DPATD: Dual-Phase Audio Transformer for Denoising

Recent high-performance transformer-based speech enhancement models demonstrate that time domain methods could achieve similar performance as time-frequency domain methods. However, time-domain speech enhancement systems typically receive input audio sequences consisting of a large number of time steps, making it challenging to model extremely long sequences and train models to perform adequately. In this paper, we utilize smaller audio chunks as input to achieve efficient utilization of audio information to address the above challenges. We propose a dual-phase audio transformer for denoising (DPATD), a novel model to organize transformer layers in a deep structure to learn clean audio sequences for denoising. DPATD splits the audio input into smaller chunks, where the input length can be proportional to the square root of the original sequence length. Our memory-compressed explainable attention is efficient and converges faster compared to the frequently used self-attention module. Extensive experiments demonstrate that our model outperforms state-of-the-art methods.Comment: IEEE DD

Machine Learning-Based Seismic Damage Assessment Of Residential Buildings Considering Multiple Earthquake And Structure Uncertainties

Wood-frame structures are used in almost 90% of residential buildings in the United States. It is thus imperative to rapidly and accurately assess the damage of wood-frame structures in the wake of an earthquake event. This study aims to develop a machine-learning-based seismic classifier for a portfolio of 6,113 wood-frame structures near the New Madrid Seismic Zone (NMSZ) in which synthesized ground motions are adopted to characterize potential earthquakes. This seismic classifier, based on a multilayer perceptron (MLP), is compared with existing fragility curves developed for the same wood-frame buildings near the NMSZ. This comparative study indicates that the MLP seismic classifier and fragility curves perform equally well when predicting minor damage. However, the MLP classifier is more accurate than the fragility curves in prediction of moderate and severe damage. Compared with the existing fragility curves with earthquake intensity measures as inputs, machine-learning-based seismic classifiers can incorporate multiple parameters of earthquakes and structures as input features, thus providing a promising tool for accurate seismic damage assessment in a portfolio scale. Once trained, the MLP classifier can predict damage classes of the 6,113 structures within 0.07 s on a general-purpose computer

Ochronotic arthropathy effectively treated with total hip and total knee arthroplasty: a case report

Ochronosis is a rare autosomal recessive disorder of tyrosine metabolism characterized by multilevel spinal degeneration and arthritis of large weight-bearing joints, which is referred to as ochronotic arthropathy. In this case report, we describe diagnosis and treatment of ochronotic arthropathy in a patient who underwent total hip arthroplasty (THA) and total knee arthroplasty (TKA). The Harris hip score was 26 preoperatively and 45, 68, 76, 90, 92, and 94 at 1, 3, 6, 9, 11, and 14 months, respectively, postoperatively. The forgotten joint score (FJS) of the hip was 27.8, 52.8, 81.1, 89.0, 90.6, and 92.4 at 1, 3, 6, 9, 11, and 14 months, respectively, postoperatively. TKA was performed 8 months after THA. The Knee Society Score was 36 before TKA and 74, 82, and 90 at 1, 3, and 6 months, respectively, after TKA. The FJS of the knee was 36.6, 63.9, and 84.5 at 1, 3, and 6 months, respectively, after TKA. The patient’s knee range of motion returned to normal, with significant reduction in pain and improved satisfaction levels after TKA. THA and TKA can achieve good clinical outcomes in patients with ochronosis accompanied by severe joint pain

Bibliometric analysis of scientific publications in rheumatology journals from China and other top-ranking countries between 2007 and 2017

Objectives Rheumatology-related diseases remain a significant burden worldwide. However, little is known about the comparative status of rheumatology research between Mainland China (MC) and the world’s leading countries. The aim of this study is to compare the quantity and quality of research output in the field of rheumatology that were written by researchers from MC, the USA, the UK, the Netherlands and France. Methods Between 2007 and 2017, all articles published in 30 rheumatology journals were identified via Science Citation Index Expanded database. The number of total and annual articles, article types (randomized controlled trials (RCTs), reviews, case reports, clinical trials and meta-analysis), impact factor (IF), citations, h-index and articles in the high-impact journals were collected for quantity and quality comparisons. The correlation of socioeconomic factors and annual publications was also analyzed. Results From 2007 to 2017, there were 53,439 articles published in rheumatology journals, of which researchers from the USA published 13,391 articles, followed by the UK, the Netherlands, France and MC with 6,179, 4,310, 4,066 and 2,898 articles, respectively. Publications from MC represented the ninth, but the number is growing rapidly. For total and average citations, MC still lags behind the other four countries in the study. Similar trends were observed in average IF, h-index and articles in the high-impact journals. In terms of article types, the USA occupies the dominant place, except for meta-analysis. The annual numbers of articles from MC and the USA were positively correlated with gross domestic product (p < 0.05). Conclusions The USA has played predominant role in rheumatology research for the last 11 years. The annual number of published articles from MC has increased notably from 2007 to 2017. Although MC has made progress in the number of published articles over the past decade, it still lags far behind the highly developed countries in most bibliometric indicators. Thus, the general quality of publications from MC needs further improvement

MyoPS A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/)

Towards a high-fidelity risk-free interest rate

The risk-free interest rate is not only an essential parameter in financial market but also a key indicator in economy. To estimate the risk-free interest rate, we use the return rates of treasury bonds, which is an important derivative of risk-free interest rate. In this project, we will use several short rate models and affine term structure to calibrate the parameters in these models as well as in in bond pricing model

Image Processing

Image processing is the set of processes which involve manipulating and extracting information from images with systematic methods. In this paper, we overview the mathematics behind a number of image processing techniques and their uses. We go into depth on the subjects of the identification of face like features of images and expression of gradients in images through vector graphics. We conclude with unique implementations of those subjects in MATLAB programs

One-Pot Synthesis of Pyrrolo[1,2-f]phenanthridines From 1-Arylpyrroles via Successive Palladium-Catalyzed Direct Arylations

International audienceA Pd-catalyzed annulative pi-extension reaction of 1-arylpyrroles using 1,2-dihalobenzenes as the coupling partners was investigated. The higher reactivity of pyrrole C2-H bond compared to C-H bonds of the aryl unit of 1-arylpyrroles allows selective synthesis of pyrrolo[1,2-f]phenanthridines via successive palladium-catalyzed direct intermolecular followed by intramolecular direct arylation steps. From 1-bromo-2-iodobenzenes bearing substituents at C4- or C5-positions and ortho-, meta- or para-substituted 1-arylpyrroles, the access to pyrrolo[1,2-f]phenanthridines containing substituents at C5, C6, C7, C8, C10 and/or C11 positions is possible