Positive radial solutions for a class of quasilinear Schrödinger equations in R3

This paper is concerned with the following quasilinear Schrödinger equations of the form: −∆u − u∆(u 2 ) + u = |u| p−2u, x ∈ R 3 where p ∈ (2, 12). By making use of the constrained minimization method on a special manifold, we prove that the existence of positive radial solutions of the above problem for any p ∈ (2, 12)

Self-optimization wavelet-learning method for predicting nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations

In the present work, we propose a self-optimization wavelet-learning method (SO-W-LM) with high accuracy and efficiency to compute the equivalent nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations. The randomly structural heterogeneity, temperature-dependent nonlinearity and material property uncertainty of heterogeneous materials are considered within the proposed self-optimization wavelet-learning framework. Firstly, meso- and micro-structural modeling of random heterogeneous materials are achieved by the proposed computer representation method, whose simulated hierarchical configurations have relatively high volume ratio of material inclusions. Moreover, temperature-dependent nonlinearity and material property uncertainties of random heterogeneous materials are modeled by a polynomial nonlinear model and Weibull probabilistic model, which can closely resemble actual material properties of heterogeneous materials. Secondly, an innovative stochastic three-scale homogenized method (STSHM) is developed to compute the macroscopic nonlinear thermal conductivity of random heterogeneous materials. Background meshing and filling techniques are devised to extract geometry and material features of random heterogeneous materials for establishing material databases. Thirdly, high-dimensional and highly nonlinear material features of material databases are preprocessed and reduced by wavelet decomposition technique. The neural networks are further employed to excavate the predictive models from dimension-reduced low-dimensional data

An Approximate Proximal Point Algorithm for Maximal Monotone Inclusion Problems

This paper presents and analyzes a strongly convergent approximate proximal point algorithm for finding zeros of maximal monotone operators in Hilbert spaces. The proposed method combines the proximal subproblem with a more general correction step which takes advantage of more information on the existing iterations. As applications, convex programming problems and generalized variational inequalities are considered. Some preliminary computational results are reported

Weak Supervision for Fake News Detection via Reinforcement Learning

Today social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.Comment: AAAI 202

Pharmacokinetics of lansoprazole injection in peptic ulcer and healthy volunteers

The pharmacokinetics of lansoprazole after a single intravenous dose of 30 mg was determined in 10 healthy volunteers and 10 peptic ulcers patients. In this work, a liquid-liquid extraction and enrichment method with RP-HPLC determination route was taken with high sensitivity and low limit detection of 5 ng/mL. The concentration-time curves in the two groups were best fitted to a two-compartment model, but their main kinetic parameters were remarkably different between healthy and ulcers volunteers. The mean maximum plasma concentration (Cmax ) and area under the curve (AUC0t ) were increased from 975.8 ng/mL to 1298.7 ng/mL and from 1439 ng·h/mL to 2301 ng·h/mL, respectively, and peak time (tmax ) decreased from 0.36 h to 0.26 h. Meanwhile, the half life (t1/2 ) prolonged from 2.25 h to 2.91 h and the clearance (CL) reduced from 20.04 L/h to 13.96 L/h. That variability of lansoprazole pharmakinetic parameter indicates that ulcers have significant effect on its metabolic process.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Re-channelization of turbidity currents in South China Sea abyssal plain due to seamounts and ridges

Turbidity currents can be characterized as net-erosive, net-depositional or net-bypassing. Whether a flow is erosive, depositional or bypasses depends on the flow velocity, concentration and size but these can also be impacted by external controls such as the degree of confinement, slope gradient and substrate type and erodibility. Our understanding of the relative importance of these controls comes from laboratory experiments and numerical modelling, as well as from field data due to the proliferation of high-resolution 3D seismic and bathymetric data, as well as the outcrop and rock record. In this study, based on extensive multibeam and seismic reflection surveys in combination with International Ocean Discovery Program cores from the South China Sea, we document a new mechanism of turbidity current transformation from depositional to erosive resulting in channel incision. We show how confinement by seamounts and bedrock highs of previously unconfined turbidity currents has resulted in the development of seafloor channels. These channels are inferred to be the result of confinement of flows, which have traversed the abyssal plain, leading to flow acceleration allowing them to erode the seafloor substrate. This interpretation is further supported by the coarsening of flow deposits within the area of the seamounts, indicating that confinement has increased flow competency, allowing turbidity currents to carry larger volumes of coarse sediment which has been deposited in this region. This basin-scale depositional pattern suggests that pre-established basin topography can have an important control on sedimentation which can impact characteristics such as potential hydrocarbon storage