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.
One example is below.
Q: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
A: A Neural Local Coherence Model
Rationale: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage
Q: We consider an Erdos-Renyi graph with n nodes and edge probability q that is embedded with a random subgraph of size K with edge probabilities p such that p > q. We address the problem of detecting the subgraph nodes when only the graph edges are observed, along with some extra knowledge of a small fraction of subgraph nodes, called cued vertices or cues. We employ a local and distributed algorithm called belief propagation (BP). Recent works on subgraph detection without cues have shown that global maximum likelihood (ML) detection strictly outperforms BP in terms of asymptotic error rate, namely, there is a threshold condition that the subgraph parameters should satisfy below which BP fails in achieving asymptotically zero error, but ML succeeds. In contrast, we show that when the fraction of cues is strictly bounded away from zero, i.e., when there exists non-trivial side-information, BP achieves zero asymptotic error even below this threshold, thus approaching the performance of ML detection. Key-words: Belief Propagation, Subgraph Detection, Semisupervised Learning, Random Graphs Corresponding author, arun.kadavankandy@inria.fr La Detection de Sousgraphes en presence des indices grace au Belief Propagation Resume : Nous considerons un graphe Erdos-Renyi qui a n sommets dont q est la probabilite d'arretes. La dessus il y un sousgraphe place sur leurs m sommets selectionnes aleatoirement et leur probabilite d'arretes est p, en sorte que p > q. Nous proposons un algorithme distribue aux calculs locales a chaque sommet, tire du "Belief Propagation" (BP), qui detecte les sommets du sousgraphe, quand on connait une fraction de sommets du sousgraphe en tant qu'indices. Des recherches recentes ont prouve que la prestation du BP dans l'absence des indices est strictement inferior par rapport a la detection globale du maximum de vraisemblance (DMV). A l'oppose, ici on prouve qu'en presence des indices, la prestation du BP est a l'hauteur de celle de DMV, dans la sens ou le premier reussie a detecter la sousgraphe avec une erreur qui tend a zero, a chaque fois le dernier peut le faire, dans la limite ou le nombre de sommets du graph tend l'infinite. Mots-cles : Belief Propagation, Detection de Sousgraphes, Semisupervised Learning, Graphes Aleatoires Subgraph Detection with cues using Belief Propagation 3
A:
Subgraph Detection with cues using Belief Propagation