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 Q]: In many domains it is desirable to assess the pref­ erences of users in a qualitative rather than quan­ titative way. Such representations of qualitative preference orderings form an important compo­ nent of automated decision tools. We propose a graphical representation of preferences that re­ flects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a rep­ resentation is often compact and arguably natural. We describe several search algorithms for domi­ nance testing based on this representation; these algorithms are quite effective, especially in spe­ cific network topologies, such as chainand tree­ structured networks, as well as polytrees.
[EX A]: Reasoning With Conditional Ceteris Paribus Preference Statements

[EX Q]: Chinese word segmentation is a fundamental task for Chinese language processing. The granularity mismatch problem is the main cause of the errors. This paper showed that the binary tree representation can store outputs with different granularity. A binary tree based framework is also designed to overcome the granularity mismatch problem. There are two steps in this framework, namely tree building and tree pruning. The tree pruning step is specially designed to focus on the granularity problem. Previous work for Chinese word segmentation such as the sequence tagging can be easily employed in this framework. This framework can also provide quantitative error analysis methods. The experiments showed that after using a more sophisticated tree pruning function for a state-of-the-art conditional random field based baseline, the error reduction can be up to 20%.
[EX A]: Binary Tree based Chinese Word Segmentation

[EX Q]: MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines.
[EX A]:
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems