Computationally efficient direction finding using polynomial rooting with reduced-order and real-valued computations

The root multiple signal classification（root-MUSIC） algorithm is one of the most important techniques for direction of arrival（DOA） estimation. Using a uniform linear array（ULA） composed of M sensors, this method usually estimates L signal DOAs by finding roots that lie closest to the unit circle of a（2M-1）-order polynomial, where L 〈 M. A novel efficient root-MUSIC-based method for direction estimation is presented, in which the order of polynomial is efficiently reduced to 2L. Compared with the unitary root-MUSIC（U-root-MUSIC） approach which involves real-valued computations only in the subspace decomposition stage, both tasks of subspace decomposition and polynomial rooting are implemented with real-valued computations in the new technique,which hence shows a significant efficiency advantage over most state-of-the-art techniques. Numerical simulations are conducted to verify the correctness and efficiency of the new estimator.

Evaluation of intrahepatic manifestation and distant extrahepatic disease in alveolar echinococcosis

BACKGROUND The main endemic areas of alveolar echinococcosis(AE)are in Central Europe and Western China.Both the infiltration of intrahepatic vascular and bile duct structures as well as extrahepatic disease can lead to further complications and may increase morbidity in patients with AE.AIM To evaluate vascular/biliary involvement in hepatic AE and its distant extrahepatic disease manifestations in an international collective was the aim.METHODS Consecutively,five experienced examiners evaluated contrast-enhanced abdominal computed tomography(CT)scans for 200 patients with hepatic AE of each of four locations(n=50)in Germany,France and China.Therefore,we retrospectively included the 50 most recent abdominal contrast-enhanced CT examinations at each center,performed because of hepatic AE from September 21,2007 to March 21,2018.AE liver lesions were classified according to the echinococcosis multilocularis Ulm classification for CT(EMUC-CT).Distant extrahepatic manifestations were documented either by whole body positron emission tomography–CT or with the addition of thoracic CT and cranial magnetic resonance imaging.Vascular/biliary involvement of the hepatic disease as well as the presence of distant extrahepatic manifestations were correlated with the EMUC-CT types of liver lesion.Statistical analysis was performed using SAS Version 9.4(SAS Institute Inc.,Cary,NC,United States).RESULTS Distant extrahepatic AE manifestations were significantly more frequent in China than in Europe(P=0.0091).A significant relationship was found between the presence of distant extrahepatic disease and AE liver lesion size(P=0.0075).Vascular/biliary structures were involved by the liver lesions significantly more frequently in China than in Europe(P<0.0001),and vascular/biliary involvement depended on lesion size.Different morphological types of AE liver lesions led to varying frequencies of vascular/biliary involvement and were associated with different frequencies of distant extrahepatic manifestations:Vascular/biliary involvement as a function of lesions primary morphology ranged from 5.88%of type IV liver lesions to 100%among type III lesions.Type IV differed significantly in these associations from types I,II,and III(P<0.0001).With respect to extrahepatic disease,the primary morphology types IV and V of liver lesions were not associated with any case of distant extrahepatic disease.In contrast,distant extrahepatic manifestations in types I–III were found to varying degrees,with a maximum of 22%for type III.CONCLUSION Different CT morphological patterns of hepatic AE lesions influence vascular/biliary involvement and the occurrence of distant extrahepatic manifestations.There are intercontinental differences regarding the characteristics of AE manifestation.

Stochastic Variational Inference-Based Parallel and Online Supervised Topic Model for Large-Scale Text Processing

Topic modeling is a mainstream and effective technology to deal with text data, with wide applications in text analysis, natural language, personalized recommendation, computer vision, etc. Among all the known topic models, supervised Latent Dirichlet Allocation （sLDA） is acknowledged as a popular and competitive supervised topic model. How- ever, the gradual increase of the scale of datasets makes sLDA more and more inefficient and time-consuming, and limits its applications in a very narrow range. To solve it, a parallel online sLDA, named PO-sLDA （Parallel and Online sLDA）, is proposed in this study. It uses the stochastic variational inference as the learning method to make the training procedure more rapid and efficient, and a parallel computing mechanism implemented via the MapReduce framework is proposed to promote the capacity of cloud computing and big data processing. The online training capacity supported by PO-sLDA expands the application scope of this approach, making it instrumental for real-life applications with high real-time demand. The validation using two datasets with different sizes shows that the proposed approach has the comparative accuracy as the sLDA and can efficiently accelerate the training procedure. Moreover, its good convergence and online training capacity make it lucrative for the large-scale text data analyzing and processing.