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.

MegaPipe: a new programming interface for scalable network I/O

Abstract: We present MegaPipe, a new API for efficient, scalable network I/O for message-oriented workloads. The design of MegaPipe centers around the abstraction of a channel - a per-core, bidirectional pipe between the kernel and user space, used to exchange both I/O requests and event notifications. On top of the channel abstraction, we introduce three key concepts of MegaPipe: partitioning, lightweight socket (lwsocket), and batching.We implement MegaPipe in Linux and adapt memcached and nginx. Our results show that, by embracing a clean-slate design approach, MegaPipe is able to exploit new opportunities for improved performance and ease of programmability. In microbenchmarks on an 8-core server with 64 B messages, MegaPipe outperforms baseline Linux between 29% (for long connections) and 582% (for short connections). MegaPipe improves the performance of a modified version of memcached between 15% and 320%. For a workload based on real-world HTTP traces, MegaPipe boosts the throughput of nginx by 75%.

Pushing podcasts to mobile devices

Abstract: The present invention relates to a system and method for pushing podcasts to mobile devices, such as cellular phones, from a remote subscription management system A subscription management system is described that is adapted to retrieve episodes from one or more remote computing devices and transmit retrieved episodes to a mobile device over a wireless network The transmissions are made via a telephone number associated with the mobile device The system includes a datastore, in communication with the server, containing at least one telephone number of a mobile device associated with a user and at least one podcast subscription associated with the user The podcast lists episodes that are located on one or more of the remote computing devices When a search module identifies a new episode, the system retrieves the new episode and transmits it to the mobile device using the telephone number to address the transmission

Graphical user interface to manipulate syndication data feeds

Abstract: A pipe specification editor is provided to configure a plurality of modules for processing a syndication data feed. The editor is operable to provide a graphical user interface to receive a user specification of a plurality of modules and to receive a user specification of wires. Each module is characterized by at least one of a group consisting of an input node and an output node, wherein the input node, if present, is configured to input a syndication data feed and the output node, if present, is configured to output a syndication data feed. At least one of the modules is a module configured to retrieve a source syndication data feed. The wires are configured to provide a syndication data feed provided from an output node of a module to an input node of another module.

Cluster Canonical Correlation Analysis

Abstract: In this paper we present cluster canonical correlation analysis (cluster-CCA) for joint dimensionality reduction of two sets of data points. Unlike the standard pairwise correspondence between the data points, in our problem each set is partitioned into multiple clusters or classes, wheretheclass labelsdefinecorrespondencesbetween the sets. Cluster-CCA is able to learn discriminant low dimensional representations that maximize the correlation between the two sets while segregating the different classes on the learned space. Furthermore, we present a kernel extension, kernel cluster canonical correlation analysis (cluster-KCCA) that extends clusterCCA to account for non-linear relationships. Cluster-(K)CCA is shown to be computationally efficient, the complexity being similar to standard (K)CCA. By means of experimental evaluation on benchmark datasets, cluster-(K)CCA is shown to achieve state of the art performance for cross-modal retrieval tasks.

Superposter behavior in MOOC forums

Abstract: Discussion forums, employed by MOOC providers as the primary mode of interaction among instructors and students, have emerged as one of the important components of online courses. We empirically study contribution behavior in these online collaborative learning forums using data from 44 MOOCs hosted on Coursera, focusing primarily on the highest-volume contributors---"superposters"---in a forum. We explore who these superposters are and study their engagement patterns across the MOOC platform, with a focus on the following question---to what extent is superposting a positive phenomenon for the forum? Specifically, while superposters clearly contribute heavily to the forum in terms of quantity, how do these contributions rate in terms of quality, and does this prolific posting behavior negatively impact contribution from the large remainder of students in the class?We analyze these questions across the courses in our dataset, and find that superposters display above-average engagement across Coursera, enrolling in more courses and obtaining better grades than the average forum participant; additionally, students who are superposters in one course are significantly more likely to be superposters in other courses they take. In terms of utility, our analysis indicates that while being neither the fastest nor the most upvoted, superposters' responses are speedier and receive more upvotes than the average forum user's posts; a manual assessment of quality on a subset of this content supports this conclusion that a large fraction of superposter contributions indeed constitute useful content. Finally, we find that superposters' prolific contribution behavior does not `drown out the silent majority'---high superposter activity correlates positively and significantly with higher overall activity and forum health, as measured by total contribution volume, higher average perceived utility in terms of received votes, and a smaller fraction of orphaned threads.