A biomimetics based robot is disclosed. The robot may include filament driven and fluid pumped elastomer based artificial muscles coordinated for slow twitch/fast twitch contraction and movement of the robot by one or more microcontrollers. A process may provide physics based simulation for movement of a robot in a virtual setting. Embodiments include artificial skin and sensor systems in the artificial muscles and artificial skin whose feedback is used to control the muscles and movement of the robot.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a compressed recurrent neural network (RNN). One of the systems includes a compressed RNN, the compressed RNN comprising a plurality of recurrent layers, wherein each of the recurrent layers has a respective recurrent weight matrix and a respective inter-layer weight matrix, and wherein at least one of recurrent layers is compressed such that a respective recurrent weight matrix of the compressed layer is defined by a first compressed weight matrix and a projection matrix and a respective inter-layer weight matrix of the compressed layer is defined by a second compressed weight matrix and the projection matrix.

Systems, methods, and non-transitory computer-readable media can receive a compressed convolutional neural network (CNN). A media content item to be processed can be acquired. The compressed CNN to can be utilized to apply a media processing technique to the media content item to produce information about the media content item. It can be determined, based on at least some of the information about the media content item, whether to transmit at least a portion of the media content item to one or more remote servers for additional media processing.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a compressed recurrent neural network (RNN). One of the systems includes a compressed RNN, the compressed RNN comprising a plurality of recurrent layers, wherein each of the recurrent layers has a respective recurrent weight matrix and a respective inter-layer weight matrix, and wherein at least one of recurrent layers is compressed such that a respective recurrent weight matrix of the compressed layer is defined by a first compressed weight matrix and a projection matrix and a respective inter-layer weight matrix of the compressed layer is defined by a second compressed weight matrix and the projection matrix.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a compressed recurrent neural network (RNN). One of the systems includes a compressed RNN, the compressed RNN comprising a plurality of recurrent layers, wherein each of the recurrent layers has a respective recurrent weight matrix and a respective inter-layer weight matrix, and wherein at least one of recurrent layers is compressed such that a respective recurrent weight matrix of the compressed layer is defined by a first compressed weight matrix and a projection matrix and a respective inter-layer weight matrix of the compressed layer is defined by a second compressed weight matrix and the projection matrix.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a compressed recurrent neural network (RNN). One of the systems includes a compressed RNN, the compressed RNN comprising a plurality of recurrent layers, wherein each of the recurrent layers has a respective recurrent weight matrix and a respective inter-layer weight matrix, and wherein at least one of recurrent layers is compressed such that a respective recurrent weight matrix of the compressed layer is defined by a first compressed weight matrix and a projection matrix and a respective inter-layer weight matrix of the compressed layer is defined by a second compressed weight matrix and the projection matrix.

An ultrasound system and a clutter filtering method thereof are provided. The clutter filtering method includes: transmitting an ultrasound signal to an object; receiving a reflection signal reflected from the object; performing a singular value decomposition (SVD) on a plurality of doppler signals constituting the reflection signal; dividing a representation of the object into a plurality of regions according to a result of performing the SVD; determining cutoff frequencies of the plurality of regions according to different methods; and performing clutter filtering on the plurality of regions by using the determined cutoff frequencies.

Systems, methods, and non-transitory computer-readable media can receive a compressed convolutional neural network (CNN). A media content item to be processed can be acquired. The compressed CNN to can be utilized to apply a media processing technique to the media content item to produce information about the media content item. It can be determined, based on at least some of the information about the media content item, whether to transmit at least a portion of the media content item to one or more remote servers for additional media processing.