Abstract: A method for detecting anomalies in a physical system generates from a set of physics rules and a process graph representing the system a set of candidate physics models that assign physics rules to portions of the process graph representing sensors. Candidate physics models are rejected if an error between the models and sensor data exceed a predetermined error tolerance. Supervised learning is used to train a machine learning model to predict an error between the physics models and the sensor data. The predicted error and predicted sensor measurements from the physics models are then used to detect anomalies using unsupervised learning on a distribution of error between the predicted sensor measurements and the sensor data.

Abstract: A method is disclosed for generating a corrective film pattern for reducing wafer bow in a semiconductor wafer fabrication process. The method inputs to a neural network a wafer bow signature for a predetermined semiconductor fabrication step. The neural network generates from the input a corrective film pattern corresponding to the wafer bow signature. The neural network is trained with a training dataset of wafer shape transformations and corresponding corrective film patterns.

Abstract: A method for detecting anomalies in a physical system generates from a set of physics rules and a process graph representing the system a set of candidate physics models that assign physics rules to portions of the process graph representing sensors. Candidate physics models are rejected if an error between the models and sensor data exceed a predetermined error tolerance. Supervised learning is used to train a machine learning model to predict an error between the physics models and the sensor data. The predicted error and predicted sensor measurements from the physics models are then used to detect anomalies using unsupervised learning on a distribution of error between the predicted sensor measurements and the sensor data.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: A system and method are disclosed for unifying tags of a media user community by transferring user-supplied tags between individual user media libraries. One aspect comprises a process for identifying, within a user community, common tags associated with media items and common media items associated with tags by cross-referencing media items and corresponding tags among individual media user libraries. The system in one embodiment provides a user with a unified classification of tags, which can be used to search, navigate and organize an individual user's media library.