Google Inc.

Massachusetts Institute of Technology

Yuan Zhang

Brian James Mulford

Nathan Frey

Alexandros Panagopoulos

Yinquan Hao

David Joseph Weiss

Darrell J Irvine

Ron Weiss

Mariane Bandeira Melo

Tasuku Kitada

Jason Michael Baldridge

Daniel Gillick

Jason Riesa

Anton Bakalov

Brandon Kwong

Yiran Zheng

Described herein are systems and methods of converting media dimensions. A device may identify a set of frames from a video in a first orientation as belonging to a scene. The device may receive a selected coordinate on a frame of the set of frames for the scene. The device may identify a first region within the frame including a first feature corresponding to the selected coordinate and a second region within the frame including a second feature. The device may generate a first score for the first feature and a second score for the second feature. The first score may be greater than the second score based on the first feature corresponding to the selected coordinate. The device may crop the frame to include the first region and the second region within a predetermined display area comprising a subset of regions of the frame in a second orientation.

Method for converting landscape video to portrait mobile layout using a selection interface

Disclosed herein are lipid nanoparticles comprising 1,2-dioleoyl-3-trimethylammonium propane (DOTAP), 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), cholesterol, and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)-2000](DSPE-PEG2000), compositions including such lipid nanoparticles, and methods of treatment using such lipid nanoparticles and compositions.

Lipid nanoparticles and use thereof to deliver RNA polynucleotides

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating dependency parses for input text segments, which may be provided as inputs to a natural language processing system. One of the systems includes a first neural network comprising: one or more initial neural network layers configured to, for each token in an input text sequence: receive features for the token; and collectively process the features to generate an alternative representation of the features for use in determining a part of speech of the token in the input text sequence; and a dependency parsing neural network configured to: process the alternative representations of the features for the tokens in the input text sequence generated by the one or more initial neural network layers to generate a dependency parse of the input text sequence.

Generating dependency parses of text segments using neural networks

A method for identifying codemixed text includes receiving codemixed text and segmenting the codemixed text into a plurality of tokens. Each token includes at least one character and is delineated from any adjacent tokens by a space. For each token of the codemixed text, the method also includes extracting features from the token and predicting a probability distribution over possible languages for the token using a language identifier model configured to receive the extracted features from the token as feature inputs. The method also includes assigning a language to each token of the codemixed text by executing a greedy search on the probability distribution over the possible languages predicted for each respective token.

Identifying codemixed text

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Torrance, CA, United States of America

Yuan Zhang