Spectral Analysis for Semantic Segmentation with Applications on Feature Truncation and Weak Annotation

We propose spectral analysis to investigate the correlation between the accuracy and the resolution of segmentation maps for semantic segmentation. The current networks predict segmentation maps on the down-sampled grid of images to alleviate the computational cost. Moreover, these networks can be trained by weak annotations that utilize only the coarse contour of segmentation maps. Despite the successful achievement of these works utilizing the low-frequency information of segmentation maps, however, the accuracy of resultant segmentation maps may also be degraded in the regions near object boundaries. It is yet unclear for a theoretical guideline to determine an optimal down-sampled grid to strike the balance between the cost and the accuracy of segmentation. We analyze the objective function (cross-entropy) and network back-propagation process in frequency domain. We discover that cross-entropy and key features of CNN are mainly contributed by the low-frequency components of segmentation maps. This further provides us quantitative results to determine the efficacy of down-sampled grid of segmentation maps. The analysis is then validated on the two applications: the feature truncation method and the block-wise annotation that limit the high-frequency components of the CNN features and annotation, respectively. The results agree with our analysis. Thus the success of the existing work utilizing low-frequency information of segmentation maps now has theoretical foundation.Comment: 21 page

A multi-product FPR model with rework and an improved delivery policy

A multi-item finite production rate (FPR) model with rework and an improved delivery policy is examined in this paper. Unlike the classic FPR model whose purpose is to derive the most economic lot size for a single-product production system with perfect quality and a continuous issuing policy, this paper considers a production of multiple products on a single machine, rework of all nonconforming items produced, and a cost-reduction, multi-delivery policy. We extend the work of Chiu et al. [1] by incorporating an improved n+1 shipment policy into their model. According to such a policy, one extra delivery of finished items is made during vendor’s production uptime to satisfy product demands during the period of vendor’s uptime and rework time. When the rest of the production lot is quality assured and the rework has been finished as well, n fixed-quantity installments of finished items are delivered to customers. The objectives are to determine an optimal, common-production cycle time that minimizes the long-run average system cost per time unit, study the effects of rework and the improved delivery policy on the optimal production. Mathematical modelling and analysis is used to derive a closed-form, optimal, common-cycle time. Finally, practical usages of the obtained results are demonstrated by a numerical example

Improved Noisy Student Training for Automatic Speech Recognition

Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy student training is an iterative self-training method that leverages augmentation to improve network performance. In this work, we adapt and improve noisy student training for automatic speech recognition, employing (adaptive) SpecAugment as the augmentation method. We find effective methods to filter, balance and augment the data generated in between self-training iterations. By doing so, we are able to obtain word error rates (WERs) 4.2%/8.6% on the clean/noisy LibriSpeech test sets by only using the clean 100h subset of LibriSpeech as the supervised set and the rest (860h) as the unlabeled set. Furthermore, we are able to achieve WERs 1.7%/3.4% on the clean/noisy LibriSpeech test sets by using the unlab-60k subset of LibriLight as the unlabeled set for LibriSpeech 960h. We are thus able to improve upon the previous state-of-the-art clean/noisy test WERs achieved on LibriSpeech 100h (4.74%/12.20%) and LibriSpeech (1.9%/4.1%).Comment: 5 pages, 5 figures, 4 tables; v2: minor revisions, reference adde

Reduced Stroke Risk among Patients with Atrial Fibrillation Receiving Chinese Herbal Medicines Treatment: Analysis of Domestic Data in Taiwan

Background and objectives: Patients with atrial fibrillation (AF) reportedly have a much higher risk of death due to stroke. Faced with this heavy burden, it remains unclear if the Chinese herbal medicines (CHMs), the most common form complementary and alternative medicine, can lower the risk of stroke for them. This study aimed to evaluate the association of CHMs use with stroke risk among them. Materials and Methods: From a nationwide database, 11,456 AF patients aged ≧ 20 years between 1998 and 2007 were identified. Afterwards, we enrolled 2670 CHMs users and randomly selected 2670 non-CHMs users using the propensity score method. The occurrence of stroke was recorded until the end of 2012. Results: Within the follow-up period, 671 CHMs users and 900 non-CHMs users developed stroke, with incidence rates of 33.02 and 45.46 per 1000 person-years, respectively. CHMs use was associated with a 30% lower stroke risk, especially for those receiving CHMs for over two years. Conclusions: The findings of the present study suggest that adding CHMs to conventional therapy could decrease subsequent stroke risk for AF patients. It is also suggested that prospective randomized trials are needed to further clarify if the detected association revealed in this study supports a causal link, and to identify the specific CHMs that may be beneficial to AF patients