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.
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Question: In this paper we present an Action Language-Answer Set Programming based approach to solving planning and scheduling problems in hybrid domains domains that exhibit both discrete and continuous behavior. We use action language H to represent the domain and then translate the resulting theory into an A-Prolog program. In this way, we reduce the problem of finding solutions to planning and scheduling problems to computing answer sets of A-Prolog programs. We cite a planning and scheduling example from the literature and show how to model it in H. We show how to translate the resulting H theory into an equivalent A-Prolog program. We compute the answer sets of the resulting program using a hybrid solver called EZCSP which loosely integrates a constraint solver with an answer set solver. The solver allows us reason about constraints over reals and compute solutions to complex planning and scheduling problems. Results have shown that our approach can be applied to any planning and scheduling problem in hybrid domains.

Answer: Planning and Scheduling in Hybrid Domains Using Answer Set Programming


Question: In this paper we examine the benefit of performing named entity recognition (NER) and co-reference resolution to an English and a Greek corpus used for text segmentation. The aim here is to examine whether the combination of text segmentation and information extraction can be beneficial for the identification of the various topics that appear in a document. NER was performed manually in the English corpus and was compared with the output produced by publicly available annotation tools while, an already existing tool was used for the Greek corpus. Produced annotations from both corpora were manually corrected and enriched to cover four types of named entities. Co-reference resolution i.e., substitution of every reference of the same instance with the same named entity identifier was subsequently performed. The evaluation, using five text segmentation algorithms for the English corpus and four for the Greek corpus leads to the conclusion that, the benefit highly depends on the segment’s topic, the number of named entity instances appearing in it, as well as the segment’s length.

Answer: Text Segmentation using Named Entity Recognition and Co-reference Resolution in English and Greek Texts


Question: Learning neural network architectures is a way to discover new highly predictive models. We propose to focus on this problem from a different perspective where the goal is to discover architectures efficient in terms of both prediction quality and computation cost, e.g time in milliseconds, number of operations... For instance, our approach is able to solve the following task: find the best neural network architecture (in a very large set of possible architectures) able to predict well in less than 100 milliseconds on my mobile phone. Our contribution is based on a new family of models called Budgeted Super Networks that are learned using reinforcement-learning inspired techniques applied to a budgeted learning objective function which includes the computation cost during disk/memory operations at inference. We present a set of experiments on computer vision problems and show the ability of our method to discover efficient architectures in terms of both predictive quality and computation time.

Answer:
Learning Time-Efficient Deep Architectures with Budgeted Super Networks