A method involving dry deposition plus wet chemical etching was devised to fabricate silicon nanowire (SiNW) arrays and to study silver catalysis during fabrication. Through investigation of the track of catalyst particles, it was shown that Ag really catalyses the etching of silicon underneath Ag, which clarifies doubts about the formation of SiNW arrays during wet chemical etching. The intrinsic properties of Ag and the network structure of Ag clusters during etching facilitate the etching process. The etching product, i.e. vertical SiNW arrays containing an Ag nanocluster mesh, could be considered as a prototype secondary composite nanostructured catalyst with promise for future applications.

Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs. However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well. In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a onestep neural approach which emphasizes interclause modeling to perform end-to-end extraction. It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking. Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document.

Articles you may be interested inPhysical properties of amorphous InGaZnO 4 films doped with Mn Appl. Phys. Lett. 94, 092504 (2009); 10.1063/1.3095505Structure, magnetization, and low-temperature spin dynamic behavior of zincblende Mn-rich Mn(Ga)As nanoclusters embedded in GaAs

We present a set of white-dwarf-main-sequence (WDMS) binaries identified spectroscopically from the Large sky Area Multi-Object fiber Spectroscopic Telescope (LAM-OST, also called the Guo Shou Jing Telescope) pilot survey. We develop a color selection criteria based on what is so far the largest and most complete Sloan Digital Sky Survey (SDSS) DR7 WDMS binary catalog and identify 28 WDMS binaries within the LAM-OST pilot survey. The primaries in our binary sample are mostly DA white dwarfs except for one DB white dwarf. We derive the stellar atmospheric parameters, masses, and radii for the two components of 10 of our binaries. We also provide cooling ages for the white dwarf primaries as well as the spectral types for the companion stars of these 10 WDMS binaries. These binaries tend to contain hot white dwarfs and early-type companions. Through cross-identification, we note that nine binaries in our sample have been published in the SDSS DR7 WDMS binary catalog. Nineteen spectroscopic WDMS binaries identified by the LAMOST pilot survey are new. Using the 3σ radial velocity variation as a criterion, we find two post-common-envelope binary candidates from our WDMS binary sample.