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Menlo Park, CALIFORNIA, United States

Facebook is an online social networking service headquartered in Menlo Park, California. Its website was launched on February 4, 2004, by Mark Zuckerberg with his college roommates and fellow Harvard University students Eduardo Saverin, Andrew McCollum, Dustin Moskovitz and Chris Hughes. The founders had initially limited the website's membership to Harvard students, but later expanded it to colleges in the Boston area, the Ivy League, and Stanford University. It gradually added support for students at various other universities and later to high-school students. Facebook now allows anyone who claims to be at least 13 years old to become a registered user of the website. Its name comes from a colloquialism for the directory given to it by American universities students.After registering to use the site, users can create a user profile, add other users as "friends", exchange messages, post status updates and photos, share videos and receive notifications when others update their profiles. Additionally, users may join common-interest user groups, organized by workplace, school or college, or other characteristics, and categorize their friends into lists such as "People From Work" or "Close Friends". Facebook had over 1.3 billion active users as of June 2014. Due to the large volume of data collected about users, the service's privacy policies have faced scrutiny, among other criticisms. Facebook, Inc. held its initial public offering in February 2012 and began selling stock to the public three months later, reaching a peak market capitalization of $104 billion. Wikipedia.


Lecun Y.,Facebook | Lecun Y.,New York University | Bengio Y.,University of Montreal | Hinton G.,Google | Hinton G.,Kings College
Nature | Year: 2015

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. © 2015 Macmillan Publishers Limited. All rights reserved. Source


Wu Y.,Facebook
IEEE Transactions on Information Theory | Year: 2011

In a distributed storage system based on erasure coding, an important problem is the repair problem: If a node storing a coded piece fails, in order to maintain the same level of reliability, we need to create a new encoded piece and store it at a new node. This paper presents a construction of systematic (n,k)-MDS codes for 2k ≤ n that achieves the minimum repair bandwidth when repairing from k+1 nodes. © 2011 IEEE. Source


Patent
Facebook | Date: 2016-01-11

In one embodiment, a method includes displaying a first content item on a screen of a computing device, the first content item occupying the entire screen and comprising an icon representing a second content item; and in response to a first user input, displaying a first animation sequence depicting the icon opening up to reveal the second content item, the second content item eventually replacing the first content item and occupying the entire screen. Then, in response to a second user input, the method further includes displaying a second animation sequence depicting the second content item closing down and returning to the icon included in the first content item, the second content item eventually disappearing from the screen.


Grant
Agency: GTR | Branch: EPSRC | Program: | Phase: Research Grant | Award Amount: 4.56M | Year: 2015

Data is everywhere, generated by increasing numbers of applications, devices and users, with few or no guarantees on the format, semantics, and quality. The economic potential of data-driven innovation is enormous, estimated to reach as much as £40B in 2017, by the Centre for Economics and Business Research. To realise this potential, and to provide meaningful data analyses, data scientists must first spend a significant portion of their time (estimated as 50% to 80%) on data wrangling - the process of collection, reorganising, and cleaning data. This heavy toll is due to what is referred as the four Vs of big data: Volume - the scale of the data, Velocity - speed of change, Variety - different forms of data, and Veracity - uncertainty of data. There is an urgent need to provide data scientists with a new generation of tools that will unlock the potential of data assets and significantly reduce the data wrangling component. As many traditional tools are no longer applicable in the 4 Vs environment, a radical paradigm shift is required. The proposal aims at achieving this paradigm shift by adding value to data, by handling data management tasks in an environment that is fully aware of data and user contexts, and by closely integrating key data management tasks in a way not yet attempted, but desperately needed by many innovative companies in todays data-driven economy. The VADA research programme will define principles and solutions for Value Added Data Systems, which support users in discovering, extracting, integrating, accessing and interpreting the data of relevance to their questions. In so doing, it uses the context of the user, e.g., requirements in terms of the trade-off between completeness and correctness, and the data context, e.g., its availability, cost, provenance and quality. The user context characterises not only what data is relevant, but also the properties it must exhibit to be fit for purpose. Adding value to data then involves the best efort provision of data to users, along with comprehensive information on the quality and origin of the data provided. Users can provide feedback on the results obtained, enabling changes to all data management tasks, and thus a continuous improvement in the user experience. Establishing the principles behind Value Added Data Systems requires a revolutionary approach to data management, informed by interlinked research in data extraction, data integration, data quality, provenance, query answering, and reasoning. This will enable each of these areas to benefit from synergies with the others. Research has developed focused results within such sub-disciplines; VADA develops these specialisms in ways that both transform the techniques within the sub-disciplines and enable the development of architectures that bring them together to add value to data. The commercial importance of the research area has been widely recognised. The VADA programme brings together university researchers with commercial partners who are in desperate need of a new generation of data management tools. They will be contributing to the programme by funding research staff and students, providing substantial amounts of staff time for research collaborations, supporting internships, hosting visitors, contributing challenging real-life case studies, sharing experiences, and participating in technical meetings. These partners are both developers of data management technologies (LogicBlox, Microsoft, Neo) and data user organisations in healthcare (The Christie), e-commerce (LambdaTek, PricePanda), finance (AllianceBernstein), social networks (Facebook), security (Horus), smart cities (FutureEverything), and telecommunications (Huawei).


Patent
Facebook | Date: 2016-01-07

In one embodiment, a method includes accessing a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each node corresponding to a user of an online social network, identifying a plurality of clusters in the social graph using graph clustering, providing a treatment to a first set of users based on the clusters, and determining a treatment effect treatment for the users in the first set based on a network exposure to the treatment for each user.

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