The Manin-Peyre conjecture for three del Pezzo surfaces

The Manin-Peyre conjecture is established for a split singular quintic del Pezzo surface with singularity type $\mathbf{A}_2$ and two split singular quartic del Pezzo surfaces with singularity types $\mathbf{A}_3+\mathbf{A}_1$ and $\mathbf{A}_4$ respectively. We use a unified and different slightly method from the previous and improve their error terms. Our method is general and can handle most of singular del Pezzo surfaces of degree $d\geq 3$.Comment: 38 pages. minor changes on Table 1. Comments welcome

Feasibility of 3D tracking and adaptation of VMAT based on VMAT-CT

Background: Local computed tomography (CT) reconstruction is achievable with portal images acquired during volumetric-modulated arc therapy (VMAT) delivery and was named as VMAT-CT. However, the application of VMAT-CT is limited because it has limited field of view and no density information. In addition, the new generation of multi-leaf collimator with faster speed and various collimator angles used in patients’ plans could cause more artifacts in VMAT-CT. The goal of this study was to extend VMAT-CT concept, generate complete three-dimensional (3D) CT images, calculate new 3D dose, track and adapt VMAT plan based on updated images and dose. Materials and methods: VMAT-CT and planning CT of phantoms were fused by rigid or deformable registration to create VMAT-CT+ images. Trackings based on planning CT, VMAT-CT+, and cone beam CT (CBCT) were compared. When prescription dose was not met for planning target volume (PTV), re-planning was demonstrated on an in-house deformable phantom. Possible uncertainties were also evaluated. Results: Tracking based on VMAT-CT+ was accurate and superior to those based on planning CT and CBCT since VMAT-CT+ can detect changes during treatment. PTV coverage in the deformable phantom decreased after deformations but went up and met the prescription goal after re-planning. The impact of uncertainties on dose was minimal. Conclusion: 3D tracking and adaptation of VMAT based on VMAT-CT are feasible. Our study has the potential to increase the confidence of beam delivery, catch and remedy errors during VMAT

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

SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization

Transfer learning has fundamentally changed the landscape of natural language processing (NLP) research. Many existing state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely large capacity of pre-trained models, aggressive fine-tuning often causes the adapted model to overfit the data of downstream tasks and forget the knowledge of the pre-trained model. To address the above issue in a more principled manner, we propose a new computational framework for robust and efficient fine-tuning for pre-trained language models. Specifically, our proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the capacity of the model; 2. Bregman proximal point optimization, which is a class of trust-region methods and can prevent knowledge forgetting. Our experiments demonstrate that our proposed method achieves the state-of-the-art performance on multiple NLP benchmarks.Comment: The 58th annual meeting of the Association for Computational Linguistics (ACL 2020

Long-term in situ observations on typhoon-triggered turbidity currents in the deep sea

This work is supported by the National Science Foundation of China (grants 91528304, 41576005, and 41530964). We thank J. Li, X. Lyu, P. Li, K. Duan, J. Ronan, Y. Wang, P. Ma, and Y. Li for cruise assistance; G. de Lange and J. Hinojosa for editing an early version of manuscript; and E. Pope and two anonymous reviewers for their reviews.Peer reviewedPublisher PD

Do economic complexity and trade diversification promote green growth in the BRICTS region? Evidence from advanced panel estimations

Green growth is a comprehensive and integrated approach that ensures the potential economic deliverables of the natural capital on a sustainable basis. Existing studies have explored various deriving factors of green growth. However, none of the studies has evaluated the combined effect of economic complexity, trade diversification, renewable energy consumption, and environment-related taxes to promote green growth. Therefore, this study quantified the impact of these variables on achieving green growth goals for BRICTS countries (Brazil, Russian Federation, India, China, Turkey, and South Africa) from 1995 to 2018. The study addressed the potential econometric issues of panel data, such as cross-section dependency, slope heterogeneity, data nonstationary through robust testing. Cross- Sectional ARDL has been applied to investigate the long-run and short-run association among the study variables. The findings suggest that economic complexity, trade diversification, renewable energy consumption, and environment-related taxes significantly drive green growth in BRICTS countries. However, their marginal contribution substantially varied. Similar results are endorsed using alternative estimators and offer pertinent policy implications