Δικτατορία και δημοκρατία Ο Sacha Baron Cohen σε απόσπασμα από την ταινία «Ο Δικτάτορας» εξηγεί στους Αμερικανούς τα πλεονεκτήματα της δικτατορίας.
Ριψοκίνδυνο ζευγάρωμα Δύο γάτες προσπαθούν να ζευγαρώσουν πάνω στο κλαδί ενός δέντρου, αλλά χάνουν την ισορροπία τους.
J.J. Abrams shows off an X-Wing fighter in new ‘Star Wars: Episode VII’ set video In a special video message from the set of Star Wars: Episode VII, director J.J. Abrams announces the creation of Star Wars: Force for Change, a brand new Star Wars initiative from Disney and Lucasfilm, in collaboration with Bad Robot, dedicated to finding creative solutions to some of the world's biggest problems. The first Star Wars: Force for Change campaign will raise funds and awareness for UNICEF's Innovation Labs and its innovative projects benefitting children in need.
Ο νέος κινητήρας του Golf Σε αντίθεση με τον κινητήρα του LADA, αυτός τροφοδοτείται με Coca-Cola και σουβλάκια.
Ο σκύλος ντύθηκε αρκουδάκι Για το Halloween, μια γυναίκα μεταμόρφωσε τον σκύλος της, ένα μικρό σι-τζου, σε αρκουδάκι. Αγόρασε ένα λούτρινο αρκουδάκι με το ίδιο χρώμα και έκοψε το πρόσωπο και το σώμα, αφήνοντας τα αυτιά και τα χέρια.
Μαθήτριες από την Ιαπωνία χορεύουν Abba Σε σχολικό διαγωνισμό χορού στην Ιαπωνία, μια ομάδα από μαθήτριες λυκείου κάνουν μια εξαιρετική χορογραφία με το κομμάτι "Gimme Gimme Gimme" των Abba.
Huge Saint Bernard dog being needy Sully the Saint Bernard dog really loves his dad! Watch as he pins him down and gives him a big hug after returning home from a long day of work. Now that's adorable!
Phase-Functioned Neural Networks for Character Control We present a real-time character control mechanism using a novel neural network architecture called a Phase-Functioned Neural Network. In this network structure, the weights are computed via a cyclic function which uses the phase as an input. Along with the phase, our system takes as input user controls, the previous state of the character, the geometry of the scene, and automatically produces high quality motions that achieve the desired user control. The entire network is trained in an end-to-end fashion on a large dataset composed of locomotion such as walking, running, jumping, and climbing movements fitted into virtual environments. Our system can therefore automatically produce motions where the character adapts to different geometric environments such as walking and running over rough terrain, climbing over large rocks, jumping over obstacles, and crouching under low ceilings. Our network architecture produces higher quality results than time-series autoregressive models such as LSTMs as it deals explicitly with the latent variable of motion relating to the phase. Once trained, our system is also extremely fast and compact, requiring only milliseconds of execution time and a few megabytes of memory, even when trained on gigabytes of motion data. Our work is most appropriate for controlling characters in interactive scenes such as computer games and virtual reality systems.