Facebook

About: Facebook is a company organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Artificial neural network & Language model. The organization has 7856 authors who have published 10906 publications receiving 570123 citations. The organization is also known as: facebook.com & FB.

The dichotomy of probabilistic inference for unions of conjunctive queries

Abstract: We study the complexity of computing a query on a probabilistic database. We consider unions of conjunctive queries, UCQ, which are equivalent to positive, existential First Order Logic sentences, and also to nonrecursive datalog programs. The tuples in the database are independent random events. We prove the following dichotomy theorem. For every UCQ query, either its probability can be computed in polynomial time in the size of the database, or is nP-hard. Our result also has applications to the problem of computing the probability of positive, Boolean expressions, and establishes a dichotomy for such classes based on their structure. For the tractable case, we give a very simple algorithm that alternates between two steps: applying the inclusion/exclusion formula, and removing one existential variable. A key and novel feature of this algorithm is that it avoids computing terms that cancel out in the inclusion/exclusion formula, in other words it only computes those terms whose Mobius function in an appropriate lattice is nonzero. We show that this simple feature is a key ingredient needed to ensure completeness. For the hardness proof, we give a reduction from the counting problem for positive, partitioned 2CNF, which is known to be nP-complete. The hardness proof is nontrivial, and combines techniques from logic, classical algebra, and analysis.

Using automated agents to facilitate chat communications

Abstract: An automated agent, such as an instant message robot, is be used to facilitate introduction of a chat participant to a small group of other chat participants in a chat room. To do so, for example, a BOT may present a chat participant who desires to be introduced to a small group of chat participants in a chat room with a series of multiple-choice questions, identify a subset of chat participants based on responses to the multiple-choice questions, and provide introductions among the chat participants in the subset to facilitate conversation therebetween. For example, the introductions provided by the BOT may indicate areas of mutual interest among chat participants in the subset, similar responses to one or more multiple-choice questions, and/or diverse responses to one or more multiple-choice questions.

Engineering Egress with Edge Fabric: Steering Oceans of Content to the World

Abstract: Large content providers build points of presence around the world, each connected to tens or hundreds of networks. Ideally, this connectivity lets providers better serve users, but providers cannot obtain enough capacity on some preferred peering paths to handle peak traffic demands. These capacity constraints, coupled with volatile traffic and performance and the limitations of the 20 year old BGP protocol, make it difficult to best use this connectivity.We present Edge Fabric, an SDN-based system we built and deployed to tackle these challenges for Facebook, which serves over two billion users from dozens of points of presence on six continents. We provide the first public details on the connectivity of a provider of this scale, including opportunities and challenges. We describe how Edge Fabric operates in near real-time to avoid congesting links at the edge of Facebook's network. Our evaluation on production traffic worldwide demonstrates that Edge Fabric efficiently uses interconnections without congesting them and degrading performance. We also present real-time performance measurements of available routes and investigate incorporating them into routing decisions. We relate challenges, solutions, and lessons from four years of operating and evolving Edge Fabric.

Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook

Abstract: We demonstrate that social media data represent a useful resource for testing models of legislative and individual-level political behavior and attitudes. First, we develop a model to estimate the ideology of politicians and their supporters using social media data on individual citizens’ endorsements of political figures. Our measure allows us to place politicians and more than 6 million citizens who are active in social media on the same metric. We validate the ideological estimates that result from the scaling process by showing they correlate highly with existing measures of ideology from Congress, and with individual-level self-reported political views. Finally, we use these measures to study the relationship between ideology and age, social relationships and ideology, and the relationship between friend ideology and turnout.

State of the Art on Neural Rendering

Abstract: Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic images from hand-crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo-realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state-of-the-art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photo-realistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. This state-of-the-art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems.