldn't reach https://github.com/tensorflow/nmt/raw/master/nmt/scripts/bleu.py
```

## Expected results
I do read the docs [here](https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_metric). There are no other parameters that help function to distinguish from local and online file but path. As what I code above, it should load from local.

## Actual results

> metric = load_metric(path=r'C:\Users\Gare\PycharmProjects\Gare\blue\bleu.py')

> ~\AppData\Local\Temp\ipykernel_19636\1855752034.py in <module>
----> 1 metric = load_metric(path=r'C:\Users\Gare\PycharmProjects\Gare\blue\bleu.py')
D:\Program Files\Anaconda\envs\Gare\lib\site-packages\datasets\load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
    817         if data_files is None and data_dir is not None:
    818             data_files = os.path.join(data_dir, "**")
--> 819
    820         self.name = name
    821         self.revision = revision
D:\Program Files\Anaconda\envs\Gare\lib\site-packages\datasets\load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)
    639         self,
    640         path: str,
--> 641         download_config: Optional[DownloadConfig] = None,
    642         download_mode: Optional[DownloadMode] = None,
    643         dynamic_modules_path: Optional[str] = None,
D:\Program Files\Anaconda\envs\Gare\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
    297             token = hf_api.HfFolder.get_token()
    298         if token:
--> 299             headers["authorization"] = f"Bearer {token}"
    300     return headers
    301
D:\Program Files\Anaconda\envs\Gare\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
    604             def _resumable_file_manager():
    605                 with open(incomplete_path, "a+b") as f:
--> 606                     yield f
    607
    608             temp_file_manager = _resumable_file_manager
ConnectionError: Couldn't reach https://github.com/tensorflow/nmt/raw/master/nmt/scripts/bleu.py

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Windows-10-10.0.22000-SP0
- Python version: 3.7.13
- PyArrow version: 7.0.0
- Pandas version: 1.3.4

Any advice would be appreciated.


Representing dictionaries (json) objects as features

enhancement

New feature or request

In the process of adding a new dataset to the hub, I stumbled upon the inability to represent dictionaries that contain different key names, unknown in advance (and may differ between samples), original asked in the [forum](https://discuss.huggingface.co/t/representing-nested-dictionary-with-different-keys/16442).

For instance:

```
sample1 = {"nps": {
  "a": {"id": 0, "text": "text1"},
  "b": {"id": 1, "text": "text2"},
}}
sample2 = {"nps": {
  "a": {"id": 0, "text": "text1"},
  "b": {"id": 1, "text": "text2"},
  "c": {"id": 2, "text": "text3"},
}}
sample3 = {"nps": {
  "a": {"id": 0, "text": "text1"},
  "b": {"id": 1, "text": "text2"},
  "c": {"id": 2, "text": "text3"},
  "d": {"id": 3, "text": "text4"},
}}
```

the `nps` field cannot be represented as a Feature while maintaining its original structure.
@lhoestq suggested to add JSON as a new feature type, which will solve this problem.

It seems like an alternative solution would be to change the original data format, which isn't an optimal solution in my case. Moreover, JSON is a common structure, that will likely to be useful in future datasets as well.


Hotfix failing CI tests on Windows

_The documentation is not available anymore as the PR was closed or merged._

This PR makes a hotfix for our CI Windows tests: https://app.circleci.com/pipelines/github/huggingface/datasets/11092/workflows/9cfdb1dd-0fec-4fe0-8122-5f533192ebdc/jobs/67414

Fix #4118

I guess this issue is related to this PR:
- huggingface/huggingface_hub#815


Failing CI tests on Windows

bug

Something isn't working

## Describe the bug
Our CI Windows tests are failing from yesterday: https://app.circleci.com/pipelines/github/huggingface/datasets/11092/workflows/9cfdb1dd-0fec-4fe0-8122-5f533192ebdc/jobs/67414


Something isn't working

Hi @arymbe, thanks for reporting.

Unfortunately, I'm not able to reproduce your problem.

Could you please write the complete stack trace? That way we will be able to see which package originates the exception.

Hello, thank you for your fast replied. this is the complete error that I got

---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

Input In [27], in <module>
----> 1 from datasets import load_dataset

venv/lib/python3.8/site-packages/datasets/__init__.py:39, in <module>
     37 from .arrow_dataset import Dataset, concatenate_datasets
     38 from .arrow_reader import ReadInstruction
---> 39 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
     40 from .combine import interleave_datasets
     41 from .dataset_dict import DatasetDict, IterableDatasetDict

venv/lib/python3.8/site-packages/datasets/builder.py:40, in <module>
     32 from .arrow_reader import (
     33     HF_GCP_BASE_URL,
     34     ArrowReader,
   (...)
     37     ReadInstruction,
     38 )
     39 from .arrow_writer import ArrowWriter, BeamWriter
---> 40 from .data_files import DataFilesDict, sanitize_patterns
     41 from .dataset_dict import DatasetDict, IterableDatasetDict
     42 from .features import Features

venv/lib/python3.8/site-packages/datasets/data_files.py:297, in <module>
    292     except FileNotFoundError:
    293         raise FileNotFoundError(f"The directory at {base_path} doesn't contain any data file") from None
    296 def _resolve_single_pattern_in_dataset_repository(
--> 297     dataset_info: huggingface_hub.hf_api.DatasetInfo,
    298     pattern: str,
    299     allowed_extensions: Optional[list] = None,
    300 ) -> List[PurePath]:
    301     data_files_ignore = FILES_TO_IGNORE
    302     fs = HfFileSystem(repo_info=dataset_info)

AttributeError: module 'huggingface_hub' has no attribute 'hf_api'

This is weird... It is long ago that the package `huggingface_hub` has a submodule called `hf_api`.

Maybe you have a problem with your installed `huggingface_hub`...

Could you please try to update it?
```shell
pip install -U huggingface_hub
```

Yap, I've updated several times. Then, I've tried numeral combination of datasets and huggingface_hub versions. However, I think your point is right that there is a problem with my huggingface_hub installation. I'll try another way to find the solution. I'll update it later when I get the solution. Thank you :)

I'm sorry I can't reproduce your problem.

Maybe you could try to create a new Python virtual environment and install all dependencies there from scratch. You can use either:
- Python venv: https://docs.python.org/3/library/venv.html
- or conda venv (if you are using conda): https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

## Describe the bug
Could you help me please. I got this following error.

AttributeError: module 'huggingface_hub' has no attribute 'hf_api'

## Steps to reproduce the bug
when I imported the datasets

# Sample code to reproduce the bug
from datasets import list_datasets, load_dataset, list_metrics, load_metric

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: macOS-12.3-x86_64-i386-64bit
- Python version: 3.8.9
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
- Huggingface-hub: 0.5.0
- Transformers: 4.18.0

Thank you in advance.


Pretty print dataset info files

maybe just do it from now on no? (i.e. not for existing `dataset_infos.json` files)

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4116). All of your documentation changes will be reflected on that endpoint.

> maybe just do it from now on no? (i.e. not for existing dataset_infos.json files)

Yes, or do this only for datasets created with `push_to_hub` to (always) keep the GH datasets small?

I reverted the change to avoid the size increase and added the `pretty_print` flag, which pretty-prints the JSON, and that flag is only True for datasets created with `push_to_hub`.

Adds indentation to the `dataset_infos.json` file when saving for nicer diffs.

(suggested by @julien-c)

This PR also updates the info files of the GH datasets. Note that this change adds more than **10 MB** to the repo size (the total file size before the change: 29.672298 MB, after: 41.666475 MB), so I'm not sure this change is a good idea.

`src/datasets/info.py` is the only relevant file for reviewers.


ImageFolder add option to ignore some folders like '.ipynb_checkpoints'

enhancement

New feature or request

Maybe it would be nice to ignore private dirs like this one (ones starting with `.`) by default.

CC @mariosasko

**Is your feature request related to a problem? Please describe.**
I sometimes like to peek at the dataset images from jupyterlab. thus '.ipynb_checkpoints' folder appears where my dataset is and (just realized) leads to accidental duplicate image additions. I think this is an easy enough thing to miss especially if the dataset is very large.

**Describe the solution you'd like**
maybe have an option `ignore` or something .gitignore style
`dataset = load_dataset("imagefolder", data_dir="./data/original", ignore="regex?")`

**Describe alternatives you've considered**
Could filter out manually


Multiprocessing with FileLock fails in python 3.9

bug

Something isn't working

On python 3.9, this code hangs:
```python
from multiprocessing import Pool
from filelock import FileLock

def run(i):
    print(f"got the lock in multi process [{i}]")

with FileLock("tmp.lock"):
    with Pool(2) as pool:
        pool.map(run, range(2))

```

This is because the subprocesses try to acquire the lock from the main process for some reason. This is not the case in older versions of python.

This can cause many issues in python 3.9. In particular, we use multiprocessing to fetch data files when you load a dataset (as long as there are >16 data files). Therefore `imagefolder` hangs, and I expect any dataset that needs to download >16 files to hang as well.

Let's see if we can fix this and have a CI that runs on 3.9.

cc @mariosasko @julien-c


Chump change, boy, now you gotta hate, boy
Pull up the with the motherfucking AK, boy
Pull up like its motherfucking game day, boy
Shoot em with the pistol, shoot em point-blank, boy, yuh
Chump change, boy, now you gotta hate, boy
Pull up the with the motherfucking AK, boy
Pull up like its motherfucking game day, boy
Shoot em with the pistol, shoot em point-blank, boy, yuh
Chump change, boy, now you gotta hate, boy
Pull up the with the motherfucking AK, boy
Pull up like its motherfucking game day, boy
Shoot em with the pistol, shoot em point-blank, boy, yuh
Product of the poison, you can bet your life on it
Teeth got the ice on it, wrist still got the slice on it
That walking stigmata and poltergeist
Still feelin stuck every single night
Dont give a fuck if I live or die
Surprised to this point that I have survived
Pull up with the motherfuckin AK tucked, bitchboy, good luck
Yung Plague got the blade, hit the slay
Then we spray with the .44 5
Bitch, its Grey59, let the devil go and ride
Aint nothing but a pussy if you gon throw shade
Fuckboy, Im hungry, and you looking like steak
Trigger finger itching, and you looking like fake
Trash done passed and you looking like waste, yah
Chump change, boy, now you gotta hate, boy
Pull up the with the motherfucking AK, boy
Pull up like its motherfucking game day, boy
Shoot em with the pistol, shoot em point-blank, boy, yuh
Chump change, boy, now you gotta hate, boy
Pull up the with the motherfucking AK, boy
Pull up like its motherfucking game day, boy
Shoot em with the pistol, shoot em point-blank, boy, yuh
Tonight we guaranteeing graves, tonights the night we ride
Tomorrow will never come, yeah, tonights the night we die
Yesterday I met my death, I think its been a long time
Stuck in limbo with my kinfolk, gave my life to $uicide
Yung Plague with the masked face
Bones will never break because I rape the souls of those who chose the wicked road, Im haunting the afraid
No name on the grave, just grey stains spray-painted slave to the great grey grave, ayy
Lend me the help, or send me to hell, either way I know Ill die
The G, the 5 the 9, its $UICIDE
Chump change, boy, now you gotta hate, boy
Pull up the with the motherfucking AK, boy
Pull up like its motherfucking game day, boy
Shoot em with the pistol, shoot em point-blank, boy, yuh


Juul smokin motherfucker, dont come around me with no vape
Got yo bitch under the cover suckin on me like a pipe
Its the hero of the dark, but I dont ever wear a cape
Take a Bic, and then I spark until the blunt evaporates
There aint no debatin $uicide, too deep when I ride
Ruby gettin high off of the pills that I supply
Just cruisin by on flat tires in an 06 Chevy
Looking like Im Bloody Mary, sippin codeine and berry
Im Donnie Darko when I spark up and Im creepin like Frank
Raisin that choppa right at your face, and after that I go blank
Cause $lickity off the hinges, with syringes, shootin up my veins
Rather be left alone, I dont care to know your name, ho
Pull up in a Lamborghini just so you can see me
Swerve into a telephone pole, my skeleton broke
Pirellis smoke and burn
Put my ashes in a blunt and light the front, your visions blurred
You might just learn how Ruby da Cherry has seen the world
Watch me flex like Im in the next life
Bitch, I was next in line for heaven
But then I ingested a double cup full of that fucking insecticide
Insects inside my dead body, ironically, after Oddy was identified
Possibly gotta be specified, cause the weather was intensified
Draggin the lake
They made a mistake because Yung Plague was recognized
The sun turned black
The sky replaced by the face with giant reddened eyes
Christ cannot compare, he cannot save by breakin bread and wine
$uicideboy$ can relate to the revelations because we dead inside


First, I turned the Nile River into blood!
Then I released the frogs as the city floods!
Along with lice, flies, and I diseased your livestock!
Then I make sure the boils grow on your wife and child!
Look up at the fucking sky, youll see a storm of hail and fire!
Then I sent out the locusts to swarm your fucking crops!
I blacked out the sun and made time stop!
Next Im coming for your firstborn son, watch him drop!
Bitch, Im Yung Plague, here to spread the flames
Makin it fucking rain alligator fangs
Lightin the flame that ignited the revelation of Osiris
Im risin above the horizon, I blackened the diamond
And siphoned all of the water supply of Zion
Bitch, I cut the head clean off of the lion
Put it on a spike, and all of the sheep became enlightened, bitch
Im standin right beside Poseidon bout to release all the Titans
Footprints in the sand, followed by a herd of rotten bison
Grey, grey, grey, grey
Grey, grey, grey, grey
Grey, grey, grey, grey
Grey, grey, grey, GREY!
Crucify me with the nails in my feet
With the nails in my wrists, and that blade in my chest
Enlightened by Zion, Im wearin the lion
With teeth on a rope that surrounding my neck
Standing in fire, Im God of the liars
Attire is made up of nothin but wire
The choir is singin my praises with phrases that let you all know Im the bastard of Satan
Black in my eyes, homicide on my mind
With that blood on my face, then I turn it to wine
Yung Christ the divine, make them fall into line
Let the planets align as they build me a shrine
The end of the world as you know it
When Im up in hell, I turn fire to snow, bitch
The waters, they swell as the polars are broken
Evoking the smoke from the bodies thats choking, ho


Bitch, Im Yung Plague with the AK steady sinning
Fuck life, and everything that fucking comes with it
Put the barrel in my mouth, blow my fucking brains out
I dont give a fuck about anything, now Im finished
Never killed a motherfucker, but thats bout to fucking change
Throw my ass in jail, and just let me fucking decay
The world is fucking grey, I grip the fucking blade
And carve a line across my neck, and now Im fucking saved
Bitch, I pull up in that motherfucking donk
Florida to N.O., we down to catch a homi
Bipolar switching, twitching, now Im Tony with the Tommy
Demonic logic, I might vomit from popping narcotics
Biologic product of a savage, my mamas psychotic
Here to abolish, killing trees with the touch of my finger
My shadows linger, the crypt keeper, its $carecrow the reaper
Burning infernos, Im eternal, these fuckboys just verbal
G-R-E-Y, hear the war cry of thousands thats burning
My T is black, hell is dark, and so is my heart
But its switchin like the nigga with the Yu-Gi-Oh! cards
Against all odds, eyes on another when you go against god
The ultimate is watching, so I guess Im in charge
And if you think that youre hard, Ima act retarded
Why you ride bikes? Better off in a car bitch
Im off it, stop, then Im back in my starship
The ultimate martian in the hood will only look like hobgoblin
Steady mobbing, push it to the limit I could talk about rough bitch
Know a couple niggas that can afford the apartments
Black on black lens so Im black and Im heartless
Shots in the dark, Im an excellent marksman
Chop a choppa down, come around with karma
Thats why a nigga vibe when a nigga want drama
Wish a nigga would like Cosmo and Wanda
No Timmy got a long black Jimmy
Smoking on that season milli and the plug is outta Philly
And I still tote the semi when Im runnin through the city
Like a motherfucking renegade
Reminiscing bout the brothers of my old days
All back bringing back to my old ways
Closed case, peel a nigga like a young K, my nigga
Drowning in my thoughts around inside will kill me in the sleep
Feeling all my days are numbered, count until the end of me
Angel wings broken but Ill never let the devil creep
ULT for life and Ima rap until the death of me
Drowning in my thoughts around inside will kill me in the sleep
Feelin like all my days are numbered, count until the end of me
Angel wings broken but Ill never let the devil creep
Ultimately $uicidal that means I can never sleep


Creeping up out the dungeon
Bitch, all it look like is fungus
Pentagram, bitch, got the hunnid
All of this weed I be crumbling, now smoking
The roaches approaching the hopeless and roping
The bitches like roses and then I start stomping
Roping the bone, better watch your step
Toting the TEC, throw that shade at your neck
Schema boys, $uicide throw up the set
Im unleashing the evil from under my bed
Killing myself with the noose or the knife
Bitch, Im through with my life, yeah, Id rather be dead
Let the demons arise, let them enter my head
Grey in my eyes, but they leave something red
Piercing the crucifix deep in my chest
Leaving a mess for the bugs to infest
Smoking my dope to the dome
King of heroin and euphoria clutching my brain
Look in the mirror then I fucking figure
Im Lucifer running around changing the game
Changing the pages of history, bitch
Fuck the Bible, Im rolling it up in a swisher
Fuck off my liver, it shivers and quivers
Im popping pain pills like Im popping the trigger
The Hitler of the $uicide cult that follows
Bottles I swallow with scripts no regard for a tomorrow
Soaking in sorrow the $carecrow has risen off the post
The muddy coast, sipping codeine till I overdose
All I think about is putting a hole in my brain
Never wanna deal with another thing
That dont got a thing to do with levitating to the plane
It started with a bang, ended with me
Face down on a bloody carpet and now Im lookin down at me
I never thought that Id be thinking the way I been
Predicaments, in a pickle, whatever you wanna say
I know what you wanna say
You wanna say itll be okay one day
Ima roll the dice and bet 7 on death
Dont fear the reaper, hes coming to siphon the life from your chest
Say that you wishing to die
But you wishing to get more attention, but bitch, now you next
Puffing that Bible swish with $lick
While watching you die and Im sipping my liq
Soon as the bottle is done, commit Seppuku, Im out this bitch
Commit Schemacide with Seppuku, its your fate
I hope you die slow, from the puncture of the blade
If you got no nuts to do it yourself then its okay
I got the red beam, and its aiming at your face
Commit Schemacide with Sepukku, its your fate
I hope you die slow, from the puncture of the blade
If you got no nuts to do it yourself then its okay
I got the red beam, and its aiming at your face


Silver-coloured gift box with white interior and silver logo. No exchanges or returns are accepted.


Collared dress featuring sleeveslling below the elbow with cuffs and pleats. Front patch pockets with flaps. Gathering at the waist. Button-up front.


Midi dress with a lapel collar and crossover V-neckline, long sleeves, an adjustable waistband with matching drawstrings. Asymmetric hem with side vents and a front slit.


Round neck midi dress with long sleeves. Featuring interior lining with adjustable straps and buttonstening at the back with an opening.


Flowing midi dress with a Johnny collar. Short sleeves. Polo-style buttonstening on the front.


Oversized collared shirt. Long cuffed sleeves with pleats. Dart detail in the back. Asymmetric hem. Polo-style buttonstening in the front.


Long sleeve bodysuit with a sweetheart neckline. Back zip and snap-buttonstening at the bottom.


Long sleeve sweatshirt with a high neck. Polo-stylestening with snap buttons.


Collared shirt with long cuffed sleeves. In-seam pockets on the sides, an asymmetric hem with side vents and frontstening with hidden buttons.


Round neck top with thin straps and a knot detail at the back.


Input: [Date] 3 years 5 weeks ago [Title] Had to log in [Body]

Output: to give my two cents on the best thread ever. I had to go with the under technique for a while, due to the animals, but they overcame that barrier. Then, just ended up putting the roll on nearby cabinet or on top of the tank. This works well until retards who come to my house try to be helpful and re-install the roll to its standard location. I have now removed all the toilet paper roll holders from all three shitters and all is happy in the hood. there ya go.


Input: [Date] 3 years 6 weeks ago [Title] I will be there [Body]

Output: It's my hometown and Taco is playing for the ole alma mater - North didn't even exist while I lived there. Anyway, my folks live about half a mile from the field, so I'm thinking it might be a perfect spot for an MGoBlog pregame shindig (post game too, maybe). Having a Michigan themed tailgate for a HS game in the heart of Buckeye country might be the best idea of all time. I don't know if we can contact each other directly on here, but if anyone figures that out, and is interested, feel free to shoot me a message of some sort. Otherwise, I'll figure out some details and toss a post up when the time gets a little closer. Cool? Cool.


Input: [Date] 3 years 11 weeks ago [Title] Oh crap [Body]

Output: I'm gonna have to get rid of my Old Glory thong before The Man comes a knockin'. Oh well, I hope I didn't taint an Amurican icon.


Input: [Date] 3 years 11 weeks ago [Title] Yes, they are hideous [Body]

Output: If someone made me wear these, I would have to stick my foot up their ass just to improve their aesthetics.


Input: [Date] 3 years 11 weeks ago [Title] Gotta agree [Body]

Output: Intergallactic might be the worst B-boy song possible in terms of a pump up song. Overall, the list isn't horrible - its a definite improvement. The rap section is pretty week tho. Can't agree with Outkast's Fresh/Clean either when ya got Bombs Over Baghdad sittin just a few tracks away. Love the inclusion of any Rage song though - great for that perfect mix of funkiness and getting pumped up. How could you not get pumped if Bombtrack comes at you?


Input: [Date] 3 years 11 weeks ago [Title] True. [Body]

Output: In fact, I heard that Shane now has the authority to accept verbal commitments and then just inform the coaches later. This role may soon extend to B-Ball recruits as well.


Input: [Date] 3 years 11 weeks ago [Title] He will, [Body]

Output: but then he is gonna be pissed when he finds out the writer he hired doesn't look like Casper and can't walk through walls.


Input: [Date] 3 years 12 weeks ago [Title] Imma go [Body]

Output: tip back some brass monkey in honor of MCA. Nothin else.


Input: [Date] 3 years 12 weeks ago [Title] This blows [Body]

Output: I am really bummed by this. I've loved me some b-boys since the eighties. I know MCA has been sick for a while, but this still really sucks. Thank you, Adam, and keep it ill - wherever you are.


Input: [Date] 3 years 13 weeks ago [Title] During his week on campus [Body]

Output: I'm sure he saw waaaaayyyyy more than that - and they weren't standing still either.


"Screen grey LMAO"


"THE RETURN TO MONKEY ISLAND! - !BasementTour / !MobileApp - !PC"


"CUSTOMS BLAST BALLS with CAS! CODE: XXJLR"


"(DROPS) ICON SKIN GAMEPLAY - Season 4 Update | Use Code "SypherPK""


"yo, couldn't sleep"


Diesel model Winnie Harlow, who suffers from vitiligo, the same rare skin condition that singer Michael Jackson was diagnosed with, has been pictured enjoying a cozy night on the town with albino fashion star Shaun Ross. The pair attended a launch event for Popular magazine at Siren Studios in Hollywood, California, on Tuesday night, and were snapped holding hands while making their way into the venue. Winnie, 19, who was unveiled as one of the newest faces of fashion label Diesel earlier this year, was wearing a short and simple black and gold mini dress, which she accessorized with a pair of gold strappy heels. Scroll down for video . Looking cozy: Winnie Harlow, 19, who suffers from the same rare skin condition as Michael Jackson, was pictured hand-in-hand with albino model Shaun Ross on Tuesday night . Out on the town: The duo were attending the launch for Popular magazine at Hollywood venue Siren Studios . Skingraft black and gold dress . Get it from Pasar here . Visit site . This look is all types of yes. Winnie Harlow looks incredible in her leather tunic dress which features striking gold trims on the sleeves and hemline. The loose fit creates a flattering silhouette too. Winnie's dress is from LA-based fashion house Skingraft, and you can still buy it by clicking right to Paser's website now. It'll be your go-to party piece! It is however, slightly pricey coming in at $750, so if you want to add a golden standard to your style for less, why not check out these various finds below? We are particularly dazzled by this Mynt 1792 number at Nordstrom. A pair of strappy metallic heels will finish off your look to perfection! Choice's black and gold stamping dress . Visit site . Mynt 1792 color block dress at Nordstrom . Visit site . Maykool black and gold dress . Visit site . Dailylook fit and flare dress . Visit site . Meanwhile Shaun, 23, opted for a more low-key look, wearing an all-black ensemble, which he topped off with a unique tall baseball hat and a pair of leather sneaker-style boots. And while it appears to be the first time that the duo have been spotted enjoying an evening out together, it is likely not the first occasion that they have met. Both stars have made regular appearances on the catwalks at various Fashion Weeks, while Shaun has also starred in campaigns for the likes of Alexander McQueen and Givenchy. Coincidentally, the models also each had a helping hand from fellow fashion star Tyra Banks. Fashion stars: While Evan opted for an all-black ensemble, topped off with a bizarre baseball cap, Winnie wore a black and gold mini dress and strappy sandals . Rising stars: Both Shaun and Winnie have each achieved global recognition in the fashion indsutry, appearing in numerous high-profile fashion campaigns . Strut your stuff: Both Winnie (L), pictured at the Desigual fall/winter 2015 show, and Shaun (R), pictured on the catwalk in February, boast impressive runway credentials . Winnie, whose real name is Chantelle Brown-Young, was one of the contestants on Tyra's hit reality series America's Next Top Model, while Shaun made an appearance on the former model's eponymous talk show in 2009. Despite their cozy appearance, it's not thought that the two are dating - however Shaun did take the time to post a heartwarming message to the model on his Instagram account, just hours after the pair attended the Popular magazine event. Alongside an image of the two of them, Shaun wrote: 'So much fun last night @winnieharlow keep up the good work. From an original believer in you.' And it seems his sweet sentiments were reciprocated by Winnie, who posted a similar image on her own account, saying: 'Sweet nothings with my fly friend @shaundross'

Winnie, 19, was diagnosed with the rare condition when she was four-years-old .
Condition causes a lack of melanin which forms white patches on the skin .


(CNN)When Melissa Atkins Wardy, author of "Redefining Girly" and a passionate advocate for fighting gender stereotypes, heard from a frustrated mom on Facebook, she knew she needed to do something. So she shared the mom's story. In a blog post, Wardy told how the mom, Veronica of Richland, Washington, wanted to buy "Big Hero 6" fabric to make pillows for her two children. The problem? The fabric didn't include two of the heroes from the movie: the female characters, Honey Lemon and GoGo. So Veronica decided to send an email to Springs Creative, the company that makes the fabric, saying that as a woman and an engineer, she was offended. She got an answer from the company but it didn't help. Girl empowerment ads like GoldieBlox: Do they work? A licensing manager for Springs Creative responded that the company has found that boys don't want girl characters on their things. "Eeeww girls! Yuck! Haha," the licensing manager wrote. It will come as no surprise that Veronica was none too pleased to receive that response. "I was frustrated and disgusted that a manager, and a female manager too, would laugh off my initial contact with the company. It was unprofessional. So I responded," Veronica told CNN. She asked that we not use her last name, in order to maintain her privacy. In an email back to the company, Veronica wrote, "By eliminating the women in your fabric design, you are telling boys that it's OK to think girls are yucky, unworthy and less than a boy. "You are also telling girls they are unworthy, unwanted and that it's un-cool to be smart and confident." What happened next shows that parents, aided by social media outrage, may have more leverage to combat gender stereotyping in our culture than they realize. And the "Big Hero 6" example -- along with a similar recent one involving TOMS, the shoes retailer -- suggests that manufacturers may be finally getting it, too. First, the rest of the story about Springs Creative and the "Big Hero" fabric: After sharing Veronica's story with her readers and her 6,700 Twitter followers, Wardy encouraged people to give Springs Creative a piece of their mind. She also invited them to tweet their own stories with the hashtag #IncludeTheGirls. Women responded in droves, pointing out how Black Widow, the female character in the popular "Avengers" superhero movie franchise, is often excluded from merchandise and how cereal boxes rarely include female characters. "This sends the direct message to both boys and girls that females are forgettable, unimportant, undesirable. What a horrible thing to teach our children!" said Wardy, who is also the founder and chief executive officer of Pigtail Pals & Ballcap Buddies, a company that makes empowering clothing for boys and girls. "The prevailing theory is, 1. boys are the default audience, and 2. boys won't want an item with a girl on it," Wardy said. "Yet, I talk with thousands of parents every week who say otherwise." Why trying to make our kids happy can backfire . Those parents are clearly making their voices heard. Because not too long after Veronica received her initial response from Springs Creative, Wardy said she got a call from the company, asking her to post a statement from Springs Creative on her site. "It is sometimes difficult to hear negative feedback but the message was clear and we intend to act upon your message," the statement read. "Most importantly, Springs Creative does not condone sexism in any shape or form and does not design products to shine a negative light on females OR males." The company said it would be talking with Disney "immediately" about additional designs for "Big Hero 6" that would incorporate all the characters. "We would never intentionally offend any segment of the population. We are a strong company with positive morals and values and we respect and see both genders equally," said the statement. Springs Creative told CNN it would have no further comment. Veronica said she was surprised the company responded so quickly. "I do not believe this would have happened without the Pigtail Pals and Ballcap Buddies community also raising their voices." The real March Madness: When will women's teams get equal buzz? Around the same time, Wardy heard from another follower about an issue with TOMS and wrote about it on her site. On the normally progressive retailer's Web landing page for kids were photos that some parents felt played into gender stereotypes. One photo suggested "Playtime Approved" shoes for boys, and the other, this one on a pink background, included the caption "Little Ladies: adorn their feet for spring." "Really, TOMS? Girls are not ornaments we adorn. Girls play, too!" Wardy wrote on her Facebook page. Two hours later, Wardy received a comment from TOMS on her Facebook page, saying that the company completely agrees and that the wording has been changed to now say "new arrivals for kids." "We're really grateful whenever our community brings something like this to our attention," said Doug Piwinski, senior vice president, global marketing and communications for TOMS. "Perhaps as much or more than any other brand, I mean we really listen to our customers in our community." So, here's my question: Are the two speedy responses from companies after women spoke out on social media more a sign that companies are truly getting it about gender stereotypes and girl and boy empowerment, or more a sign of the power of parents? Or are they a mixture of both? How to Super Bowl #LikeAGirl . Wardy said there's no doubt that companies are in business to protect their bottom line, and that will always be a motivator. But she said these back-to-back incidents show what can happen when parents make it clear they won't buy products from companies that sell gender stereotypes. " 'Be not silent' is my mantra. Speak up!" Wardy said. "The success with Springs Creative and TOMS and other wins we've had in the past is an example of the power of parents (moms and dads) and children's advocates aggregating their voices to say, 'Enough is enough.' " Veronica said it's a sign of the power of parents becoming "more aware, reactive and using social media to make their voices heard by companies that market to children." She added, "One voice can be dismissed; the hailstorm of voices collectively cannot be ignored so easily." It is clear, from these two examples, that parents using their voices online and with their wallets can encourage companies to change. There's no question that raising awareness about stereotypes helps, said Wardy. "If companies aren't paying attention by now, they most certainly should be." Pink, princess-y and sexy too soon . Do you think more companies are getting the message to include girl characters on merchandise? Share your thoughts with Kelly Wallace on Twitter or CNN Living on Facebook.

A mom of two got upset when "Big Hero 6" fabric didn't include the two female characters .
Another mom called attention to gender stereotypes on a TOMS Web page .
In both cases, the companies responded quickly to answer parents' concerns .


(CNN)Blinky and Pinky on the Champs Elysees? Inky and Clyde running down Broadway? Power pellets on the Embarcadero? Leave it to Google to make April Fools' Day into throwback fun by combining Google Maps with Pac-Man. The massive tech company is known for its impish April Fools' Day pranks, and Google Maps has been at the center of a few, including a Pokemon Challenge and a treasure map. This year the company was a day early to the party, rolling out the Pac-Man game Tuesday. It's easy to play: Simply pull up Google Maps on your desktop browser, click on the Pac-Man icon on the lower left, and your map suddenly becomes a Pac-Man course. Twitterers have been tickled by the possibilities, playing Pac-Man in Manhattan, on the University of Illinois quad, in central London and down crooked Lombard Street in San Francisco, among many locations: .

Google Maps has a temporary Pac-Man function .
Google has long been fond of April Fools' Day pranks and games .
Many people are turning their cities into Pac-Man courses .


(CNN)Would you want a TV program about your family history to include details of a distant, long-deceased relative who had owned slaves? Seriously, who in their right mind would want to be tarnished by the sins of an ancestor you had no connection to other than a remote bloodline? I wouldn't, and neither did Ben Affleck, who lobbied producers of a PBS show, "Finding Your Roots," to remove any reference to his great-great-great grandfather Benjamin Cole, a Georgia slave owner in the mid-1800's, in an episode that looked at Affleck's family history. (Affleck revealed Cole's name Wednesday night.) Here's the thing that might surprise many, given the tendency of the media to exploit any potentially scandalous material: The show's producer, Harvard professor Henry Louis Gates Jr., apparently acquiesced to Affleck's request, because when the episode aired in October 2014, there was no mention of the star's slave-owning ancestor. Affleck's attempt to alter the content of the program only publicly became known a few days ago after WikiLeaks released hacked emails revealing an exchange between Gates and Sony Pictures chief Michael Lynton. When Gates asked how he should respond to Affleck's request to delete the material, Lynton responded, "all things being equal, I would definitely take it out." And on Tuesday, Affleck, via Facebook, admitted that he had urged Gates to excise any reference to his slave-owning relative. Affleck explained, "I didn't want any television show about my family to include a guy who owned slaves. I was embarrassed. The very thought left a bad taste in my mouth." Now Gates has publicly denied that he made his decision about the content of the program based on Affleck's request, but it seems likely that he did. After all, Affleck noted as much in his Facebook post, writing that Gates "agreed with me on the slave owner but made other choices I disagreed with." And three other celebrities profiled on the series last season were shown to have been related to slave owners. So it's unlikely that it was simply happenstance that it left out any reference to Affleck's familial slave ownership connection. In any event, PBS has launched an internal review to determine if the show violated its own editorial standards. Whatever the results of the review, Affleck and Gates did the right thing. Let's be clear: "Finding Your Roots" is not an investigative news show; it's an entertainment program. In fact, as Affleck noted, much of the material is provided directly by the celebrity being profiled. It's not "60 Minutes" but more in the nature of a sophisticated profile of celebrities, using their marquee names to attract viewers. There's not even a hint that Affleck strong-armed the producers or made any type of threats against them if they included the information. In the emails between Gates and Lynton, which they presumably believed at the time were confidential and would remain so, there was no mention of undue pressure by Affleck. Instead, Gates simply noted that Affleck  "asked us to edit out something." After the emails were revealed, Gates issued a statement, saying, "Ultimately, I maintain editorial control on all of my projects and, with my producers, decide what will make for the most compelling program. In the case of Mr. Affleck we focused on what we felt were the most interesting aspects of his ancestry -- including a Revolutionary War ancestor, a third great-grandfather who was an occult enthusiast, and his mother who marched for civil rights during the Freedom Summer of 1964." My view would be different if Affleck had a history of uttering racist remarks or engaging in racist conduct. That would have made the information truly newsworthy. But instead we have a man known for championing progressive causes, which I'm sure made him even more acutely sensitive that some on the right might possibly use this information against him in the future. Perhaps that's what Affleck meant in his Facebook post when he wrote that this information made him feel "vulnerable." Affleck has noted that he regrets asking PBS to not include information about his "distant relative."  That's a nice gesture, but it was not needed. Affleck had every right to ask for the information about a long-deceased distant relative to be left out of the show. And PBS had the choice to include it or leave it out. I applaud PBS for doing the right thing at a time when media outlets rarely show any restraint on the lives of people in the public eye.

Ben Affleck admits he asked PBS show "Finding Your Roots" to avoid mentioning his slave-owning ancestor .
Dean Obeidallah says the actor and the show were right to leave the detail out .


Requires practice (qualifier value)

Representation of a person's general functional status capability.


Does not (qualifier value)


Unable (qualifier value)


Assisted (qualifier value)


Independent (qualifier value)


Dependent (qualifier value)


Able to and does (qualifier value)


Able with difficulty (qualifier value)


Does (qualifier value)


Does without difficulty (qualifier value)


Computer vision | Image and signal processing | Pattern recognition | Machine learning | Artificial intelligence | Mixed reality


PhD Student @Mila_Quebec @IID_ULaval @universitelaval | ex-@nvidia, masters @iiscbangalore | robust deep learning, autonomous driving, OOD, XAI


Actor at Asmita theater
Wanna be a storyteller.


#ML #3DArt #GameDev #VR #AR #photogrammetry


The human right is to become an immortal all-mighty god.


23.
MERN stack, in progress
Humanities, in progress


PhD student at The University of Hong Kong. Doing research about 3D vision and computer graphics.


https://t.co/DP6muBl1Li. in biomedical engineering, machine learning & deep learning enthusiast


but the geoduck is the biggest clam in North America


DHS DHS Department of Homeland Security (DHS): Financial Assistance Department of Homeland Security Department of Homeland Security (DHS): Financial Assistance DHS


How to Multiply Rational Numbers


www.bvd-pi.com http://www.bvd-pi.com bvd-pi.com www.bvd-pi.com bvd-pi.com http://www.bvd-pi.com www.bvd-pi.com www.bvd-pi.com


Hume Cronyn Hume Cronyn Hume Cronyn English Hume Cronyn English English Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn English English English Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn English Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Wikipedia - http://en.wikipedia.org/wiki/Hume_Cronyn Hume Cronyn Hume Cronyn English Hume Cronyn English Hume Cronyn Hume Cronyn English view article Hume Cronyn Jessica Tandy and Hume Cronyn Hume Cronyn Hume Cronyn FULL ARTICLE AT WIKIPEDIA.ORG Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn English Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn view article Hume Cronyn Hume Cronyn Hume Cronyn English FULL ARTICLE AT WIKIPEDIA.ORG Hume Cronyn English Hume Cronyn English Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn Hume Cronyn FULL ARTICLE AT WIKIPEDIA.ORG Hume Cronyn Wiki Hume Cronyn Hume Cronyn English Hume Cronyn English English Hume Cronyn Hume Cronyn FULL ARTICLE AT WIKIPEDIA.ORG English Hume Cronyn


How Much Does It (Really) Cost to Decorate?


Jacory Harris


Produce Weight Yield Chart


I'm still waiting for my card.


I withdrew cash but it still shows as pending?


On what do you base your exchange rates?


Is the closest ATM to me within 2 miles?


I want to get a new card before my current one expires, how much will it cost me and how long will it take to get here.


Can this app be used to exchange currencies?


My money hasn't been deposited yet. When will it be?


What's the youngest I can be to open an account?


What went wrong with my transfer?


Do you have EUR currency available for me?


Add crying, waiting, hoping to my weekly buzz playlist.


I'd like to watch Broadway Nights at Megaplex Theatres


Please add a track by david freiberg to my workout playlist.


Add brian larsen to my digster sleep


Will there be a blizzard in North Carolina


find Three in the Attic


I want to hear a nineties track by stat quo on Deezer


Play music by mina caputo on Lastfm


Please show me the picture History Repeating


Is there snowfall in Hineston New Hampshire


Previous studies showed a dramatic difference between two S1 isoforms in their heat-induced aggregation measured at low ionic strength: S1(A1) aggregated at much lower temperature than S1(A2) [15,16]. This implies that the N-terminal extension of A1, due to its semi-rigid, antenna-like structure [17], can be involved not only in intramolecular interaction, but also into intermolecular interactions with motor domains of other S1 molecules. It seems possible that these interactions may induce some conformational changes in the S1 motor domain, which can be expressed in changes of its thermal denaturation.


In the present work, we applied differential scanning calorimetry (DSC) for a detailed comparative analysis of thermal denaturation of two S1 isoforms, S1(A1) and S1(A2), and, in particular, to investigate the domain structure of these S1 isoforms. For this purpose, a new calorimetric approach was specially developed to analyze the DSC profiles of irreversibly denaturing multidomain proteins (see Section 2). Using this approach, we have revealed two calorimetric domains in the S1 molecule, the more thermostable domain denaturing in two steps. We also applied special approaches to identify these calorimetric domains, i.e., to reveal their correspondence to structural domains of the S1 molecule. Furthermore, we applied dynamic light scattering (DLS) to analyze aggregation of the two S1 isoforms induced by their thermal denaturation and to elucidate the mechanism of this aggregation. The experiments were performed at physiological ionic strength (100 mM KCl), and the results were compared with those obtained at low ionic strength both in earlier studies [15,16] and in the present work. We have found no appreciable difference between S1(A1) and S1(A2) in their aggregation under conditions used (100 mM KCl). It has been shown that kinetics of this process is independent of the protein concentration and the aggregation rate is limited by irreversible denaturation of the motor domain. Some difference between the two S1 isoforms was only revealed by DSC, independently of the ionic strength conditions, in the thermal denaturation of the regulatory domain. Thus, the interaction of the N-terminal extension of the A1 light chain with the motor domain seems to be too weak to produce any effect on its thermal denaturation even at low ionic strength.


For the sake of simplicity it is assumed that denaturation of each individual calorimetric domain is described by the one-stage model of irreversible denaturation:


where N and D are native and denatured states of the protein and k is the rate constant of the first order (or pseudo-first order) reaction. Further, it is assumed that the dependence of the rate constant k on temperature follows the Arrhenius law:


Integration of this equation gives the expression for the portion of the native form as a function of temperature:


[USER] The only problem Kenya can cause in Russia is give them Ruto. He'll steal everything


When will ngilu burn her finance offices again?


RT @BillHanage: I wish more people understood this so clearly. Of course the more people are vaccinated the better it gets


[Clinical application of lateral great toe flap in decoratively reconstruction of thumb pulp defect].

To investigate the validity and the outcome of a new surgery method that utilizes lateral great toe flap to decoratively reconstruct the thumb pulp loss. From Jan. 2009 to Jan. 2014, 22 cases with unilateral thumb pulp defect were included, for whom, lateral great toe flap was deployed to reconstruct the thumb. Blood circulation was re-established by the anastomosis of the digital vessels of toes and fingers in 18 cases, the other 4 cases were conducted by a dorsal metatarsal artery and vein anastomosed to the deep branch of the radial artery and the dorsal metacarpal vein respectively; as for the nerve repair, toe-to-finger digital nerve anastomosis was performed. The donor sites were covered by full-thickness inguinal skin grafts. All the 22 flaps survived without complications. With 6 to 12 months follow-up, the surviving fingers all presented aesthetically pleasant appearance with vivid loops and whorls. Normal functions like sweating were restored as well, and the two-point-discrimination ranged 3.0 to 6.0 mm (4.2 mm in average). The skin grafts to the donor sites healed uneventfully, the appearance and functions of the feet were unaffected. Lateral toe flap should be the first-line choice for reconstructing thumb defect, as it conveniently restores both functions and outlook.