Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a clustering neural network. One of the methods includes obtaining unlabeled training data; and training the clustering neural network on the unlabeled training data to determine trained values of the clustering parameters by minimizing a normalized cuts loss function that includes a first term that measures an expected normalized cuts of clustering nodes in a graph representing the data set into the plurality of clusters according to clustering outputs generated by the clustering neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Provided herein are methods and compositions to extract and enrich by, physical separation or amplification, relatively short nucleic acids from a nucleic acid composition containing a high background of longer nucleic acids (e.g., host or maternal nucleic acids; genomic nucleic acid and the like).

Abstract: Provided herein are methods and compositions to extract and enrich by, physical separation or amplification, relatively short nucleic acids from a nucleic acid composition containing a high background of longer nucleic acids (e.g., host or maternal nucleic acids; genomic nucleic acid and the like).

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: A method comprises: receiving, via a processor, an image depicting a tissue; quantifying, via the processor, the image based on: segmenting, via the processor, the image into a plurality of segments; identifying, via the processor, a plurality of histological elements in the segments; forming, via the processor, a network graph comprising a plurality of nodes, wherein the histological elements correspond to the nodes; measuring, via the processor, a feature of the network graph; performing, via the processor, a transformation on the image based on the feature; determining, via the processor, a non-parametric feature of the image based on the transformation; saving, via the processor, the non-parametric feature onto a database.

Abstract: A method comprises: receiving, via a processor, an image depicting a tissue; quantifying, via the processor, the image based on: segmenting, via the processor, the image into a plurality of segments; identifying, via the processor, a plurality of histological elements in the segments; forming, via the processor, a network graph comprising a plurality of nodes, wherein the histological elements correspond to the nodes; measuring, via the processor, a feature of the network graph; performing, via the processor, a transformation on the image based on the feature; determining, via the processor, a non-parametric feature of the image based on the transformation; saving, via the processor, the non-parametric feature onto a database.

Abstract: Provided herein are methods and compositions to extract and enrich by, physical separation or amplification, relatively short nucleic acids from a nucleic acid composition containing a high background of longer nucleic acids (e.g., host or maternal nucleic acids; genomic nucleic acid and the like).

Abstract: Provided herein are methods and compositions to extract and enrich by, physical separation or amplification, relatively short nucleic acids from a nucleic acid composition containing a high background of longer nucleic acids (e.g., host or maternal nucleic acids; genomic nucleic acid and the like).

Abstract: Provided herein are methods and compositions to extract and enrich by, physical separation or amplification, relatively short nucleic acids from a nucleic acid composition containing a high background of longer nucleic acids (e.g., host or maternal nucleic acids; genomic nucleic acid and the like).

Abstract: Provided herein are methods and compositions to extract and enrich by, physical separation or amplification, relatively short nucleic acids from a nucleic acid composition containing a high background of longer nucleic acids (e.g., host or maternal nucleic acids; genomic nucleic acid and the like).

Abstract: Provided herein are methods and compositions to extract and enrich by, physical separation or amplification, relatively short nucleic acids from a nucleic acid composition containing a high background of longer nucleic acids (e.g., host or maternal nucleic acids; genomic nucleic acid and the like).

Abstract: Provided herein are methods and compositions to extract and enrich by, physical separation or amplification, relatively short nucleic acids from a nucleic acid composition containing a high background of longer nucleic acids (e.g., host or maternal nucleic acids; genomic nucleic acid and the like).

Abstract: Provided herein are methods and compositions to extract and enrich by, physical separation or amplification, relatively short nucleic acids from a nucleic acid composition containing a high background of longer nucleic acids (e.g., host or maternal nucleic acids; genomic nucleic acid and the like).

Abstract: A system for delivering multimedia information between at least one mobile vessel and at least one multimedia content center includes at least one media server, wireless access router, and client device. The media server is placed in the multimedia content center for providing or storing the multimedia information. The wireless access router is coupled to the media server in the multimedia content center for transmitting or receiving the multimedia information for the media server over a wireless local area network. The client device installed in the mobile vessel for transmitting or receiving the multimedia information to or from the media server via the wireless access router over the wireless local area network, thereby allowing the client device to utilize the multimedia information.