Study on Modification of Lignin as Dispersant of Aqueous Graphene Suspension and Corrosion Performance in Waterborne G/Epoxy Coating

Though graphene (G) as an excellent protective material for metal, it can aggravate metal corrosion in other side. The modification of sodium lignin sulfonate was achieved by using itaconic acid and acrylamide，which was proved by UV-vis and Raman spectra. The modified sodium lignin sulfonate (LAI) with more carboxylic groups can be used as the dispersant for aqueous graphene suspension. The commercial graphene can be dispersed uniformly and stability in water via π-π interaction with LAI at high concentration (6 mg/mL)，and the LAI-G system can be used as an inhibitor in waterborne epoxy coatings too. Electrochemical impedance spectroscope (EIS) and Tafel polarization curves showed that the corrosion performance of waterborne epoxy system with well-dispersed G (0.5 wt %) was remarkably improved compared with pure epoxy coating

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201

Numerical Investigation of the Combined Influence of Shield Tunneling and Pile Cutting on Underpinning Piles

In this study, the combined influence of shield tunneling and the old pile cutting process on underpinning piles is investigated through finite element method (FEM) modeling based on a shield tunnel project in Nanchang Metro Line 2, China. Numerical models have been developed to analyze the influence of intersection angles and the vertical distance between the underpinning foundation and tunnels on the mechanical responses of underpinning piles during tunnel excavation. Simulation results show that the bending moment of the underpinning piles decreases with increasing vertical distance between the pile and tunnel, and is inversely proportional to the intersection angle between the underpinning beam and tunnel. In addition, the maximum pile bending moment occurs in the buried depth of the tunnel axis, indicating a high risk of damage in this part. According to the simulation results, more attention should be given to the underpinning piles in case a small vertical distance and intersection angle are encountered

Free-Form Composition Networks for Egocentric Action Recognition

Egocentric action recognition is gaining significant attention in the field of human action recognition. In this paper, we address data scarcity issue in egocentric action recognition from a compositional generalization perspective. To tackle this problem, we propose a free-form composition network (FFCN) that can simultaneously learn disentangled verb, preposition, and noun representations, and then use them to compose new samples in the feature space for rare classes of action videos. First, we use a graph to capture the spatial-temporal relations among different hand/object instances in each action video. We thus decompose each action into a set of verb and preposition spatial-temporal representations using the edge features in the graph. The temporal decomposition extracts verb and preposition representations from different video frames, while the spatial decomposition adaptively learns verb and preposition representations from action-related instances in each frame. With these spatial-temporal representations of verbs and prepositions, we can compose new samples for those rare classes in a free-form manner, which is not restricted to a rigid form of a verb and a noun. The proposed FFCN can directly generate new training data samples for rare classes, hence significantly improve action recognition performance. We evaluated our method on three popular egocentric action recognition datasets, Something-Something V2, H2O, and EPIC-KITCHENS-100, and the experimental results demonstrate the effectiveness of the proposed method for handling data scarcity problems, including long-tailed and few-shot egocentric action recognition

Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task. However, the absence of a systematical benchmark inhibits the development of designing effective, efficient and economic LLM-based Text-to-SQL solutions. To address this challenge, in this paper, we first conduct a systematical and extensive comparison over existing prompt engineering methods, including question representation, example selection and example organization, and with these experimental results, we elaborate their pros and cons. Based on these findings, we propose a new integrated solution, named DAIL-SQL, which refreshes the Spider leaderboard with 86.6% execution accuracy and sets a new bar. To explore the potential of open-source LLM, we investigate them in various scenarios, and further enhance their performance with supervised fine-tuning. Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well as the advantages and disadvantages of the supervised fine-tuning. Additionally, towards an efficient and economic LLM-based Text-to-SQL solution, we emphasize the token efficiency in prompt engineering and compare the prior studies under this metric. We hope that our work provides a deeper understanding of Text-to-SQL with LLMs, and inspires further investigations and broad applications.Comment: We have released code on https://github.com/BeachWang/DAIL-SQ

Modeling the mid-piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS)

The mid-Piacenzian Warm Period (MPWP, ~ 3.264–3.025 Ma) is the most recent example of a persistently warmer climate in equilibrium with atmospheric CO2 concentrations similar to today. Towards studying patterns and dynamics of a warming climate the MPWP is often compared to today. Following the Pliocene Model Intercomparison Project, Phase 2 (PlioMIP2) protocol we prepare a water isotope-enabled Community Earth System Model (iCESM1.2) simulation that is warmer and wetter than the PlioMIP2 multi-model ensemble (MME). While our simulation resembles PlioMIP2 MME in many aspects we find added insights. (1) Considerable warmth at high latitudes exceeds previous simulations. Polar amplification (PA) is comparable to proxies, enabled by iCESM1.2’s high climate sensitivity and a distinct method of ocean initialization. (2) Major driver of warmth is the downward component of clear-sky surface long-wave radiation. (3) In iCESM1.2 modulated dominance of dynamic (δDY) processes causes different low-latitude (~ 30 S°–10°N) precipitation response than the PlioMIP2 MME, where thermodynamic processes (δTH) dominate. (4) Modulated local condensation leads to lower δ18O across tropical Indian Ocean and surrounding Asian-African-Australian monsoon regions. (5) We find contrasting changes in tropical atmospheric circulations (Hadley and Walker cells). Anomalous regional meridional (zonal) circulation, forced by changes in tropical-subtropical (tropical) diabatic processes, presents a more comprehensive perspective than explaining weakened and expanded Hadley circulation (strengthened and westward-shifted Walker circulation) via static stability. (6) Enhanced Atlantic meridional overturning circulation owes to a closed Bering Strait

Phytoplankton blooms with sequential cyclonic and anticyclonic eddies during the passage of tropical cyclone Hibaru

Two phytoplankton blooms triggered by the tropical cyclone (TC) Hibaru were studied in the Bay of Bengal. Hibaru occurred in southeastern Sri Lanka in January 2005. After the passage of Hibaru, two strong phytoplankton blooms appeared in the study area (3.5° N-6° N, 83.5° E-88.5° E). In this study, the dynamic mechanisms were investigated with remote sensing, multisource reanalysis products and Argo float data. The first bloom on January 19 to 20 was induced by upwelling with the upper cyclonic eddy and mixed entrainment caused by Hibaru, where the maximum chlorophyll a (Chl-a) concentration was 0.235 mg•m–3. Sea surface cooling and heavy rainfall also occurred. The second bloom from January 27 to 28 was triggered by the interaction of the upper cyclonic eddy and submarine anticyclonic eddy after the passage of Hibaru, where the maximum of the Chl-a concentration was 0.124 mg•m–3. With the submarine anticyclonic eddy and weakened barrier layer thickness (BLT), the subsurface horizontally converged chlorophyll and nutrient water was uplifted with upwelling. This study contributes to the assessment of the ecological impact of ocean eddies during the passage of TC in the Bay of Bengal