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Carl Vondrick MIT Hamed Pirsiavash University of Maryland Baltimore County Antonio Torralba MIT NIPS 2016

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Abstract

We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation.

Below are some selected videos that are generated by our model. These videos are not real; they are hallucinated by a generative video model. While they are not photo-realistic, the motions are fairly reasonable for the scene category they are trained on.

Beach

Train Station

Conditional Video Generations

We can also use the model to add animations to static images by training it to generate the future. Of course, the future is uncertain, so the model rarely generates the "correct" future, but we think the prediction has some plausibility.

More Results

For more results, check out the links below. Warning: The pages are fairly large (100 MB), so please browse on a computer and not a phone, and wait for the GIFs to download completely.

Learning models that generate videos may also be a promising way to learn representations. For example, we can train generators on a large repository of unlabeled videos, then fine-tune the discriminator on a small labeled dataset in order to recognize some actions with minimal supervision. We report accuracy on UCF101 and compare to other unsupervised learning methods for videos:

We can also visualize what emerges in the representation for predicting the future. While not all units are semantic, we found there are a few hidden units that fire on objects which are sources of motions, such as people or train tracks. Since generating the future requires understanding moving objects, the network may learn to recognize these objects internally, even though it is not supervised to do so.

Hidden unit for "people"
Hidden unit for "train tracks"

The images above highlight regions where a particular convolutional hidden unit fires.

POS is the future, more pools and less hashrate sales will probably work too.

Sean June 4, 2018 @ 7:35 am

Jeez, five pages of autobot links. Copy/paste as a way of life.

Take the Monero route. Make your algorithm as ASIC unfriendly as possible and allow for regular modifications. The more mainstream hardware you have mining the better.

And integrate your securenodes and (upcoming) supernodes into the process in some way. There’s a massive resource right there, right now, doing little other than synthetic challenges even before you get the supernodes sorted out. (I know, a trivial endeavour to get the nodes validating blocks)

You have all the pieces.

Geoff Cooke June 5, 2018 @ 10:41 pm

Has there been any consideration by the Dev team to the use of ProgPOW ( Progressive POW Algo ) ?

Thoughts on the Rise of Equihash-capable ASICs and 51% Attacks against Equihash Blockchains June 6, 2018 @ 11:30 am

[…] double-spend part is extremely interesting here because in the case of the ZenCash attack, the attacker knew exactly where to hit by seemingly targeting an exchange which had set by default […]

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June 6, 2018 @ 11:46 am

[…] exchange partners took mitigating actions, and our marketing team put out this comprehensivecommunity updatethat same […]

ZenCash: Mais um Ataque de 51% Ocorre em uma Criptomoeda | Cotabit June 6, 2018 @ 11:50 am

[…] A ZenCash (ZEN), uma criptomoeda focada em privacidade, com valor de mercado de US$ 100 milhões, tornou-se a mais recentealtcoina sucumbir a um ataque de 51%, conforme confirmado pela equipe em umpost no blog. […]

ZenCash Cryptocurrency Hacked in 51% Attack |Crowdfund Insider – Online Coin News June 6, 2018 @ 1:00 pm

[…] ZenCash, a privacy coin and fork of ZClassic, which is itself a fork of ZCash, a privacy coin once recommended by Edward Snowdon, has been subjected to a 51% attack where a hacker gained enough control of the ZenCash network to “double spend” 23,152.3 ZEN coins. […]

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June 6, 2018 @ 1:01 pm

[…] ZenCash(ZEN)は公式ブログで、日本時間6月3日(日)午前11:43頃、51%攻撃を受けたと発表した。51%攻撃とは、不正な取引を目的に、悪意ある者が過半数以上のマイニングの計算能力を保持し、ブロックチェーンのネットワークを乗っ取ることをいう。 […]

ZenCash Cryptocurrency Hacked in 51% Attack |Crowdfund Insider – Top Coins News June 6, 2018 @ 1:21 pm

Ifbleeding carries on after baby is a week old or if bleeding happens at a later stagethan the first week, continue breastfeeding but always check with your doctor (Newman, 2014).

If the cause of blood in breast milk is due to a mother’s cracked ordamaged nipples, it is important to find the cause of the damage so the nipples can heal. See our article on Brand New Unisex Cheap Price Alymer Croc Slouch Bag Finery Cheap Eastbay SN6eKCAl3n
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or using a pump with a very high vacuum could cause bleeding from broken capillaries (tiny blood vessels) as they are very delicate.

An intraductal papilloma is a small growth in the lining of a duct near the nipple. It is usually noncancerous and doesn’t cause pain

Fibrocystic breasts is the name used for a group of symptoms in the glandular breast tissue including breast pain, solid lumps and cysts. Breastfeeding A Guide for the Health Profession discusses that fibrocystic breasts are a cause of a discharge of blood from the nipple in pregnancy and lactation in approximately a third of cases but it is not a contraindication to breastfeeding. The authors mention one case where fibrocystic breasts were found to be the cause of an infant vomiting due to the volume of blood in breast milk.

Jack Newman, Canadian paediatrician and breastfeeding expert says that although blood will cause a baby to spit up more, breastfeeding can continue. He explains:

Taking the baby off the breast is often suggested if the mother’s nipples bleed from a crack or abrasion. But blood in the milk is not a reason to take thebaby off the breast. The issue is the pain the mother feels, not the blood. Blood in the baby’s stomach can cause spitting up but is not dangerous. If we can make the mother’s pain tolerable, even if the nipples continue to bleed, let’s keep the baby on the breast. If the damage is minimized by fixing the way thebaby takes the breast, the abrasions/cracks will heal and the bleeding will stop.

Breastfeeding author and lactation consultant Nancy Mohrbacher Fox Fur Pom Pom Charm in White Fendi For Sale Official Site Ti0CHQ5F8p
agrees that it is OK to continue breastfeeding and that the bleeding will not be harmful to the baby.

If your baby has drunk a lot of blood stained breast milk they might have very dark coloured poop or may spit up very blood-stained milk. The blood can form a large clump in the stomach and this might look like a lot of blood when spit up— always check with your health professional for immediate medical advice

always check with your health professional for immediate medical advice

Your health professionals will be able to determine whether the blood is from baby or the breastby checking the regurgitated blood for foetal or adult haemoglobin

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