Dependence of electronic and optical properties on a high-frequency field for carbon nanotubes

We study theoretically the electronic structure, transport and optical properties for a zigzag single-wall carbon nanotube connected to two normal conductor leads under the irradiation of an external electromagnetic field at low temperatures, with particular emphasis on the features of high-frequency response. Using the standard nonequilibrium Green's function techniques, we examine the time-averaged density of states, the conductivity, the dielectric function and the electron energy loss spectra for the system with photon polarization parallel with the tunneling current direction, respectively. Through some numerical examples, it is shown that the density of states is strongly dependent on the incident electron energy, the strength and frequency of the applied field. For higher electron energies in comparison with lead-nanotube coupling energy, the system conductance decreases with increasing the field strength and increases with increasing the field frequency respectively, and shows some oscillation structures. Moreover, the optical functions for the system have also a rich structure with the variation of field frequency. It may demonstrate that this transport dependence on the external field parameters can be used to give the energy spectra information of carbon nanotubes and to detect the high-frequency microwave irradiation.Comment: 6 Revtex pages, 4 figures. to be appeared in JA

How to Train Your Agent to Read and Write

Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (\eg{students}) to fully {grasp} this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. Although there have been existing works focusing on summarizing (\emph{i.e.}, reading) the knowledge in a given text or generating (\emph{i.e.}, writing) a text based on the given knowledge, the ability of simultaneously reading and writing is still under development. Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice. In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a \textit{Reader} that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text \textit{Writer} that generates a novel paragraph, and a \textit{Reviewer} that reviews the generated paragraph from three different aspects. Extensive experiments show that our DRAW network outperforms considered baselines and several state-of-the-art methods on AGENDA and M-AGENDA datasets. Our code and supplementary are released at https://github.com/menggehe/DRAW

Deep Learning-Based Modeling of 5G Core Control Plane for 5G Network Digital Twin

Digital twin is a key enabler to facilitate the development and implementation of new technologies in 5G and beyond networks. However, the complex structure and diverse functions of the current 5G core network, especially the control plane, lead to difficulties in building the core network of the digital twin. In this paper, we propose two novel data-driven architectures for modeling the 5G control plane and implement corresponding deep learning models, namely 5GC-Seq2Seq and 5GC-former, based on the Vanilla Seq2Seq model and Transformer decoder respectively. To train and test models, we also present a solution that allows the signaling messages to be interconverted with vectors, which can be utilized in dataset construction. The experiments are based on 5G core network signaling data collected by the Spirent C50 network tester, including various procedures related to registration, handover, PDU sessions, etc. Our results show that 5GC-Seq2Seq achieves over 99.98% F1-score (A metric to measure the accuracy of positive samples) with a relatively simple structure, while 5GC-former attains higher than 99.998% F1-score by establishing a more complex and highly parallel model, indicating that the method proposed in this paper reproduces the major functions of the core network control plane in 5G digital twin with high accuracy

SpOctA: A 3D Sparse Convolution Accelerator with Octree-Encoding-Based Map Search and Inherent Sparsity-Aware Processing

Point-cloud-based 3D perception has attracted great attention in various applications including robotics, autonomous driving and AR/VR. In particular, the 3D sparse convolution (SpConv) network has emerged as one of the most popular backbones due to its excellent performance. However, it poses severe challenges to real-time perception on general-purpose platforms, such as lengthy map search latency, high computation cost, and enormous memory footprint. In this paper, we propose SpOctA, a SpConv accelerator that enables high-speed and energy-efficient point cloud processing. SpOctA parallelizes the map search by utilizing algorithm-architecture co-optimization based on octree encoding, thereby achieving 8.8-21.2x search speedup. It also attenuates the heavy computational workload by exploiting inherent sparsity of each voxel, which eliminates computation redundancy and saves 44.4-79.1% processing latency. To optimize on-chip memory management, a SpConv-oriented non-uniform caching strategy is introduced to reduce external memory access energy by 57.6% on average. Implemented on a 40nm technology and extensively evaluated on representative benchmarks, SpOctA rivals the state-of-the-art SpConv accelerators by 1.1-6.9x speedup with 1.5-3.1x energy efficiency improvement.Comment: Accepted to ICCAD 202

Galactic Phylogenetics

Phylogenetics is a widely used concept in evolutionary biology. It is the reconstruction of evolutionary history by building trees that represent branching patterns and sequences. These trees represent shared history, and it is our intention for this approach to be employed in the analysis of Galactic history. In Galactic archaeology the shared environment is the interstellar medium in which stars form and provides the basis for tree-building as a methodological tool. Using elemental abundances of solar-type stars as a proxy for DNA, we built in Jofre et al 2017 such an evolutionary tree to study the chemical evolution of the solar neighbourhood. In this proceeding we summarise these results and discuss future prospects.Comment: Contribution to IAU Symposium No. 334: Rediscovering our Galax

Utility of cone-beam CT imaging for the determination of feeding vessels during arterial embolization for massive hemoptysis

PURPOSE:We aimed to evaluate the role of cone-beam computed tomography (CT) performed as an adjunct to angiography for the determination of feeding vessels responsible for bleeding during arterial embolization for massive hemoptysis.METHODS:In this retrospective study, 23 patients with massive hemoptysis who underwent cone-beam CT evaluation prior to arterial embolization from December 2014 to December 2017 were included. During the angiographic session, two interventional radiologists selected the possible feeding vessels that were likely to supply the bleeding target lesions. Contrast-enhanced cone-beam CT was performed at the indefinite feeding arteries as an adjunct to angiography to determine whether the artery was a real feeding vessel, based on whether the target lesion was detected in the perfused territory of the study artery on images.RESULTS:Selective cone-beam CT was successfully performed in 21 patients, at 26 possible feeding vessels that were detected by selective angiography. Cone-beam CT determined the feeding vessel in 24 arteries (92.3%) in 19 patients (90.5%). As a result of cone-beam CT findings, 16 of 24 study arteries were judged as definitively not feeding vessels (66.7%) and the remaining 8 study arteries were judged as definitively feeding vessels (33.3%). In 2 of 26 study arteries, cone-beam CT could not determine the feeding vessel (7.7%).CONCLUSION:Cone-beam CT performed as an adjunctive technique to angiography is sufficient to provide adequate information for confident determination of the feeding vessel, which is essential for the operators to perform accurate embolization during arterial embolization for massive hemoptysis