A Neural RDE-based model for solving path-dependent PDEs

The concept of the path-dependent partial differential equation (PPDE) was first introduced in the context of path-dependent derivatives in financial markets. Its semilinear form was later identified as a non-Markovian backward stochastic differential equation (BSDE). Compared to the classical PDE, the solution of a PPDE involves an infinite-dimensional spatial variable, making it challenging to approximate, if not impossible. In this paper, we propose a neural rough differential equation (NRDE)-based model to learn PPDEs, which effectively encodes the path information through the log-signature feature while capturing the fundamental dynamics. The proposed continuous-time model for the PPDE solution offers the benefits of efficient memory usage and the ability to scale with dimensionality. Several numerical experiments, provided to validate the performance of the proposed model in comparison to the strong baseline in the literature, are used to demonstrate its effectiveness

3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation

With the popularity of 3D sensors in self-driving and other robotics applications, extensive research has focused on designing novel neural network architectures for accurate 3D point cloud completion. However, unlike in point cloud classification and reconstruction, the role of adversarial samples in3D point cloud completion has seldom been explored. In this work, we show that training with adversarial samples can improve the performance of neural networks on 3D point cloud completion tasks. We propose a novel approach to generate adversarial samples that benefit both the performance of clean and adversarial samples. In contrast to the PGD-k attack, our method generates adversarial samples that keep the geometric features in clean samples and contain few outliers. In particular, we use principal directions to constrain the adversarial perturbations for each input point. The gradient components in the mean direction of principal directions are taken as adversarial perturbations. In addition, we also investigate the effect of using the minimum curvature direction. Besides, we adopt attack strength accumulation and auxiliary Batch Normalization layers method to speed up the training process and alleviate the distribution mismatch between clean and adversarial samples. Experimental results show that training with the adversarial samples crafted by our method effectively enhances the performance of PCN on the ShapeNet dataset.Comment: 11 page, 5 figure

Operando STM study of the interaction of imidazolium-based ionic liquid with graphite

Understanding interactions at the interfaces of carbon with ionic liquids (ILs) is crucially beneficial for the diagnostics and performance improvement of electrochemical devices containing carbon as active materials or conductive additives in electrodes and ILs as solvents or additives in electrolytes. The interfacial interactions of three typical imidazolium-based ILs, 1-alkyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide (AMImTFSI) ILs having ethyl (C2), butyl (C4) and octyl (C8) chains in their cations, with highly oriented pyrolytic graphite (HOPG) were studied in-situ by electrochemical scanning tunneling microscopy (EC-STM). The etching of HOPG surface and the exfoliation of graphite/graphene flakes as well as cation intercalation were observed at the HOPG/C2MImTFSI interface. The etching also takes place in C4MImTFSI at −1.5 V vs Pt but only at step edges with a much slower rate, whereas C8MIm+ cations adsorbs strongly on the HOPG surface under similar conditions with no observable etching or intercalation. The EC-STM observations can be explained by the increase in van der Waals interaction between the cations and the graphite surface with increasing length of alkyl chains

Dominant factor affecting Pb speciation and the leaching risk among land- use types around Pb-Zn mine

Soil lead (Pb) pollution around the mining area has severely threaten human health. However, Pb leaching risk in soils with different land uses and which is the proper land use are still unknown. In this work, Pb speciation characteristics and the dominant soil factors affecting Pb speciation in three land uses (farmland, woodland, and grassland) surrounding the Pb-Zn mine in Feng Country, Shaanxi province were investigated. Moreover, the Pb leaching risk and associated determining factors were evaluated by the combination of leached Pb concentration and structural equation model (SEM). The results showed that farmland presented the highest total Pb content (410.1 mg kg(-1)) among three land use types. The reducible fraction of Pb (Fe-Mn oxides bound) was the major speciation ( > 50%) in all tested soils of three land-use types. Soil total phosphorus (TP), water content (WC), and pH play major role in regulating Pb speciation. Though soil biological properties, like microbial communities, catalase, and microbial biomass nitrogen (MBN) exhibited distinct responses to three different land uses, they showed minor influence on Pb speciation. More interestingly, SEM analysis indicated that Pb leaching risk was directly linked with bacteria abundance, total Pb content, clay content, and C/N. Grassland presented the higher predicted Pb leaching concentration (85.03 mg kg(-1)), compared with that in woodland, suggesting that grassland was the worst land-use type to buffer the Pb toxicity. Woodland could be recommended as the proper native land use to alleviate environmental risk. Overall, our results demonstrated the dominant factor to regulate Pb speciation and pointed out the proper land-use in relieving Pb leaching risk around Pb-Zn mine. These finding provides the new strategies to the remediation and management of metal-contaminated soil

Clustering Optimized Portrait Matting Algorithm Based on Improved Sparrow Algorithm

As a result of the influence of individual appearance and lighting conditions, aberrant noise spots cause significant mis-segmentation for frontal portraits. This paper presents an accurate portrait segmentation approach based on a combination of wavelet proportional shrinkage and an upgraded sparrow search (SSA) clustering algorithm to solve the accuracy challenge of segmentation for frontal portraits. The brightness component of the human portrait in HSV space is first subjected to wavelet scaling denoising. The elite inverse learning approach and adaptive weighting factor are then implemented to optimize the initial center location of the K-Means algorithm to improve the initial distribution and accelerate the convergence speed of SSA population members. The pixel segmentation accuracy of the proposed method is approximately 70% and 15% higher than two comparable traditional methods, while the similarity of color image features is approximately 10% higher. Experiments show that the proposed method has achieved a high level of accuracy in capricious lighting conditions

Parental Perceptions of Oral Health and School-Based Dental Sealant Programs

Introduction: Community Health Needs Assessment (University of Vermont Medical Center, 2013) Identified oral health in pediatric population as a primary concern Barriers to dental care cited: access, affordability, education School-Based Sealant Program (SBSP) Dental sealants are an evidence-based method of cavity prevention CDC strongly recommends delivery via SBSPs Few Vermont schools have such a program Vermont Medicaid State Plan amendment allows dental hygienists to bill without on-site dentist (2015)4 Unique opportunity to pilot an SBSP Pilot program implemented by the University of Vermont Medical Center Community Health Improvement Goal: sustainable model able to be replicated in Vermont schools Pilot School Selection – Milton Elementary-Middle School (MEMS) Demographics representative of Vermont schools (46% free & reduced lunch program); school administration supportive of an SBSP; no existing dental education (“Tooth Tutor”) program per Vermont Office of Oral Healthhttps://scholarworks.uvm.edu/comphp_gallery/1232/thumbnail.jp