BANDWIDTH EFFICIENT FORMATION OF BROADCAST NETWORK WITH MULTIPLE DESCRIPTION CODING

In this paper, we consider the delay and fault-tolerance of data  broadcasting in  Internet of Things (IoT) networks,  in  which  nodes  form  a network  topology to deliver  live data  from  a source  to the  end receivers. We first consider to build a Small Height Tree which gives an overlay  with small expected  end-to-end  delay. The end-to-end delay and the fault-tolerance can be improved by  adopting  appropriate topology  for  the  overlay  according  to the  characteristics of providers.   Efficient and fault-tolerant in service level agreement (SLA) guaranteed services can hardly be achieved solely by tree or mesh. By multiple-path data  delivery with  multiple  description coding,  service  operators can  use the scheme  to predict  the  amount  of resources to be acquired,  and hence the cost, from  the network

China

Economic development processes in post-1949 China can be divided into two periods. In the first, 1950-70, the economy was extensively and intensively controlled by the state with a priority for developing heavy industries. In the second, since the 80s and known as the \u27reform period,\u27 the Chinese economy has increasingly been integrated with the world economy and relying on light (rural) industries as the prime motor of economic growth. Yet, in both these periods, Chinese policymakers shared the same \u27developmental\u27 philosophy in which social costs, that is the reproduction costs of human labour and nature, are largely ignored. The following is a critical sketch of government policies and their impact on the domestic population in these two periods

A Statistical Decomposition Based Neural Network For Multivariate Time Series Forecasting

Machine learning based time series forecasting methods are popular and can match the performance of statistical models, in terms of accuracy, scalability, speed, etc. This disclosure presents techniques that incorporate statistical modeling into a neural network framework. The hybrid time series forecasting model described herein is named Seasonality Trend AutoRegressive Residual Yeo-Johnson power transformation Neural Network (STARRY-N). STARRY-N combines the advantages of residual neural network structure (such as N-BEATS) and explainable statistical forecasting models (such as TBATS). The model utilizes a neural network structure with separate stacks for trend, power transformed trend, seasonality, residual correction, and covariate adoption such as holiday effects. STARRY-N has good accuracy and is an explainable forecasting model

Optimizing machine learning methods to discover strong gravitational lenses in the Deep Lens Survey

Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and unsupervised learning algorithms trained with the ResNetV2 neural network architecture on their ability to efficiently find strong gravitational lenses in the Deep Lens Survey (DLS). We use galaxy images from the survey, combined with simulated lensed sources, as labeled data in our training datasets. We find that models using semi-supervised learning along with data augmentations (transformations applied to an image during training, e.g., rotation) and Generative Adversarial Network (GAN) generated images yield the best performance. They offer 5--10 times better precision across all recall values compared to supervised algorithms. Applying the best performing models to the full 20 deg$^2$ DLS survey, we find 3 Grade-A lens candidates within the top 17 image predictions from the model. This increases to 9 Grade-A and 13 Grade-B candidates when $1$% ($\sim2500$ images) of the model predictions are visually inspected. This is $\gtrsim10\times$ the sky density of lens candidates compared to current shallower wide-area surveys (such as the Dark Energy Survey), indicating a trove of lenses awaiting discovery in upcoming deeper all-sky surveys. These results suggest that pipelines tasked with finding strong lens systems can be highly efficient, minimizing human effort. We additionally report spectroscopic confirmation of the lensing nature of two Grade-A candidates identified by our model, further validating our methods.Comment: 23 pages, 15 figures (including appendix), published in MNRA

Coronavirus-positive Nasopharyngeal Aspirate as Predictor for Severe Acute Respiratory Syndrome Mortality

Severe acute respiratory syndrome (SARS) has caused a major epidemic worldwide. A novel coronavirus is deemed to be the causative agent. Early diagnosis can be made with reverse transcriptase-polymerase chain reaction (RT-PCR) of nasopharyngeal aspirate samples. We compared symptoms of 156 SARS-positive and 62 SARS-negative patients in Hong Kong; SARS was confirmed by RT-PCR. The RT-PCR–positive patients had significantly more shortness of breath, a lower lymphocyte count, and a lower lactate dehydrogenase level; they were also more likely to have bilateral and multifocal chest radiograph involvement, to be admitted to intensive care, to need mechanical ventilation, and to have higher mortality rates. By multivariate analysis, positive RT-PCR on nasopharyngeal aspirate samples was an independent predictor of death within 30 days

Case report: Infective endocarditis caused by Brevundimonas vesicularis

BACKGROUND: There are few reports in the literature of invasive infection caused by Brevundimonas vesicularis in patients without immunosuppression or other predisposing factors. The choice of antimicrobial therapy for bacteremia caused by the pathogen requires more case experience to be determined. CASE PRESENTATION: The case of a 40-year-old previously healthy man with subacute endocarditis proposed to be contributed from an occult dental abscess is described. The infection was found to be caused by B. vesicularis on blood culture results. The patient recovered without sequelae after treatment with ceftriaxone followed by subsequent ciprofloxacin therapy owing to an allergic reaction to ceftriaxone and treatment failure with ampicillin/sulbactam. CONCLUSION: To our knowledge, this is the first report of B. vesicularis as a cause of infective endocarditis. According to an overview of the literature and our experience, we suggest that third-generation cephalosporins, piperacillin/tazobactam, and ciprofloxacin are effective in treating invasive B. vesicularis infections, while the efficacy of ampicillin-sulbactam needs further evaluation

Numerical comparison of the closing dynamics of a new trileaflet and a bileaflet mechanical aortic heart valve

[[abstract]]The closing velocity of the leaflets of mechanical heart valves is excessively rapid and can cause the cavitation phenomenon. Cavitation bubbles collapse and produce high pressure which then damages red blood cells and platelets. The closure mechanism of the trileaflet valve uses the vortices in the aortic sinus to help close the leaflets, which differs from that of the monoleaflet or bileaflet mechanical heart valves which mainly depends on the reverse flow. We used the commercial software program Fluent to run numerical simulations of the St. Jude Medical bileaflet valve and a new trileaflet mechanical heart valve. The results of these numerical simulations were validated with flow field experiments. The closing velocity of the trileaflet valve was clearly slower than that of the St. Jude Medical bileaflet valve, which would effectively reduce the occurrence of cavitation. The findings of this study are expected to advance the development of the trileaflet valve.[[incitationindex]]SCI[[booktype]]電子版[[booktype]]紙

Newborn Genetic Screening for Hearing Impairment: A Preliminary Study at a Tertiary Center

Universal newborn hearing screening (UNHS) is of paramount importance for early identification and management of hearing impairment in children. However, infants with slight/mild, progressive, or late-onset hearing impairment might be missed in conventional UNHS. To investigate whether genetic screening for common deafness-associated mutations could assist in identifying these infants, 1017 consecutive newborns in a tertiary hospital were subjected to both newborn hearing screening using a two-step distortion-product otoacoustic emissions (DPOAE) screening and newborn genetic screening (NGS) for deafness. The NGS targeted 4 deafness-associated mutations commonly found in the Taiwanese population, including p.V37I (c.109G>A) and c.235delC of the GJB2 gene, c.919-2A>G of the SLC26A4 gene, and mitochondrial m.1555A>G of the 12S rRNA gene. The results of the NGS were then correlated to the results of the NHS. Of the 1017 newborns, 16 (1.6%) had unilateral DPOAE screening failure, and 22 (2.2%) had bilateral DPOAE screening failure. A total of 199 (19.6%) babies were found to have at least 1 mutated allele on the NGS for deafness, 11 (1.1%) of whom were homozygous for GJB2 p.V37I, 6 (0.6%) compound heterozygous for GJB2 p.V37I and c.235delC, and 1 (0.1%) homoplasmic for m.1555A>G, who may potentially have hearing loss. Among them, 3 babies, 5 babies, and 1 baby, respectively, passed the NHS at birth. Comprehensive audiological assessments in the 9 babies at 3 months identified 1 with slight hearing loss and 2 with mild hearing loss. NGS for common deafness-associated mutations may identify infants with slight/mild or potentially progressive hearing impairment, thus compensating for the inherent limitations of the conventional UNHS