General, Strong Impurity-Strength Dependence of Quasiparticle Interference

Quasiparticle interference (QPI) patterns in momentum space are often assumed to be independent of the strength of the impurity potential when compared with other quantities, such as the joint density of states. Here, using the $T$-matrix theory, we show that this assumption breaks down completely even in the simplest case of a single-site impurity on the square lattice with an $s$ orbital per site. Then, we predict from first-principles, a very rich, impurity-strength-dependent structure in the QPI pattern of TaAs, an archetype Weyl semimetal. This study thus demonstrates that the consideration of the details of the scattering impurity including the impurity strength is essential for interpreting Fourier-transform scanning tunneling spectroscopy experiments in general.Comment: main manuscript: 8 pages, 6 figures, Supplementary Information: 3 pages, 6 figure

A new species of torrent catfish, Liobagrus geumgangensis (Teleostei, Siluriformes, Amblycipitidae), from Korea

In a recent survey of populations of the Korean torrent catfish Liobagrus, a distinctive species was discovered from the Geum River and its tributaries flowing into the western coast of Korea, and here described as a new species, L. geumgangensis sp. nov. It is distinguishable from other congeners by a combination of the following characters: I, 8 pectoral fin-rays; 52–56 caudal-fin rays; a relatively short occiput to dorsal-fin origin distance (6.9–9.8% SL); a short pelvic-fin insertion to anal-fin origin distance (11.9–17.3% SL); a long dorsal-fin base (10.6–13.5% SL); 8–9 gill rakers; 5–8 serrations on the pectoral fin; the body and fins are dark yellow, the margins of the dorsal, anal, and caudal fins are dark brown, but the outermost rim is faintly yellow. Analysis of the cytb gene also confirmed that L. geumgangensis is a monophyletic lineage distinct from other congeners

Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks

Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution.Comment: accepted for publication on IEEE Wireless Communications Letter

I-gel as a first-line airway device in the emergency room for patients with out-of-hospital cardiac arrest

Aim. The optimal method for advanced airway management during cardiac arrest remains controversial. Most patients with out-of-hospital cardiac arrest (OHCA) in Korea are managed with a bag-valve mask by paramedics, while physicians perform advanced airway management in emergency departments (ED). Endotracheal intubation (ETI) has a risk of failure at the first attempt. By contrast, I-gel, a supraglottic airway device, is easier to insert than an endotracheal tube and shows a higher first-attempt success rate than ETI in out-of-hospital settings by paramedics in the United States. We reviewed the use of ETI and I-gel by ED physicians to assess the first attempt success rate in a hospital setting. Methods. We conducted a retrospective chart review of patients with non-traumatic OHCA who were managed with either ETI using a Macintosh laryngoscope, or I-gel in the ED of Korean hospital from January 2012 to January 2014. Results. Of 322 adult patients with non-traumatic OHCA, 160 received I-gel and 162 received ETI. The first-attempt success rate was higher in the I-gel group (96.9%) than in the ETI group (84.6%, p < 0.001). The time from arrival to obtaining advanced airway management was shorter in the I-gel group than in the ETI group. Conclusions. I-gel showed a better first-attempt success rate and shorter insertion time compared with ETI when performed by physicians in a hospital setting

A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization

Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to the capability of handling nonconvex formulations. However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i.e., the number of antennas or users. This paper develops a bipartite graph neural network (BGNN) framework, a scalable DL solution designed for multi-antenna beamforming optimization. The MU-MISO system is first characterized by a bipartite graph where two disjoint vertex sets, each of which consists of transmit antennas and users, are connected via pairwise edges. These vertex interconnection states are modeled by channel fading coefficients. Thus, a generic beamforming optimization process is interpreted as a computation task over a weight bipartite graph. This approach partitions the beamforming optimization procedure into multiple suboperations dedicated to individual antenna vertices and user vertices. Separated vertex operations lead to scalable beamforming calculations that are invariant to the system size. The vertex operations are realized by a group of DNN modules that collectively form the BGNN architecture. Identical DNNs are reused at all antennas and users so that the resultant learning structure becomes flexible to the network size. Component DNNs of the BGNN are trained jointly over numerous MU-MISO configurations with randomly varying network sizes. As a result, the trained BGNN can be universally applied to arbitrary MU-MISO systems. Numerical results validate the advantages of the BGNN framework over conventional methods.Comment: accepted for publication on IEEE Transactions on Wireless Communication

Deep Learning Methods for Universal MISO Beamforming

This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.Comment: to appear in IEEE Wireless Communications Letters (5 pages, 3 figures, 2 tables

Effects of mutation at a conserved N-glycosylation site in the bovine retinal cyclic nucleotide-gated ion channel

AbstractBovine retinal cyclic nucleotide-gated (CNG) ion channel contains an evolutionary conserved N-glycosylation site in the external loop between the fifth transmembrane segment and the pore-forming region. The effect of tunicamycin treatment and the site-specific mutation suggested that the channel is glycosylated when expressed in Xenopus oocytes. To test the role of glycosylation in this channel, N-glycosylation was abolished by mutation, and the detailed permeation and the gating characteristics of the mutant channel were investigated. The charge contribution turned out to be detectable, although the mutation of the N-glycosylation site did not affect expression and functionality of the CNG channel in oocytes