We propose a novel network-based approach for location estimation in social media that integrates evidence of the social tie strength between users for improved location estimation. Concretely, we propose a location estimator -FriendlyLocation -that leverages the relationship between the strength of the tie between a pair of users, and the distance between the pair. Based on an examination of over 100 million geoencoded tweets and 73 million Twitter user profiles, we identify several factors such as the number of followers and how the users interact that can strongly reveal the distance between a pair of users. We use these factors to train a decision tree to distinguish between pairs of users who are likely to live nearby and pairs of users who are likely to live in different areas. We use the results of this decision tree as the input to a maximum likelihood estimator to predict a user's location. We find that this proposed method significantly improves the results of location estimation relative to a stateof-the-art technique. Our system reduces the average error distance for 80% of Twitter users from 40 miles to 21 miles using only information from the user's friends and friendsof-friends, which has great significance for augmenting traditional social media and enriching location-based services with more refined and accurate location estimates.

We have fabricated strained Ge channel ptype metal-oxide-semiconductor field-effect transistors (p-MOSFETs) on Si 0.3 Ge 0.7 virtual substrates. The poor interface between silicon dioxide (SiO 2) and the Ge channel was eliminated by capping the strained Ge layer with a relaxed, epitaxial silicon surface layer grown at 400ºC. Ge p-MOSFETs fabricated from this structure show a hole mobility enhancement of nearly 8 times that of co-processed bulk Si devices, and the Ge MOSFETs have a peak effective mobility of 1160 cm 2 /V-s. These MOSFETs demonstrate the possibility of creating a surface channel enhancement mode MOSFET with buried channellike transport characteristics.

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Highly dynamic real-time microblog systems have already published petabytes of real-time human sensor data in the form of status updates. However, the lack of user adoption of geo-based features per user or per post signals that the promise of microblog services as location-based sensing systems may have only limited reach and impact. Thus, in this article, we propose and evaluate a probabilistic framework for estimating a microblog user's location based purely on the content of the user's posts. Our framework can overcome the sparsity of geo-enabled features in these services and bring augmented scope and breadth to emerging location-based personalized information services. Three of the key features of the proposed approach are: (i) its reliance purely on publicly available content; (ii) a classification component for automatically identifying words in posts with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. On average we find that the location estimates converge quickly, placing 51% of users within 100 miles of their actual location.

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