e Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and applications. However, possible noises and con icts are inevitably introduced in the process of constructing. And the KG based tasks or applications assume that the knowledge in the KG is completely correct and inevitably bring about potential deviations. In this paper, we establish a knowledge graph triple trustworthiness measurement model that quantify their semantic correctness and the true degree of the facts expressed. e model is a crisscrossing neural network structure. It synthesizes the internal semantic information in the triples and the global inference information of the KG to achieve the trustworthiness measurement and fusion in the three levels of entity level, relationship level, and KG global level. We analyzed the validity of the model output con dence values, and conducted experiments in the real-world dataset FB15K (from Freebase) for the knowledge graph error detection task. e experimental results showed that compared with other models, our model achieved signi cant and consistent improvements.
Shale gas has become one of the primary energy resources during the past few years, and its impact has been profound in many countries. Hydraulic fracturing treatments are required for the development of shale gas reservoirs, and the consequent hydraulic fractures usually connect with the original small-scale natural fractures forming complex fracture networks in these reservoirs. Therefore, a model for numerical simulation, which is capable of accurately modeling naturally and hydraulically fractured reservoirs, is essential in optimization and management of such reservoirs. In this paper, we develop a comprehensive model that couples embedded discrete fractures, multiple interacting continua, and geomechanics to accurately simulate the fluid flow in shale gas reservoirs with multiscale fractures. Large-scale hydraulic fractures are described by an embedded discrete fracture method, while middle-scale and small-scale natural fractures are modeled by a multiple interacting continua method. Usually, the connection of matrix–natural fractures–hydraulic fractures–wells is considered as the main pathway for the gas flow from a reservoir to a production well. However, geomechanics effects are significant in fractured reservoirs, which may lead to the closure of fractures and a dramatic decrease in gas conductivity in the fractures during the depletion of pressure. When the permeability of fractures is close to that of matrix, the gas production directly from the pathway of matrix–hydraulic fractures–wells as well as the fluid flow in shale matrix cannot be ignored. To accurately predict the fluid flow and well performance, the geomechanics effects and all the possible connections between different regimes must be taken into account. We implement this comprehensive model in our in-house reservoir simulator and study its behavior by numerical experiments. According to the numerical simulation results, accurate and comprehensive production prediction is performed, and reasonable physical phenomena is captured. Sensitivity studies are also performed to show the impacts of different parameters on the prediction of well performance.
Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL). However, existing GANs in SSL have two problems: (1) the generator and the discriminator (i.e. the classifier) may not be optimal at the same time; and (2) the generator cannot control the semantics of the generated samples. The problems essentially arise from the two-player formulation, where a single discriminator shares incompatible roles of identifying fake samples and predicting labels and it only estimates the data without considering the labels. To address the problems, we present triple generative adversarial net (Triple-GAN), which consists of three players-a generator, a discriminator and a classifier. The generator and the classifier characterize the conditional distributions between images and labels, and the discriminator solely focuses on identifying fake image-label pairs. We design compatible utilities to ensure that the distributions characterized by the classifier and the generator both converge to the data distribution. Our results on various datasets demonstrate that Triple-GAN as a unified model can simultaneously (1) achieve the state-of-the-art classification results among deep generative models, and (2) disentangle the classes and styles of the input and transfer smoothly in the data space via interpolation in the latent space class-conditionally.
BackgroundMotor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads.MethodsEleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks.ResultsAll subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution.ConclusionsThis paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities.
Two Mn(II) coordination polymers with azide and the zwitterionic dicarboxylate ligand bis(N-carboxymethyl-4-pyridinium) (bcp) were synthesized, and structurally and magnetically characterized. They are formulated as [Mn(3)(bcp)(2)(N(3))(2)(SO(4))(2)(H(2)O)(4)].6H(2)O (1) and [Mn(4)(bcp)(2)(N(3))(8)(H(2)O)(2)].4H(2)O (2). Compound 1 contains anionic linear [Mn(3)(N(3))(2)(COO)(4)(SO(4))(2)(H(2)O)(4)](4-) units with simultaneous mu(2)-EO (end-on) azide, sulfate and carboxylate bridges, while compound 2 contains [Mn(4)(COO)(4)(N(3))(8)(H(2)O)(2)](4-) clusters with mixed mu(2)-EO azide, mu(3)-EO azide and carboxylate bridges. In these compounds, the anionic tri- or tetranuclear units are linked into coordination chains by the cationic bipyridinium spacers, and are also hydrogen bonded into chains by double O-H...O bridges. Magnetic analyses were carried out on temperature-variable susceptibility data for both compounds, and also on isothermal magnetization data for 1. It is revealed that all the mixed double and triple bridges, [(EO-N(3))(COO)(SO(4))] in 1, [(COO)(EO-N(3))(2)] and [(COO)(EO-N(3))] in 2, transmit antiferromagnetic coupling between Mn(II) ions. The [(EO-N(3))(2)] bridge in , with Mn-N-Mn = 96.6 degrees, also transmits antiferromagnetic coupling, providing the first example in the antiferromagnetic regime predicted theoretically for double EO-azide bridges between Mn(II) ions. The double hydrogen bonding [(O-H...O)(2)] bridges in both compounds induce weak antiferromagnetic interactions.
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