Spare support model based on gamma degradation process

Spare parts ordering is very important in the domain of system support based on condition-based maintenance. For a single-unit system with condition monitoring, a joint degradation and spare parts ordering model is established in this paper to achieve the lowest total cost rate as the objective. The degradation process of system is assumed to be followed a gamma process. A decision on optimal spare ordering time by the improved cost rate model based on the proposed degradation model is made. Finally, a case analysis is implemented to demonstrate the effectiveness of the proposed model in this paper. Analysis results show that the proposed model can reduce the cost rate effectively

A convolutional attentional neural network for sentiment classification

Neural network models with attention mechanism have shown their efficiencies on various tasks. However, there is little research work on attention mechanism for text classification and existing attention model for text classification lacks of cognitive intuition and mathematical explanation. In this paper, we propose a new architecture of neural network based on the attention model for text classification. In particular, we show that the convolutional neural network (CNN) is a reasonable model for extracting attentions from text sequences in mathematics. We then propose a novel attention model base on CNN and introduce a new network architecture which combines recurrent neural network with our CNN-based attention model. Experimental results on five datasets show that our proposed models can accurately capture the salient parts of sentences to improve the performance of text classification

Convolution-based neural attention with applications to sentiment classification

Neural attention mechanism has achieved many successes in various tasks in natural language processing. However, existing neural attention models based on a densely connected network are loosely related to the attention mechanism found in psychology and neuroscience. Motivated by the finding in neuroscience that human possesses the template-searching attention mechanism, we propose to use convolution operation to simulate attentions and give a mathematical explanation of our neural attention model. We then introduce a new network architecture, which combines a recurrent neural network with our convolution-based attention model and further stacks an attention-based neural model to build a hierarchical sentiment classification model. The experimental results show that our proposed models can capture salient parts of the text to improve the performance of sentiment classification at both the sentence level and the document level

Commonsense knowledge enhanced memory network for stance classification

Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification

Empathetic Response Generation with State Management

A good empathetic dialogue system should first track and understand a user's emotion and then reply with an appropriate emotion. However, current approaches to this task either focus on improving the understanding of users' emotion or on proposing better responding strategies, and very few works consider both at the same time. Our work attempts to fill this vacancy. Inspired by task-oriented dialogue systems, we propose a novel empathetic response generation model with emotion-aware dialogue management. The emotion-aware dialogue management contains two parts: (1) Emotion state tracking maintains the current emotion state of the user and (2) Empathetic dialogue policy selection predicts a target emotion and a user's intent based on the results of the emotion state tracking. The predicted information is then used to guide the generation of responses. Experimental results show that dynamically managing different information can help the model generate more empathetic responses compared with several baselines under both automatic and human evaluations

2D/2D Heterojunction of TiO2 Nanoparticles and Ultrathin G-C3N4 Nanosheets for Efficient Photocatalytic Hydrogen Evolution

Photocatalytic hydrogen evolution is considered one of the promising routes to solve the energy and environmental crises. However, developing efficient and low-cost photocatalysts remains an unsolved challenge. In this work, ultrathin 2D g-C3N4 nanosheets are coupled with flat TiO2 nanoparticles as face-to-face 2D/2D heterojunction photocatalysts through a simple electro-static self-assembly method. Compared with g-C3N4 and pure TiO2 nanosheets, 2D/2D TiO2/g-C3N4 heterojunctions exhibit effective charge separation and transport properties that translate into outstanding photocatalytic performances. With the optimized heterostructure com-position, stable hydrogen evolution activities are threefold and fourfold higher than those of pure TiO2, and g-C3N4 are consistently obtained. Benefiting from the favorable 2D/2D heterojunction structure, the TiO2/g-C3N4 photocatalyst yields H2 evolution rates up to 3875 μmol·g with an AQE of 7.16% at 380 nm.R.D.: K.X., X.H., X.W. and C.Z. thank the China Scholarship Council for the scholarship support. IREC and ICN2 acknowledge funding from Generalitat de Catalunya, projects 2017 SGR 1246 and 2017 SGR 327, respectively. The authors thank the support from the project NANOGEN (PID2020-116093RB-C43), funded by MCIN/AEI/10.13039/501100011033/ and the project COMBENERGY (PID2019-105490RB-C32) from the Spanish Ministerio de Ciencia e Innovación. ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCAProgramme / Generalitat de Catalunya. Baoying Li greatly appreciates the financial support from the National Natural Science Foundation of China (Nos. 22171154 & 21801144), the Youth Innovative Talents Recruitment and the Cultivation Program of Shandong Higher Education. This study was supported by MCIN with funding from the European Union NextGenerationEU (PRTR-C17.I1), Generalitat de Catalunya and by “ERDF A way of making Europe” by the “European Union”

Transition-based directed graph construction for emotion-cause pair extraction

Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p<0.01) in F1 measure

Chromium phosphide CrP as highly active and stable electrocatalysts for oxygen electroreduction in alkaline media

Catalysts for oxygen reduction reaction (ORR) are key components in emerging energy technologies such as fuel cells and metal-air batteries. Developing low-cost, high performance and stable electrocatalysts is critical for the extensive implementation of these technologies. Herein, we present a procedure to prepare colloidal chromium phosphide CrP nanocrystals and we test their performance as ORR electrocatalyst. CrP-based catalysts exhibited remarkable activities with a limiting current density of 4.94¿mA¿cm-2 at 0.2¿V, a half-potential of 0.65¿V and an onset potential of 0.8¿V at 1600¿rpm, which are comparable to commercial Pt/C. Advantageously, CrP-based catalysts displayed much higher stabilities and higher tolerances to methanol in alkaline solution. Using density functional theory calculations, we demonstrate CrP to provide a very strong chemisorption of O2 that facilitates its reduction and explains the excellent ORR performance experimentally demonstrated.Postprint (author's final draft

A knowledge regularized hierarchical approach for emotion cause analysis

Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure