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.

Q: The interest in the combination of probability with logics for modeling the world has rapidly increased in the last few years. One of the most effective approaches is the Distribution Semantics which was adopted by many logic programming languages and in Descripion Logics. In this paper, we illustrate the work we have done in this research field by presenting a probabilistic semantics for description logics and reasoning and learning algorithms. In particular, we present in detail the system TRILL , which computes the probability of queries w.r.t. probabilistic knowledge bases, which has been implemented in Prolog. Note: An extended abstract / full version of a paper accepted to be presented at the Doctoral Consortium of the 30th International Conference on Logic Programming (ICLP 2014), July 19-22, Vienna, Austria

A: Reasoning with Probabilistic Logics
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Q: The Complete Tang Poems (CTP) is the most important source to study Tang poems. We look into CTP with computational tools from specific linguistic perspectives, including distributional semantics and collocational analysis. From such quantitative viewpoints, we compare the usage of "wind" and "moon" in the poems of Li Bai () and Du Fu (). Colors in poems function like sounds in movies, and play a crucial role in the imageries of poems. Thus, words for colors are studied, and "" (bai2, white) is the main focus because it is the most frequent color in CTP. We also explore some cases of using colored words in antithesis() pairs that were central for fostering the imageries of the poems. CTP also contains useful historical information, and we extract person names in CTP to study the social networks of the Tang poets. Such information can then be integrated with the China Biographical Database of Harvard University.

A: Color Aesthetics and Social Networks in Complete Tang Poems: Explorations and Discoveries
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Q: We consider the problem of proper learning a Boolean Halfspace with integer weights {0, 1, . . . , t} from membership queries only. The best known algorithm for this problem is an adaptive algorithm that asks n ) membership queries where the best lower bound for the number of membership queries is n [4]. In this paper we close this gap and give an adaptive proper learning algorithm with two rounds that asks n membership queries. We also give a non-adaptive proper learning algorithm that asks n ) membership queries.

A:
Learning Boolean Halfspaces with Small Weights from Membership Queries
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