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: 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.
Solution is here: A Neural Local Coherence Model
Explanation: 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

Now, solve this: <lb>We study the best arm identification (Best-1-Arm) problem, which is defined as follows.<lb>We are given n stochastic bandit arms. The ith arm has a reward distribution<lb>Di with an<lb>unknown mean i. Upon each play of the ith arm, we can get a reward, sampled i.i.d. from<lb>Di. We would like to identify the arm with the largest mean with probability at least 1 ,<lb>using as few samples as possible. We provide a nontrivial algorithm for Best-1-Arm, which<lb>improves upon several prior upper bounds on the same problem. We also study an important<lb>special case where there are only two arms, which we call the Sign- problem. We provide a<lb>new lower bound of Sign-, simplifying and significantly extending a classical result by Farrell<lb>in 1964, with a completely new proof. Using the new lower bound for Sign-, we obtain the<lb>first lower bound for Best-1-Arm that goes beyond the classic Mannor-Tsitsiklis lower bound,<lb>by an interesting reduction from Sign- to Best-1-Arm. We propose an interesting conjecture<lb>concerning the optimal sample complexity of Best-1-Arm from the perspective of instance-wise<lb>optimality.
Solution:
On the Optimal Sample Complexity for Best Arm Identification