Abstract: The present disclosure relates to methods for the treatment of diseases or disorders (e.g., cancer) with mTOR inhibitors. Specifically, the disclosure relates to methods of treating a subject having a cancer by administering a particular dosage of an mTOR inhibitor. In some embodiments this disclosure includes methods for delaying, preventing, or treating acquired resistance to RAS inhibitors using a dosage of an mTOR inhibitor. In some embodiments, this disclosure relates to methods of treating or preventing adverse events associated with administration of an mTOR inhibitor using tacrolimus.

Abstract: The present disclosure relates to methods for the treatment of diseases or disorders (e.g., cancer) with mTOR inhibitors. Specifically, the disclosure relates to methods of treating a subject having a cancer by administering a particular dosage of an mTOR inhibitor. In some embodiments this disclosure includes methods for delaying, preventing, or treating acquired resistance to RAS inhibitors using a dosage of an mTOR inhibitor. In some embodiments, this disclosure relates to methods of treating or preventing adverse events associated with administration of an mTOR inhibitor using tacrolimus.

Abstract: Techniques are presented to provide personalized search results to a user. A user history optionally classified into projects or tasks is determined. A profile for the user is determined by identifying keywords, concepts or other user and/or task specific descriptors within user history documents. A proximal neighborhood based on the user's history and adjustable crawling parameters is determined. The adjustable crawling parameters define which documents linked to documents in the user history are included within the proximal neighborhood. A user query is determined and used to search the set of documents within the proximal neighborhood. The results are then ranked based on the user profile information. The ranked results are optionally displayed to the user.

Abstract: Techniques for determining user types based on multi-modal clustering are provided. The topology, content and usage of a document collection or web site is determined. The user paths are identified using longest repeating subsequence techniques and a multi-modal information need vector is determined for each significant user path. Multi-modal vectors for each document in the significant path, content, uniform resource locators, inlink and outlink multi-modal vectors are determined and combined based on path position and access frequency. Multi-modal clustering is performed based on a multi-modal similarity function and a specified measure of similarity using a type of multi-modal clustering such as K-means or wavefront clustering. The identified clusters may be further analyzed based on changes to the weighting of the corresponding content, url, inlinks and outlinks multi-modal feature vectors.

Abstract: Techniques for determining user types based on multi-modal clustering are provided. The topology, content and usage of a document collection or web site is determined. The user paths are identified using longest repeating subsequence techniques and a multi-modal information need vector is determined for each significant user path. Multi-modal vectors for each document in the significant path, content, uniform resource locators, inlink and outlink multi-modal vectors are determined and combined based on path position and access frequency. Multi-modal clustering is performed based on a multi-modal similarity function and a specified measure of similarity using a type of multi-modal clustering such as K-means or wavefront clustering. The identified clusters may be further analyzed based on changes to the weighting of the corresponding content, url, inlinks and outlinks multi-modal feature vectors.

Abstract: A system and method for determining a quantitative measure of qualitative usability of related Web pages. Web pages are accepted that each include at least one hyperlink referencing and proximal cues relating to distal content included in another Web page. An information goal identifying a target Web page is specified. An activation network is formed. A directed graph including nodes corresponding to the Web pages and arcs corresponding to the hyperlinks is built. A weight is assigned to each arc to represent a probability of traversal of the corresponding hyperlink based on a relatedness of keywords in the information goal to the proximal cues included in the referenced Web page. A traversal through the activation network to the node corresponding to the target Web page is evaluated as a quantitative measure of usability.

Abstract: A system and method for determining a quantitative measure of search efficiency of related Web pages. An information goal is specified. A target Web page is identified within a plurality of Web pages. The information goal is searched via a search function in the Web pages to identify potential Web pages that include at least one hyperlink referencing and proximal cues relating to distal content included in another potential Web page. An activation network is formed. A directed graph is built, including nodes corresponding to the potential Web pages and arcs corresponding to the hyperlinks. A weight is assigned to each arc to represent a probability of traversal of the corresponding hyperlink based on a relatedness of keywords in the information goal to the proximal cues. A traversal through the activation network to the node corresponding to the target Web page is evaluated as a quantitative measure of search efficiency.

Abstract: Techniques are presented to provide personalized search results to a user. A user history optionally classified into projects or tasks is determined. A profile for the user is determined by identifying keywords, concepts or other user and/or task specific descriptors within user history documents. A proximal neighborhood based on the user's history and adjustable crawling parameters is determined. The adjustable crawling parameters define which documents linked to documents in the user history are included within the proximal neighborhood. A user query is determined and used to search the set of documents within the proximal neighborhood. The results are then ranked based on the user profile information. The ranked results are optionally displayed to the user.

Abstract: A method is disclosed for automatically performing conceptual highlighting of electronic text. User's interests can be explicitly determined via keywords that the user specifies, and/or are implicitly constructed from user browsing and reading activity. User interests may be expressed as an interest profile. Conceptual keywords related to user interests are selected by combining spreading activation and word co-occurrence, by latent semantic analysis, or other methods. The invention automatically highlights sentences and other information that contain conceptual keywords related to user interests. Highlights can be activated when the user directly performs a keyword search or index search, or the invention can generate information reflecting user interests, apply it to the text, and generate and display highlights. An algorithm is disclosed for computing a conceptual keyword vector through an iterative spreading activation process also employing word co-occurrence.

Abstract: To annotate a three-dimensional electronic object, e.g., document, a user specifies, on a two-dimensional screen, a portion of a page of a three-dimensional document as a specified page area to be annotated by making a stroke. The annotation may be displayed to the user by a hybrid technique where the annotation is displayed by a 3D polyline segment placed behind the near clipping plane of a virtual camera frustrum. At the same time, previous annotations are displayed by another technique, such as, for example, the texture coloring technique. During the intermittent time between the stroke and another stroke the 3D polyline segment is removed from behind the near clipping plane and the page texture is updated with the annotation data. The display techniques support highlighting annotations, free-form annotations, and text annotations.

Abstract: Techniques for determining user types based on multi-modal clustering are provided. The topology, content and usage of a document collection or web site is determined. The user paths are identified using longest repeating subsequence techniques and a multi-modal information need vector is determined for each significant user path. Multi-modal vectors for each document in the significant path, content, uniform resource locators, inlink and outlink multi-modal vectors are determined and combined based on path position and access frequency. Multi-modal clustering is performed based on a multi-modal similarity function and a specified measure of similarity using a type of multi-modal clustering such as K-means or wavefront clustering. The identified clusters may be further analyzed based on changes to the weighting of the corresponding content, url, inlinks and outlinks multi-modal feature vectors.