In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
Knowing the largest rate at which data can be sent on an end-to-end path such that the egress rate is equal to the ingress rate with high probability can be very practical when choosing transmission rates in video streaming or selecting peers in peer-to-peer applications. We introduce probabilistic available bandwidth, which is defined in terms of ingress rates and egress rates of traffic on a path, rather than in terms of capacity and utilization of the constituent links of the path like the standard available bandwidth metric. In this paper, we describe a distributed algorithm, based on a probabilistic graphical model and Bayesian active learning, for simultaneously estimating the probabilistic available bandwidth of multiple paths through a network. Our procedure exploits the fact that each packet train provides information not only about the path it traverses, but also about any path that shares a link with the monitored path. Simulations and PlanetLab experiments indicate that this process can dramatically reduce the number of probes required to generate accurate estimates.

Ex Output:
Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning


Ex Input:
We develop a probabilistic latent-variable model to discover semantic frames—types of events and their participants—from corpora. We present a Dirichlet-multinomial model in which frames are latent categories that explain the linking of verb-subject-object triples, given document-level sparsity. We analyze what the model learns, and compare it to FrameNet, noting it learns some novel and interesting frames. This document also contains a discussion of inference issues, including concentration parameter learning; and a small-scale error analysis of syntactic parsing accuracy. Note: this work was originally posted online October 2012 as part of CMU MLD’s Data Analysis Project requirement. This version has no new experiments or results, but has added some discussion of new related work.

Ex Output:
Learning Frames from Text with an Unsupervised Latent Variable Model


Ex Input:
Annotating semantic data with metadata is becoming more and more important to provide information about the statements being asserted. While initial solutions proposed a data model to represent a specific dimension of metainformation (such as time or provenance), the need for a general annotation framework which allows representing different context dimensions is needed. In this paper, we extend the 4dFluents ontology by Welty and Fikes—on associating temporal validity to statements—to any dimension of context, and discuss possible issues that multidimensional context representations have to face and how we address them.

Ex Output:

NdFluents: A Multi-dimensional Contexts Ontology