Explore the latest research from Facebook

This paper presents the Jump-Start mechanism implemented inside the HipHop Virtual Machine (HHVM). Jump-Start is a practical approach to share VM profile data at a large scale, being used to power one of the largest websites in the world.

This paper describes Tectonic’s design, explaining how it achieves scalability, supports multitenancy, and allows tenants to specialize operations to optimize for diverse workloads. The paper also presents insights from designing, deploying, and operating Tectonic.

To understand the effects of demographic attributes on attacker behavior in stolen social accounts, we devised a method to instrument and monitor such accounts. We then created, instrumented, and deployed more than 1000 Facebook accounts, and exposed them to criminals.

We present a feasible condition for the use of diffractive NLOS, for a single obstacle with shallow diffraction angle, modest foliage loss, and moderate foliage, which we call NLOSv1.

In this paper, we take the first step towards automating this process, with the view of producing models which train faster and more robustly. Concretely, we present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures.

We propose a novel framework to identify subgoals useful for exploration in sequential decision making tasks under partial observability.

We consider a multi-agent framework for distributed optimization where each agent has access to a local smooth strongly convex function, and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents’ local functions. We propose an algorithm wherein each agent operates asynchronously and independently of the other agents.

Using the recently deployed Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities. In both simulations and real experiments we match potential donors with opportunities, guided by a machine learning model trained on prior observations of donor behavior.

This paper is the first of a two-part series that discusses a numerical methodology that relies on the concept of cumulative equivalent exposure to evaluate contact burn injury thresholds.

This paper is the second of a two-part series that discusses a numerical methodology that relies on the concept of cumulative equivalent exposure to evaluate contact burn injury thresholds. In Part I, the effect of a finite thermal mass is analyzed for an infinite plate of several finite thicknesses. In Part II, the sensitivities to object shape, size, thickness, contact resistance and applied heat flux are considered.