Abstract: An output buffer circuit is provided. The output buffer circuit receives a control signal (OE) and a data signal (Dout) from a first core circuit (10) and operates in a transmitting mode according to the control signal. The output buffer circuit converts the data signal into an output signal at a first voltage level or a ground voltage level according to the data signal logic level and a supply voltage (VDDIO). The supply voltage is adjusted to pull up or pull down the first voltage level of the output signal.

Abstract: A method and system for detecting events based on query-page relationships is provided. The event detection system detects events by analyzing occurrences of query-page pairs generated from a user selecting the page of the pair from a search result for the query of the pair. The event detection system may identify semantic and temporal similarity between query-page pairs. The event detection system then identifies clusters of query-page pairs that are semantically and temporally similar.

Abstract: An input output device coupled between a core circuit and a pad and including an output cell, an input cell, and a pre-driver. The output cell includes an output stage and a voltage level converter. The output stage includes a first transistor and a second transistor connected to the first transistor in serial between a first supply voltage and a second voltage. The voltage level converter generates a first gate voltage to the first transistor according to the first voltage and a data signal. When the first supply voltage is increased, the first gate voltage is increased. When the data signal is at a high level, the first transistor is turned on. The input cell includes a pull unit and a first N-type transistor. The pre-driver turns off the first and the second transistors.

Abstract: Queries describe users' search needs and therefore they play a role in the context of learning to rank for information retrieval and Web search. However, most existing approaches for learning to rank do not explicitly take into consideration the fact that queries vary significantly along several dimensions and require different objectives for the ranking models. The technique described herein incorporates query difference into learning to rank by introducing query-dependent loss functions. Specifically, the technique employs query categorization to represent query differences and employs specific query-dependent loss functions based on such kind of query differences. The technique employs two learning methods. One learns ranking functions with pre-defined query difference, while the other one learns both of them simultaneously.

Abstract: A method and system for distributed training of a hierarchical classifier for classifying documents using a classification hierarchy is provided. A training system provides training data that includes the documents and classifications of the documents within the classification hierarchy. The training system distributes the training of the classifiers of the hierarchical classifier to various agents so that the classifiers can be trained in parallel. For each classifier, the training system identifies an agent that is to train the classifier. Each agent then trains its classifiers.

Abstract: The page ranking technique described herein employs a Markov Skeleton Mirror Process (MSMP), which is a particular case of Markov Skeleton Processes, to model and calculate page importance scores. Given a web graph and its metadata, the technique builds an MSMP model on the web graph. It first estimates the stationary distribution of a EMC and views it as transition probability. It next computes the mean staying time using the metadata. Finally, it calculates the product of transition probability and mean staying time, which is actually the stationary distribution of MSMP. This is regarded as page importance.

Abstract: A method and system for generating a ranking function using a fidelity-based loss between a target probability and a model probability for a pair of documents is provided. A fidelity ranking system generates a fidelity ranking function that ranks the relevance of documents to queries. The fidelity ranking system operates to minimize a fidelity loss between pairs of documents of training data. The fidelity loss may be derived from “fidelity” as used in the field of quantum physics. The fidelity ranking system may use a learning technique in conjunction with a fidelity loss when generating the ranking function. After the fidelity ranking system generates the fidelity ranking function, it uses the fidelity ranking function to rank the relevance of documents to queries.

Abstract: An output buffer circuit is provided. The output buffer circuit receives a control signal (OE) and a data signal (Dout) from a first core circuit (10) and operates in a transmitting mode according to the control signal. The output buffer circuit converts the data signal into an output signal at a first voltage level or a ground voltage level according to the data signal logic level and a supply voltage (VDDIO). The supply voltage is adjusted to pull up or pull down the first voltage level of the output signal.

Abstract: Described is a technology in which documents associated with a query are ranked by a ranking model that depends on the query. When a query is processed, a ranking model for the query is selected/determined based upon nearest neighbors to the query in query feature space. In one aspect, the ranking model is trained online, based on a training set obtained from a number of nearest neighbors to the query. In an alternative aspect, ranking models are trained offline using training sets; the query is used to find a most similar training set based on nearest neighbors of the query, with the ranking model that corresponds to the most similar training set being selected for ranking. In another alternative aspect, the ranking models are trained offline, with the nearest neighbor to the query determined and used to select its associated ranking model.

Abstract: A method and system for high-order co-clustering of objects of heterogeneous types is provided. A clustering system co-clusters objects of heterogeneous types based on joint distributions for objects of non-central types and objects of a central type. The clustering system uses an iterative approach to co-clustering the objects of the various types. The clustering system divides the co-clustering into a sub-problem, for each non-central type (e.g., first type and second type), of co-clustering objects of that non-central type and objects of the central type based on the joint distribution for that non-central type. After the co-clustering is completed, the clustering system clusters objects of the central type based on the clusters of the objects of the non-central types identified during co-clustering. The clustering system repeats the iterations until the clusters of objects of the central type converge on a solution.

Abstract: Procedures for learning and ranking items in a listwise manner are discussed. A listwise methodology may consider a ranked list, of individual items, as a specific permutation of the items being ranked. In implementations, a listwise loss function may be used in ranking items. A listwise loss function may be a metric which reflects the departure or disorder from an exemplary ranking for one or more sample listwise rankings used in learning. In this manner, the loss function may approximate the exemplary ranking for the plurality of items being ranked.

Abstract: A method and system for projecting queries and images into a similarity space where queries are close to their relevant images is provided. A similarity space projection (“SSP”) system learns a query projection function and an image projection function based on training data. The query projection function projects the relevance of the most relevant words of a query into a similarity space and the image projection function projects the relevance to an image of the most relevant words of a query into the same similarity space so that queries and their relevant images are close in the similarity space. The SSP system can then identify images that are relevant to a target query and queries that are relevant to a target image using the projection functions.

Abstract: The present invention provides techniques for generating data that is used for ranking documents. In one embodiment, a method involves the step of extracting data features from a number of documents to be ranked. The data features extracted from the documents are established in conjunction with a first feature map and a second feature map, wherein the first feature map and the second feature map are capable of keeping the relative ordering between two document instances. In one embodiment, the two feature maps are specially a divide feature map and a minus feature map. Once the data is mapped, the method involves the step of generating pairwise preferences from the first feature map and the second feature map. Then the pairwise preferences are aggregated into a total order, which can be used to produce one or more relevancy scores.

Abstract: The present invention introduces a new approach to learning systems. More specifically, the present invention provides learned methods for optimize ranking models. In one aspect of the present invention, an objective function is defined as the likelihood of ground truth based on a Luce model. In another aspect, techniques of the present invention provide a way of representing different kinds of ground truths as a constraint set of permutations. In yet another aspect of the present invention, techniques of the present invention provide a way of learning the model parameter by maximizing the likelihood of the ground truth.

Abstract: The present invention provides methods for improving a ranking model. In one embodiment, a method includes the step of obtaining queries, documents, and document labels. The process then initializes active sets using the document labels, wherein two active sets are established for each query, a perfect active set and an imperfect active set. Then, the process optimizes an empirical loss function by the use of the first and second active set, whereby parameters of the ranking model are modified in accordance to the empirical loss function. The method then updates the active sets with additional ranking data, wherein the updates are configured to work in conjunction with the optimized loss function and modified ranking model. The recalculated active sets provide an indication for ranking the documents in a way that is more consistent with the document metadata.

Abstract: The present invention provides an improved method for ranking documents using a ranking model. One embodiment employs Continuous Conditional Random Fields (CRF) as a model, which is a conditional probability distribution representing a mapping relationship from retrieved documents to their ranking scores. The model can naturally utilize features of the content information of documents as well as the relation information between documents for global ranking. The present invention also provides a learning algorithm for creating Continuous CRF. Also provided, the invention introduces Pseudo Relevance Feedback and Topic Distillation.

Abstract: Method for creating a graph representing web browsing behavior, including receiving web browsing behavior data from one or more web browsers; adding a node on the graph for each web page listed in the web browsing behavior data; adding a first link connecting two or more nodes on the graph, wherein the first link representing a hyperlink for accessing a webpage; calculating an amount of time in which each web page is being accessed; determining a number of units of time in the calculated amount of time; adding one or more virtual nodes to the graph based on the number of units of time; and adding a second link connecting two or more virtual nodes on the graph, wherein the second link representing a virtual hyperlink for accessing a webpage.

Abstract: Method for determining a webpage importance, including receiving web browsing behavior data of one or more users; creating a model of the web browsing behavior data; calculating a stationary probability distribution of the model; and correlating the stationary probability distribution to the webpage importance.

Abstract: A method and system for introducing spam into a search engine for testing purposes is provided. An active spam testing system receives from a tester a specification of spam that is to be introduced into the search engine for testing purposes. The testing system may then generate auxiliary data structures for storing indications of the spam that is to be introduced. A search engine has original data structures that may include a content index and a link data structure. The testing system stores the indications of the spam in the auxiliary data structures so that use of the search engine for non-testing purposes is not affected. When the search engine is used for testing purposes, the search engine generates search results based on a combination of the original data structures and the auxiliary data structures.

Abstract: A method and system for determining temporal importance of documents having links between documents based on a temporal analysis of the links is provided. A temporal ranking system collects link information or snapshots indicating the links between documents at various snapshot times. The temporal ranking system calculates a current temporal importance of a document by factoring in the current importance of the document derived from the current snapshot (i.e., with the latest snapshot time) and the historical importance of the document derived from the past snapshots. To calculate the current temporal importance of a web page, the temporal ranking system aggregates the importance of the web page for each snapshot.