Exponential stability for impulsive delay differential equations by Razumikhin method

AbstractIn this paper, we study exponential stability for impulsive delay differential equation of the form x˙(t)=f(t,xt),t≠tk,Δx(t)=Ik(t,xt−),t=tk,k∈N. By employing the Razumikhin technique and Lyapunov functions, several exponential stability criteria are established. Some examples are also discussed to illustrate our results

Finite-time ruin probability of a perturbed risk model with dependent main and delayed claims

This paper considers a delayed claim risk model with stochastic return and Brownian perturbation in which each main claim may be accompanied with a delayed claim occurring after a stochastic period of time, and the price process of the investment portfolio is described as a geometric Lévy process. By means of the asymptotic results for randomly weighted sum of dependent subexponential random variables we obtain some asymptotics for finite-time ruin probability. A simulation study is also performed to check the accuracy of the obtained theoretical result via the crude Monte Carlo method

Verbal Explanations for Deep Reinforcement Learning Neural Networks with Attention on Extracted Features

In recent years, there has been increasing interest in transparency in Deep Neural Networks. Most of the works on transparency have been done for image classification. In this paper, we report on work of transparency in Deep Reinforcement Learning Networks (DRLNs). Such networks have been extremely successful in learning action control in Atari games. In this paper, we focus on generating verbal (natural language) descriptions and explanations of deep reinforcement learning policies. Successful generation of verbal explanations would allow better understanding by people (e.g., users, debuggers) of the inner workings of DRLNs which could ultimately increase trust in these systems. We present a generation model which consists of three parts: an encoder on feature extraction, an attention structure on selecting features from the output of the encoder, and a decoder on generating the explanation in natural language. Four variants of the attention structure full attention, global attention, adaptive attention and object attention - are designed and compared. The adaptive attention structure performs the best among all the variants, even though the object attention structure is given additional information on object locations. Additionally, our experiment results showed that the proposed encoder outperforms two baseline encoders (Resnet and VGG) on the capability of distinguishing the game state images

Supported CuII Single-Ion Catalyst for Total Carbon Utilization of C2 and C3 Biomass-Based Platform Molecules in the N-Formylation of Amines

The shift from fossil carbon sources to renewable ones is vital for developing sustainable chemical processes to produce valuable chemicals. In this work, value-added formamides were synthesized in good yields by the reaction of amines with C2 and C3 biomass-based platform molecules such as glycolic acid, 1,3-dihydroxyacetone and glyceraldehyde. These feedstocks were selectively converted by catalysts based on Cu-containing zeolite 5A through the in situ formation of carbonyl-containing intermediates. To the best of our knowledge, this is the first example in which all the carbon atoms in biomass-based feedstocks could be amidated to produce formamide. Combined catalyst characterization results revealed preferably single CuII sites on the surface of Cu/5A, some of which form small clusters, but without direct linking via oxygen bridges. By combining the results of electron paramagnetic resonance (EPR) spin-trapping, operando attenuated total reflection (ATR) IR spectroscopy and control experiments, it was found that the formation of formamides might involve a HCOOH-like intermediate and .NHPh radicals, in which the selective formation of .OOH radicals might play a key role. © 2021 The Authors. Chemistry - A European Journal published by Wiley-VCH Gmb

Sentiment Analysis of Name Entity for Text

Abstract-Recent years, big data has attracted increasing interest. Sentiment analysis from microblog as one kind of big data also receive great attention. Some recent research works are not suitable for sentiment analysis as the result that users prefer to express their feelings in individual ways. In this paper, a framework is proposed to calculate sentiment for aspects of event. Based on some state of art technologies, we build up one flowchart to get sentiment for aspects of event. During the process, name entities with the same meaning are clustered and sentiment carrier are filtered. In this way sentiment can be got even user express feeling for the same object with different words