Generation of Pauses Within the Z-Score Model

We have previously proposed [BB94] a model for the generation of segmental durations which proceeds in two steps : (a) prediction of the timing of a salient acoustic event per syllable according to phonotactic and syntactic information, and (b) application of a repartition model which determines the duration of each individual segment between these events. This paper focusses on the repartition model and describes how the initial model has been enriched to account for the emergence of pauses as speech rate is decreased. The last section of this paper describes a perceptual evaluation of the whole model. This evaluation shows that, for the same distribution of prediction errors, a precise timing of these events is perceptually more relevant than a segment-based method aiming at predicting precisely each individual segmental duration

The Strategy and History of Electoral System Choice

Peer reviewe

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094