Observation of Exciton-Phonon Sideband in Individual Metallic Single-Walled Carbon Nanotubes

Single-walled carbon nanotubes (SWCNTs) are quasi-one-dimensional systems with poor Coulomb screening and enhanced electron-phonon interaction, and are good candidates for excitons and exciton-phonon couplings in metallic state. Here we report back scattering reflection experiments on individual metallic SWCNTs. An exciton-phonon sideband separated by 0.19 eV from the first optical transition peak is observed in a metallic SWCNT of chiral index (13,10), which provides clear evidences of excitons in metallic SWCNTs. A static dielectric constant of 10 is estimated from the reflectance spectrum.Comment: 5 pages, 3 figures; typos corrected, references updated, text re-arrange

A cooperative domain model for multiple phase transitions and complex conformational relaxations in polymers with shape memory effect

Shape memory polymers (SMPs) are thermo-rheologically complex materials showing significant temperature and time dependences. Their segments often undergo cooperative phase transitions and conformational relaxations simultaneously along with shape memory effect (SME). In this study, a cooperative domain model is proposed to describe the composition dependence, multiple phase transitions and conformational relaxations of SMPs within their glass transition zones. Variations in local-area compositions and cooperative domains of the amorphous SMPs cause significant differences in their segmental relaxation. At a fixed domain size, both intermolecular activation energy and relaxation time significantly influence the SME and thermomechanical properties of the SMPs. Finally, the model is successfully applied to predict the shape memory behavior of SMPs with one stage SME and triple-SME, and the theoretical results have been validated by the experimental ones. This model could be a powerful tool to understand the working mechanisms and provide a theoretical guidance for the designs of multi-SME in SMPs

TI-DNS: A Trusted and Incentive DNS Resolution Architecture based on Blockchain

Domain Name System (DNS) is a critical component of the Internet infrastructure, responsible for translating domain names into IP addresses. However, DNS is vulnerable to some malicious attacks, including DNS cache poisoning, which redirects users to malicious websites displaying offensive or illegal content. Existing countermeasures often suffer from at least one of the following weakness: weak attack resistance, high overhead, or complex implementation. To address these challenges, this paper presents TI-DNS, a blockchain-based DNS resolution architecture designed to detect and correct the forged DNS records caused by the cache poisoning attacks in the DNS resolution process. TI-DNS leverages a multi-resolver Query Vote mechanism to ensure the credibility of verified records on the blockchain ledger and a stake-based incentive mechanism to promote well-behaved participation. Importantly, TI-DNS is easy to be adopted as it only requires modifications to the resolver side of current DNS infrastructure. Finally, we develop a prototype and evaluate it against alternative solutions. The result demonstrates that TI-DNS effectively and efficiently solves DNS cache poisoning

A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects

The recent development of deep learning combined with compressed sensing enables fast reconstruction of undersampled MR images and has achieved state-of-the-art performance for Cartesian k-space trajectories. However, non-Cartesian trajectories such as the radial trajectory need to be transformed onto a Cartesian grid in each iteration of the network training, slowing down the training process and posing inconvenience and delay during training. Multiple iterations of nonuniform Fourier transform in the networks offset the deep learning advantage of fast inference. Current approaches typically either work on image-to-image networks or grid the non-Cartesian trajectories before the network training to avoid the repeated gridding process. However, the image-to-image networks cannot ensure the k-space data consistency in the reconstructed images and the pre-processing of non-Cartesian k-space leads to gridding errors which cannot be compensated by the network training. Inspired by the Transformer network to handle long-range dependencies in sequence transduction tasks, we propose to rearrange the radial spokes to sequential data based on the chronological order of acquisition and use the Transformer to predict unacquired radial spokes from acquired ones. We propose novel data augmentation methods to generate a large amount of training data from a limited number of subjects. The network can be generated to different anatomical structures. Experimental results show superior performance of the proposed framework compared to state-of-the-art deep neural networks.Comment: Accepted at MICCAI 202