You will be shown an abstract from a computer science research paper. Given this abstract, your task is to extract all unique entities with the following types:

- "Task": Applications, problems to solve, systems to construct. Examples include "information extraction", "machine reading system", "image segmentation".
- "Method": : Methods, models, systems to use, or tools, components of a system, frameworks. Examples include "language model", "CORENLP", "POS parser".
- "Metric": Metrics, measures, or entities that can express quality of a system / method. Examples include "F1", "BLEU", "Precision", "time complexity".
- "Material": Data, datasets, resources, Corpus, Knowledge base. Examples include "image data", "speech data", "stereo images", "CoNLL", "Wikipedia".
- "OtherScientificTerm": Phrases that are a scientific terms but do not fall into any of the above classes. Examples include "physical or geometric constraints", "qualitative prior knowledge", "tree kernel", "noise".
- "Generic": General terms or pronouns that may refer to a entity but are not themselves informative, often used as connection words. Examples include "model", "approach", "them".

Please return the output as a JSON object of the format: {"type1" : ["example_entity", ...], "type2" : ["example_entity", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. Entity types with no matching entities should be assigned an empty array [].

For instance, the output might look like: {"Task": ["speech recognition", ...], "Method": ["Conditional random field"], "Material": [], ...}.

Only output the JSON object and do not include any additional text.

Abstract:

In some auction domains, there is uncertainty regarding the final availability of the goods being auctioned off. For example, a government may auction off spectrum from its public safety network, but it may need this spectrum back in times of emergency. In such a domain, standard combinatorial auctions perform poorly because they lead to violations of individual rationality (IR), even in expectation, and to very low efficiency. In this paper, we study the design of core-selecting payment rules for such domains. Surprisingly, we show that in this new domain, there does not exist a payment rule with is guaranteed to be ex-post core-selecting. However, we show that by designing rules that are 'execution-contingent,' i.e., by charging payments that are conditioned on the realization of the availability of the goods, we can reduce IR violations. We design two core-selecting rules that always satisfy IR in expectation. To study the performance of our rules we perform a computational Bayes-Nash equilibrium analysis. We show that, in equilibrium, our new rules have better incentives, higher efficiency, and a lower rate of ex-post IR violations than standard core-selecting rules.
{"Generic": [], "Material": [], "Method": ["combinatorial auctions", "computational Bayes-Nash equilibrium analysis"], "Metric": ["rate of ex-post IR violations"], "OtherScientificTerm": ["individual rationality (IR)", "violations of individual rationality (IR)", "payment rule", "core-selecting rules"], "Task": ["auction domains", "design of core-selecting payment rules"]}