公开(公告)号：US20210298624A1

公开(公告)日：2021-09-30

申请号：US16829179

申请日：2020-03-25

Applicant: X Development LLC

Inventor： Sarah Ann Laszlo ,  Jeffrey Bush ,  Bin Ni ,  Matthew Sibigtroth

IPC: A61B5/04 ,  A61B5/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for neuroanatomical tract visualization using synaptic connectivity graphs. In one aspect, a method comprises: presenting, to a user and through a display, a representation of a synaptic connectivity graph representing synaptic connectivity between neurons in a brain of a biological organism; receiving, from the user, data specifying a seed neuron in the brain; identifying a neuroanatomical tract corresponding to the seed neuron in the brain; and presenting, to the user and through the display, a geometric representation of at least a portion of the brain of the biological organism that visually distinguishes the neuroanatomical tract corresponding to the seed neuron at neuronal resolution.

公开(公告)号：US11048482B2

公开(公告)日：2021-06-29

申请号：US16523363

申请日：2019-07-26

Applicant: X Development LLC

Inventor： Georgios Evangelopoulos ,  Benoit Schillings ,  Bin Ni

IPC: G06F8/33 ,  G06N3/04 ,  G06N3/08 ,  G06F8/36 ,  G06F8/71 ,  G06F8/35 ,  G06N3/02

Abstract: Implementations are described herein for automatically identifying, recommending, and/or automatically effecting changes to a source code base based on updates previously made to other similar code bases. Intuitively, multiple prior “migrations,” or mass updates, of complex software system code bases may be analyzed to identify changes that were made. More particularly, a particular portion or “snippet” of source code—which may include a whole source code file, a source code function, a portion of source code, or any other semantically-meaningful code unit—may undergo a sequence of edits over time. Techniques described herein leverage this sequence of edits to predict a next edit of the source code snippet. These techniques have a wide variety of applications, including but not limited to automatically updating of source code, source code completion, recommending changes to source code, etc.

公开(公告)号：US20210011694A1

公开(公告)日：2021-01-14

申请号：US16506161

申请日：2019-07-09

Applicant: X Development LLC

Inventor： Bin Ni ,  Zhiqiang Yuan ,  Qianyu Zhang

IPC: G06F8/41 ,  G06N20/00

Abstract: Techniques are described herein for translating source code in one programming language to source code in another programming language using machine learning. In various implementations, one or more components of one or more generative adversarial networks, such as a generator machine learning model, may be trained to generate “synthetically-naturalistic” source code that can be used as a translation of source code in an unfamiliar language. In some implementations, a discriminator machine learning model may be employed to aid in training the generator machine learning model, e.g., by being trained to discriminate between human-generated (“genuine”) and machine-generated (“synthetic”) source code.