Acquisition of Entailment Relations in Korean Causatives

This study investigates whether two Korean causative types (morphological and syntactic) have the same entailment properties in adult and child Korean. Patterson (1974) claims that only the morphological causative entails the occurrence of the caused event, while Kim’s (2005) experimental study found no such entailment for either type of causatives. In this study, a Truth Value Judgment Task (Crain and McKee 1985; Crain and Thornton 1998) was conducted with sixteen Korean-speaking adults and showed that the entailment relation is required for the morphological causative. Twenty-five Korean-speaking children participated in the same task and behaved similarly to adults in that they rejected the morphological causative when the caused event did not take place. On the other hand, it was revealed that some children were sensitive to the type of causation depicted in the task, independent of the entailment properties. They showed a tendency to link the morphological causative only when it was associated with direct causation, but not with indirect causation. This observed difference between adults and children may be explained by the Iconicity Principle (Haiman 1983), which predicts the morphological causative to be associated with direct causation, and the syntactic causative with indirect causation

Depth from a Light Field Image with Learning-based Matching Costs

One of the core applications of light field imaging is depth estimation. To acquire a depth map, existing approaches apply a single photo-consistency measure to an entire light field. However, this is not an optimal choice because of the non-uniform light field degradations produced by limitations in the hardware design. In this paper, we introduce a pipeline that automatically determines the best configuration for photo-consistency measure, which leads to the most reliable depth label from the light field. We analyzed the practical factors affecting degradation in lenslet light field cameras, and designed a learning based framework that can retrieve the best cost measure and optimal depth label. To enhance the reliability of our method, we augmented an existing light field benchmark to simulate realistic source dependent noise, aberrations, and vignetting artifacts. The augmented dataset was used for the training and validation of the proposed approach. Our method was competitive with several state-of-the-art methods for the benchmark and real-world light field datasets.11Nsciescopu