An Unsupervised Autoregressive Model for Speech Representation Learning

This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that aim to remove noise or speaker variabilities, ours is designed to preserve information for a wide range of downstream tasks. In addition, the proposed model does not require any phonetic or word boundary labels, allowing the model to benefit from large quantities of unlabeled data. Speech representations learned by our model significantly improve performance on both phone classification and speaker verification over the surface features and other supervised and unsupervised approaches. Further analysis shows that different levels of speech information are captured by our model at different layers. In particular, the lower layers tend to be more discriminative for speakers, while the upper layers provide more phonetic content.Comment: Accepted to Interspeech 2019. Code available at: https://github.com/iamyuanchung/Autoregressive-Predictive-Codin

Efficient Downlink Channel Reconstruction for FDD Multi-Antenna Systems

In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. We first estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high estimation accuracy. Simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting that the promising application of the proposed scheme in large-scale antenna array systems

Epidemiology of digital amputation and replantation in Taiwan: A population-based study

AbstractBackgroundPublications on digital amputation and replantation have been mostly derived from case series in high-volume hand surgery practices, and epidemiological studies are few. This study used a population-based dataset to illustrate the incidence of digital amputation, patient and hospital characteristics, and their relationships with replantation.MethodsA claim for reimbursement dataset (2008) was provided as a research database by the Bureau of National Health Insurance, Taiwan. Patients with ICD-9-CM coded as digital amputation (885 and 886) were included. These were cross-referenced with procedure codes for replantation procedures (84.21 and 84.22). We defined the patients who underwent thumb replantation (84.21) and thumb amputation (84.01) during a single hospitalization as replantation failure. Patient and hospital characteristics were studied with statistical analysis.ResultsIn total, 2358 patients with digital amputation were admitted (1859 male, 499 female), mean age 39.2 ± 15.5 years. The incidence was 10.2/100,000 person–years. The highest incidence was 14.7/100,000 person–years in the age group 45–54 years. Machinery and powered hand tools caused 68.8% of digital amputations. Thumb amputation [odds ratio (OR): 1.35, p = 0.01], private hospital (OR: 1.40, p = 0.01), medical center (OR: 2.38, p < 0.001), regional hospital (OR: 2.41, p < 0.001) and hospitals with an annual volume >20 digital amputations (OR: 4.23, p < 0.001) were associated with higher attempt rates for replantation. Elderly patients (age >65 years) had higher risk of thumb replantation failure (OR: 32.30, p = 0.045), while hospitals with >20 annual replantations had lower risk (OR: 0.11, p = 0.02).ConclusionOur study of the National Health Insurance database characterized the epidemiology of digital amputation patients undergoing replantation and the facilities in Taiwan where these procedures are performed. The hospitals treating more digital amputation patients had higher attempt rates and lower thumb failure rates

Debris Flow Risk Assessment and Land-Use Planning – A Case Study of Jhonglun Hot Spring Area

The Jhonglun Scenic Area in Chiayi County, is famous for its hot spring, the region was hit by debris flow with tremendous losses and resulted with dramatic change of the landscape during Typhoon Morakot in 2009. The most effective strategy for reducing natural hazard risks is through land-use planning. Following the concept of Risk=Hazard*Exposure*Vulnerability, this study conducted risk identification through the collection of landslide inventory and history debris flow hazard mapping of Chiayi DF051 potential debris flow torrent. Together with elements at risk information from field investigations, the risk analysis was conducted with several return periods debris flow simulation to recognize the possible economic losses and fatalities by debris flow. The identified high risk areas in Jhonglun Scenic Area were compared to the current special district planning to understand the spatial distribution of high risk areas. The result shows that some of the designated zones were among the areas with high debris flow risks, which further indicates that land-use planning should consider the consequences of natural hazards. The result of this study provides one of the first steps for land use planning restrictions within the potential debris flow region