Scalable and Effective Conductance-based Graph Clustering

Conductance-based graph clustering has been recognized as a fundamental operator in numerous graph analysis applications. Despite the significant success of conductance-based graph clustering, existing algorithms are either hard to obtain satisfactory clustering qualities, or have high time and space complexity to achieve provable clustering qualities. To overcome these limitations, we devise a powerful \textit{peeling}-based graph clustering framework \textit{PCon}. We show that many existing solutions can be reduced to our framework. Namely, they first define a score function for each vertex, then iteratively remove the vertex with the smallest score. Finally, they output the result with the smallest conductance during the peeling process. Based on our framework, we propose two novel algorithms \textit{PCon\_core} and \emph{PCon\_de} with linear time and space complexity, which can efficiently and effectively identify clusters from massive graphs with more than a few billion edges. Surprisingly, we prove that \emph{PCon\_de} can identify clusters with near-constant approximation ratio, resulting in an important theoretical improvement over the well-known quadratic Cheeger bound. Empirical results on real-life and synthetic datasets show that our algorithms can achieve 5$\sim$42 times speedup with a high clustering accuracy, while using 1.4$\sim$7.8 times less memory than the baseline algorithms

Diagnostic Accuracy of CEUS LI-RADS for the Characterization of Liver Nodules 20 mm or Smaller in Patients at Risk for Hepatocellular Carcinoma.

Background: American College of Radiology contrast agent–enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) was developed to improve the accuracy of hepatocellular carcinoma (HCC) diagnosis at contrast agent2enhanced US. However, to the knowledge of the authors, the diagnostic accuracy of the system in characterization of liver nodules 20 mm or smaller has not been fully evaluated. Purpose: To evaluate the diagnostic accuracy of CEUS LI-RADS in diagnosing HCC in liver nodules 20 mm or smaller in patients at risk for HCC. Materials and Methods: Between January 2015 and February 2018, consecutive patients at risk for HCC presenting with untreated liver nodules 20 mm or less were enrolled in this retrospective double-reader study. Each nodule was categorized according to the CEUS LI-RADS and World Federation for Ultrasound in Medicine and Biology (WFUMB)–European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) criteria. Diagnostic performance of CEUS LI-RADS and WFUMB-EFSUMB characterization was evaluated by using tissue histologic analysis, multiphase contrast-enhanced CT and MRI, and imaging follow-up as reference standard and compared by using McNemar test. Results: The study included 175 nodules (mean diameter, 16.1 mm 6 3.4) in 172 patients (mean age, 51.8 years 6 10.6; 136 men). The sensitivity of CEUS LR-5 versus WFUMB-EFSUMB criteria in diagnosing HCC was 73.3% (95% confidence inter-val [CI]: 63.8%, 81.5%) versus 88.6% (95% CI: 80.9%, 94%), respectively (P, .001). The specificity of CEUS LR-5 versus WFUMB-EFSUMB criteria was 97.1% (95% CI: 90.1%, 99.7%) versus 87.1% (95% CI: 77%, 94%), respectively (P = .02). No malignant lesions were found in CEUS LR-1 and LR-2 categories. Only two nodules (of 41; 5%, both HCC) were malignant in CEUS LR-3 category. The incidences of HCC in CEUS LR-4, LR-5, and LR-M were 48% (11 of 23), 98% (77 of 79), and 75% (15 of 20), respectively. Two of 175 (1.1%) histologic analysis2confirmed intrahepatic cholangiocarcinomas were categorized as CEUS LR-M by CEUS LI-RADS and misdiagnosed as HCC by WFUMB-EFSUMB criteria. Conclusion: The contrast-enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) algorithm was an effective tool for characterization of small (≤20 mm) liver nodules in patients at risk for hepatocellular carcinoma (HCC). Compared with World Federation for Ultrasound in Medicine and Biology2European Federation of Societies for Ultrasound in Medicine and Biology criteria, CEUS LR-5 demonstrated higher specificity for diagnosing small HCCs with lower sensitivity

Selection and Mid-infrared Spectroscopy of Ultraluminous Star-Forming Galaxies at z~2

Starting from a sample of 24 \micron\ sources in the Extended Groth Strip, we use 3.6 to 8 \micron\ color criteria to select ultraluminous infrared galaxies (ULIRGs) at $z\sim2$. Spectroscopy from 20-38 \micron\ of 14 objects verifies their nature and gives their redshifts. Multi-wavelength data for these objects imply stellar masses ${>}10^{11}$ \Msun\ and star formation rates $\ge$410 \Msun yr$^{-1}$. Four objects of this sample observed at 1.6 \micron\ (rest-frame visible) with {\it HST}/WFC3 show diverse morphologies, suggesting that multiple formation processes create ULIRGs. Four of the 14 objects show signs of active galactic nuclei, but the luminosity appears to be dominated by star formation in all cases.Comment: 33 pages, 13 figures, accepted by Ap

A Three-Dimensional Porous Conducting Polymer Composite with Ultralow Density and Highly Sensitive Pressure Sensing Properties

An ultralight conducting polyaniline/SiC/polyacrylonitrile (PANI/SiC/PAN) composite was fabricated by in situ polymerization of aniline monomer on the surface of fibers in SiC/PAN aerogel. The SiC/PAN aerogel was obtained by electrospinning, freeze-drying, and heat treatment. The ingredient, morphology, structure, and electrical properties of the aerogel before and after in situ polymerization were investigated by X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), and voltage-current characteristic measurement. The thermostability of PANI/SiC/PAN composite was investigated by thermogravimetric analysis (TGA) and electrical resistance measured at different temperatures. The density of the PANI/SiC/PAN composite was approximately 0.211 g cm−3, the porosity was 76.44%, and the conductivity was 0.013 S m−1. The pressure sensing properties were evaluated at room temperature. The electrical resistance of as-prepared sample decreased gradually with the increase of pressure. Furthermore, the pressure sensing process was reversible and the response time was short (about 1 s). This composite may have application in pressure sensor field