Learning from Multi-View Multi-Way Data via Structural Factorization Machines

Real-world relations among entities can often be observed and determined by different perspectives/views. For example, the decision made by a user on whether to adopt an item relies on multiple aspects such as the contextual information of the decision, the item's attributes, the user's profile and the reviews given by other users. Different views may exhibit multi-way interactions among entities and provide complementary information. In this paper, we introduce a multi-tensor-based approach that can preserve the underlying structure of multi-view data in a generic predictive model. Specifically, we propose structural factorization machines (SFMs) that learn the common latent spaces shared by multi-view tensors and automatically adjust the importance of each view in the predictive model. Furthermore, the complexity of SFMs is linear in the number of parameters, which make SFMs suitable to large-scale problems. Extensive experiments on real-world datasets demonstrate that the proposed SFMs outperform several state-of-the-art methods in terms of prediction accuracy and computational cost.Comment: 10 page

A Study on the Training Mode of Electronic Application- Oriented Undergraduate with Industry Needs

Electronic industry is an economic pillar in China. Due to the Moore’s Law, the industry requires continuous development and innovation. In order to achieve these goals, the cultivation of electronic application-oriented undergraduate is essential. However, at current, the innovative educational concepts and teaching methods are lagging behind so that the graduates cannot meet the requirements of electronic industry. In this paper, we adopt a variety of measures, which include the construction of parallel course system, the establishment of cooperative education mechanics and the creation of training evaluation mode, to improve the training quality of electronic application-oriented undergraduate. The research findings show that the undergraduates improve their engineering practices and innovation abilities

A novel numerical implementation of electrochemical-thermal battery model for electrified powertrains with conserved spherical diffusion and high efficiency

The performance of batteries in electrified powertrain systems is highly influenced by mass diffusion and electrochemistry which are often ignored in the simulation of these systems due to the lack of a conserved, efficient, and integrable battery model. Therefore, this work numerically implements an electrochemical-thermal battery model with conserved numerical schemes and efficient numerical methods which include Jacobian-based and Jacobian-Free Newton Krylov (JFNK) solvers. The performance of the developed model is evaluated by simulating measurements of a LiFePO 4 battery under constant discharge rates and Urban Dynamometer Driving Schedule (UDDS), as well as by a detailed comparison with existing battery models. The comparison highlights two features of our model: (a) negligible mass imbalances in the spherical diffusion modelling, which are five orders of magnitude smaller than those from a recent battery model in the literature; (b) efficient modelling of real-world driving cycles with the computational time two orders of magnitude shorter than that of the literature model. These advanced features indicate that our model can be applied in both fundamental electrochemical-thermal studies of lithium-ion battery and detailed simulations of electrified powertrains as an accurate and efficient sub-model.</p

Adversarial Directed Graph Embedding

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs.Comment: 8 pages, 5 figure

KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60, in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.Comment: 10 pages, 7 figures, Appear in AAAI 202

Fusion characterization of biomass ash

The ash fusion characteristics are important parameters for thermochemical utilization of biomass. In this research, a method for measuring the fusion characteristics of biomass ash by Thermo-mechanical Analyzer, TMA, is described. The typical TMA shrinking ratio curve can be divided into two stages, which are closely related to ash melting behaviors. Several characteristics temperatures based on the TMA curves are used to assess the ash fusion characteristics. A new characteristics temperature, T-m, is proposed to represent the severe melting temperature of biomass ash. The fusion characteristics of six types of biomass ash have been measured by TMA. Compared with standard ash fusibility temperatures (AFT) test, TMA is more suitable for measuring the fusion characteristics of biomass ash. The glassy molten areas of the ash samples are sticky and mainly consist of K-Ca-silicates. (C) 2016 Elsevier B.V. All rights reserved.</p