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FOOTWEAR - Low-tops & sneakers on YOOX.COM Celine 44SsYky46C
FOOTWEAR - Low-tops & sneakers on YOOX.COM Celine
Matt Novak
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If you’ve ever watched the History Channel at 3AM, you know that the Nazis had a secret program during World War II to develop flying saucers. The Nazi’s UFO experiments never actually flew, but the model toy company Revell recently released a set in Germany that makes it look like one of the Nazi saucers actually worked. And historians are pissed.

“At that time it was technologically impossible to build something like this,” historian Jens Wehner told Germany’s Frankfurter Allgemeine Zeitung (FAZ) newspaper last week.

The toy company has pulled the 69-part set, known as the Haunebu II, from store shelves. But you can still find plenty of the toys available for sale online. The Nazi UFO is even seen on the box blasting Allied planes out of the sky—a disgusting image to promote, to say the least.

The real-life flying saucer project was started in 1934 and some conspiracy theorists believe that the Nazis actually did achieve flight with the vehicle in 1943. Some believe that the Nazis hid these UFOs in secret bases in places like Antartica, and the History Channel even devoted a 2009 episode of its show “UFO Hunters” to the Nazi plans for space travel . But there’s no real evidence that the Nazis achieved flight with these strange contraptions.

“Unfortunately, our product description does not adequately express this and we apologize for it,” Revell said in a statement .

Why pull the toy from shelves? Germany has strict laws against glorification of Nazis. And while the government didn’t demand it, historians pointed out that making a toy that imagined Nazis had achieved space travel might corrupt young minds. And they’re not wrong.

“Enthusiasts can use this as a strategy to cast doubt on what we know today about Nazism,” Wehner told Frankfurter Allgemeine Zeitung .

Holocaust deniers like to promote the idea that the Nazis were actually the oppressed group and that we’d have an advanced high-tech society if the Third Reich had succeeded. Ideas like a working Nazi flying saucer help fuel myths about the Nazi regime and downplay the atrocities that they committed, like killing roughly 10 million people in a campaign to systematically exterminate Jews and other “undesirables.”

But the last thing that the people of 2018 need is a sympathetic view of the Nazis. We already have plenty of those on Twitter and at the highest levels of government .

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Matt Novak

Matt Novak is the editor of Gizmodo's Paleofuture blog

(incoming, n_units, activation='sigmoid', dropout=None, bias=True, weights_init=None, return_seq=False, return_state=False, initial_state=None, dynamic=False, trainable=True, restore=True, reuse=False, scope=None, name='SimpleRNN')

Simple Recurrent Layer.

Input

3-D Tensor [samples, timesteps, input dim].

Output

if : 3-D Tensor [samples, timesteps, output dim]. else: 2-D Tensor [samples, output dim].

Arguments

(incoming, n_units, activation='tanh', inner_activation='sigmoid', dropout=None, bias=True, weights_init=None, forget_bias=1.0, return_seq=False, return_state=False, initial_state=None, dynamic=False, trainable=True, restore=True, reuse=False, scope=None, name='LSTM')

Long Short Term Memory Recurrent Layer.

Input

3-D Tensor [samples, timesteps, input dim].

Output

if : 3-D Tensor [samples, timesteps, output dim]. else: 2-D Tensor [samples, output dim].

Arguments

References

Long Short Term Memory, Sepp Hochreiter Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.

Links

http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf

(incoming, n_units, activation='tanh', inner_activation='sigmoid', dropout=None, bias=True, weights_init=None, return_seq=False, return_state=False, initial_state=None, dynamic=False, trainable=True, restore=True, reuse=False, scope=None, name='GRU')

Gated Recurrent Unit Layer.

Input

3-D Tensor Layer [samples, timesteps, input dim].

Output

if : 3-D Tensor [samples, timesteps, output dim]. else: 2-D Tensor [samples, output dim].

Arguments

References

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, K. Cho et al., 2014.

Links

http://arxiv.org/abs/1406.1078

(incoming, rnncell_fw, rnncell_bw, return_seq=False, return_states=False, initial_state_fw=None, initial_state_bw=None, dynamic=False, scope=None, name='BiRNN')

Build a bidirectional recurrent neural network, it requires 2 RNN Cells to process sequence in forward and backward order. Any RNN Cell can be used i.e. SimpleRNN, LSTM, GRU... with its own parameters. But the two cells number of units must match.

Input

3-D Tensor Layer [samples, timesteps, input dim].

Output

if : 3-D Tensor [samples, timesteps, output dim]. else: 2-D Tensor Layer [samples, output dim].

Arguments

incoming n_units activation dropout weights_init return_seq return_state initial_state dynamic

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