Abstract: A computing system generates a default view of content items associated with a user account. The default view is representative of an underlying hierarchical structure of the content items associated with the user account. The computing system receives a request to transition from the default view to a modified view of the content items. The computing system ranks the content items based on a predicted likelihood of the user interacting with the particular content item. The computing system identifies the subset of content items for the modified view based on the ranking. The computing system generates the modified view based on the identified subset of the content items. The modified view maintains the underlying hierarchical structure of the content items associated with the user account. The computing system provides the modified view to the user associated with the user account for display.

Abstract: A computer-implemented process is programmed to process a source input, determine text enhancements, and present the text enhancements to apply to the sentences dictated from the source input. A text processor may use machine-learning models to process an audio input to generate sentences in a presentable format. An audio input can be processed by an automatic speech recognition model to generate electronic text. The electronic text may be used to generate sentence structures using a normalization model. A comprehension model may be used to identify instructions associated with the sentence structures and generate sentences based on the instructions and the sentence structures. An enhancement model may be used to identify enhancements to apply to the sentences. The enhancements may be presented alongside sentences generated by the comprehension model to provide the user an option to select either the enhancements or the sentences.

Abstract: A computer-implemented process is programmed to process a source input, determine text enhancements, and present the text enhancements to apply to the sentences dictated from the source input. A text processor may use machine-learning models to process an audio input to generate sentences in a presentable format. An audio input can be processed by an automatic speech recognition model to generate electronic text. The electronic text may be used to generate sentence structures using a normalization model. A comprehension model may be used to identify instructions associated with the sentence structures and generate sentences based on the instructions and the sentence structures. An enhancement model may be used to identify enhancements to apply to the sentences. The enhancements may be presented alongside sentences generated by the comprehension model to provide the user an option to select either the enhancements or the sentences.

Abstract: A computing system generates a default view of content items associated with a user account. The default view is representative of an underlying hierarchical structure of the content items associated with the user account. The computing system receives a request to transition from the default view to a modified view of the content items. The computing system ranks the content items based on a predicted likelihood of the user interacting with the particular content item. The computing system identifies the subset of content items for the modified view based on the ranking. The computing system generates the modified view based on the identified subset of the content items. The modified view maintains the underlying hierarchical structure of the content items associated with the user account. The computing system provides the modified view to the user associated with the user account for display.

Abstract: Methods and apparatuses for the analysis of mass spectroscopic (MS) data as well as ultraviolet (UV) absorption data. In particular, described herein are tools and methods to assist in generating annotated peptide maps from a raw MS data.

Abstract: Methods and apparatuses for the analysis of mass spectroscopic (MS) data as well as ultraviolet (UV) absorption data. In particular, described herein are tools and methods to assist in generating annotated peptide maps from a raw MS data.