HBPC sites reflected the rationale and mission of HBPC by focusing on complex chronic illness of home-based veterans and providing comprehensive primary care using interdisciplinary teams. Our next series of studies will examine how HBPC site structural characteristics and care models are related to the processes and outcomes of care to determine whether there are best practice standards that define an optimal HBPC structure and care model or whether multiple approaches to HBPC better serve the needs of veterans.
We study beef packing margins before and after mandatory price reporting (MPR) was implemented in 2001 using a model that identifies and tests for switching between cooperative and non‐cooperative regime pricing. Our results show that after MPR took effect, the duration of non‐cooperative regimes was considerably shorter, while cooperative regimes were longer. Oligopsonistic rent, as measured by average economic profit, rose from $0.88/head in the 1990s to $2.59/head after 2001. While MPR is not likely the sole cause for such an increase, there was clearly more market power being exercised in fed cattle markets in the years after the program was implemented than before.
Pseudo bounding boxes from the self-training paradigm are inevitably noisy for semi-supervised object detection. To cope with that, a dual decoupling training framework is proposed in the present study, i.e. clean and noisy data decoupling, and classification and localization task decoupling. In the first decoupling, two-level thresholds are used to categorize pseudo boxes into three groups, i.e. clean backgrounds, noisy foregrounds and clean foregrounds. With a specially designed noise-bypass head focusing on noisy data, backbone networks can extract coarse but diverse information; and meanwhile, an original head learns from clean samples for more precise predictions. In the second decoupling, we take advantage of the two-head structure for better evaluation of localization quality, thus the category label and location of a pseudo box can remain independent of each other during training. The approach of two-level thresholds is also applied to group pseudo boxes into three sections of different location accuracy. We outperform existing works by a large margin on VOC datasets, reaching 54.8 mAP(+1.8), and even up to 55.9 mAP(+1.5) by leveraging MS-COCO train2017 as extra unlabeled data. On MS-COCO benchmark, our method also achieves about 1.0 mAP improvements averaging across protocols compared with the prior state-of-the-art.
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