No abstract

A large number of single-crystalline TaS2 nanobelts have been prepared via a two-step method. In this approach, TaS3 nanobelts as precursors were synthesized via chemical vapour transition (CVT), and pyrolysed into TaS2 nanobelts in vacuum. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) showed that the TaS2 nanobelts have a rectangular section of ∼15 × 40 to 80 × 900 nm2, and length up to several centimetres. Magnetic measurement indicated that the TaS2 nanobelts have superconductivity at 2.7 K, and the superconducting transition temperature (Tc) is higher than that (Tc = 0.8 K) reported for the bulk 2H-TaS2. The current density in electron field emission reached about 100 µA cm−2 at an applied field of about 27 V µm−1, so the nanobelts could be used as electronic field emitters.

In CTD-ILD patients, serum CEA and CA 19-9 are elevated and can be indicators of disease severity. Moreover, serum CEA is a significant and independent predictor of survival.

Traditional semi‐supervised clustering uses only limited user supervision in the form of instance seeds for clusters and pairwise instance constraints to aid unsupervised clustering. However, user supervision can also be provided in alternative forms for document clustering, such as labeling a feature by indicating whether it discriminates among clusters. This article thus fills this void by enhancing traditional semi‐supervised clustering with feature supervision, which asks the user to label discriminating features during defining (labeling) the instance seeds or pairwise instance constraints. Various types of semi‐supervised clustering algorithms were explored with feature supervision. Our experimental results on several real‐world data sets demonstrate that augmenting the instance‐level supervision with feature‐level supervision can significantly improve document clustering performance.

Traditional semi-supervised clustering uses only limited user supervision in the form of labeled instances and pairwise instance constraints to aid unsupervised clustering. However, user supervision can also be provided in alternative forms for document clustering, such as labeling a feature by indicating whether it discriminates among clusters. This paper thus fills this void by enhancing traditional semi-supervised clustering with feature supervision which asks the user to label discriminating features during labeling the instance or pairwise instance constraints. Various types of semi-supervised clustering algorithms were explored with feature supervision. Our experimental results on several real-world datasets demonstrate that augmenting the instance-level supervision with feature-level supervision can significantly improve document clustering performance.