Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

The ErbB receptors and their ligands in cancer: an overview.

Abstract: This review article provides an overview on the most recent advances on the role of ErbB receptors and growth factors of the epidermal growth factor (EGF)-family of peptides in cancer pathogenesis and progression. The ErbB tyrosine kinases and the EGF-like peptides form a complex system. In fact, the interactions occurring between receptors and ligands of these families affect the type and the duration of the intracellular signals that derive from receptor activation. Interestingly, activation of ErbB receptors is also driven by different classes of membrane receptor, suggesting that ErbB kinases can amplify growth promoting signals carried by different pathways. The importance of ErbB receptors and EGF-like peptides in development of organs and tissues has been demonstrated by using different mouse models. In vitro and in vivo studies have also shown that ErbB receptors and their ligands can act as transforming genes. However, evidence suggests that cooperation of different receptors and ligands is necessary to induce a fully transformed phenotype. Indeed, co-expression of different ErbB receptors and EGF-like growth factors is a common phenomenon in human primary carcinomas. This observation suggests that the growth and the survival of carcinoma cells is sustained by a network of receptors/ligands of the ErbB family. In this respect, the contemporary expression of different ErbB tyrosine kinases and/or EGF-like growth factors in human carcinomas might also affect tumor response to target based agents directed against the ErbB receptor/ligand system.

A Framework for Computing the Privacy Scores of Users in Online Social Networks

Abstract: A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their daily information-sharing activities.In this article, we approach the privacy issues raised in online social networks from the individual users’ viewpoint: we propose a framework to compute the privacy score of a user. This score indicates the user’s potential risk caused by his or her participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive information a user discloses, the higher his or her privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility.

Method of controlling an Internet browser interface and a controllable browser interface

Abstract: The present invention is directed to a method of dynamically controlling and displaying an Internet browser interface, and to a dynamically controllable Internet browser interface. In accordance with the present invention, a browser interface may be customized using a controlling software program that may be provided by an Internet content provider, an ISP, or that may reside on an Internet user's computer. The controlling software program enables the Internet user, the content provider, or the ISP to customize and control the information and/or functionality of a user's browser and browser interface.

Extraction and matching of characteristic fingerprints from audio signals

Abstract: An audio fingerprint is extracted from an audio sample, where the fingerprint contains information that is characteristic of the content in the sample. The fingerprint may be generated by computing an energy spectrum for the audio sample, resampling the energy spectrum logarithmically in the time dimension, transforming the resampled energy spectrum to produce a series of feature vectors, and computing the fingerprint using differential coding of the feature vectors. The generated fingerprint can be compared to a set of reference fingerprints in a database to identify the original audio content.

"I'm eating a sandwich in Glasgow": modeling locations with tweets

Abstract: Social media such as Twitter generate large quantities of data about what a person is thinking and doing in a particular location. We leverage this data to build models of locations to improve our understanding of a user's geographic context. Understanding the user's geographic context can in turn enable a variety of services that allow us to present information, recommend businesses and services, and place advertisements that are relevant at a hyper-local level.In this paper we create language models of locations using coordinates extracted from geotagged Twitter data. We model locations at varying levels of granularity, from the zip code to the country level. We measure the accuracy of these models by the degree to which we can predict the location of an individual tweet, and further by the accuracy with which we can predict the location of a user. We find that we can meet the performance of the industry standard tool for predicting both the tweet and the user at the country, state and city levels, and far exceed its performance at the hyper-local level, achieving a three- to ten-fold increase in accuracy at the zip code level.