64 research outputs found

    Terahertz imaging with sub-wavelength resolution by femtosecond laser filament in air

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    Terahertz (THz) imaging provides cutting edge technique in biology, medical sciences and non-destructive evaluation. However, due to the long wavelength of the THz wave, the obtained resolution of THz imaging is normally a few hundred microns and is much lower than that of the traditional optical imaging. We introduce a sub-wavelength resolution THz imaging technique which uses the THz radiation generated by a femtosecond laser filament in air as the probe. This method is based on the fact that the femtosecond laser filament forms a waveguide for the THz wave in air. The diameter of the THz beam, which propagates inside the filament, varies from 20 {\mu}m to 50 {\mu}m, which is significantly smaller than the wavelength of the THz wave. Using this highly spatially confined THz beam as the probe, THz imaging with resolution as high as 20 {\mu}m (~{\lambda}/38) can be realized.Comment: 10 pages, 7 figure

    Empathetic Response Generation with State Management

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    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

    MCDAN: a Multi-scale Context-enhanced Dynamic Attention Network for Diffusion Prediction

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    Information diffusion prediction aims at predicting the target users in the information diffusion path on social networks. Prior works mainly focus on the observed structure or sequence of cascades, trying to predict to whom this cascade will be infected passively. In this study, we argue that user intent understanding is also a key part of information diffusion prediction. We thereby propose a novel Multi-scale Context-enhanced Dynamic Attention Network (MCDAN) to predict which user will most likely join the observed current cascades. Specifically, to consider the global interactive relationship among users, we take full advantage of user friendships and global cascading relationships, which are extracted from the social network and historical cascades, respectively. To refine the model's ability to understand the user's preference for the current cascade, we propose a multi-scale sequential hypergraph attention module to capture the dynamic preference of users at different time scales. Moreover, we design a contextual attention enhancement module to strengthen the interaction of user representations within the current cascade. Finally, to engage the user's own susceptibility, we construct a susceptibility label for each user based on user susceptibility analysis and use the rank of this label for auxiliary prediction. We conduct experiments over four widely used datasets and show that MCDAN significantly overperforms the state-of-the-art models. The average improvements are up to 10.61% in terms of Hits@100 and 9.71% in terms of MAP@100, respectively

    An n-of-1 Trial Service in Clinical Practice: Testing the Effectiveness of Liuwei Dihuang Decoction for Kidney-Yin Deficiency Syndrome

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    Objective. To describe the clinical use of n-of-1 RCTs for kidney-Yin deficiency syndrome that is a traditional Chinese medicine syndrome in publicly clinical practice in China. Methods. Our study included patients with kidney-Yin deficiency syndrome, using a within-patient, randomized, double-blind, crossover comparison of Liuwei Dihuang decoction versus placebo. Outcome Measures. Primary outcome measures included number of individual completion rates, response rate, and post-n-of-1 RCTs decisions. Secondary measures were the whole group score of individual Likert scale, SF-36 questionnaire. Results. Fifty patients were recruited and 3 were not completed. Forty-seven patients completed 3 pairs of periods, 3 (6.38%) were responders, 28 (59.57%) were nonresponders, and 16 (34.05%) were possible responders. Doctors and patients used the trial results to making decision. Three responders stayed on the medication management, 28 nonresponders ceased the LDD, 7 patients of the 16 possible responders could not give clear decision, and the others kept the same medication station. Among the whole group, neither the individual Likert score nor the SF-36 showed any statistical differences between LDD and placebo. Discussion. More attention should be paid to choose experienced TCM doctor as investigator and keep the simulant same with test medication in n-of-1 RCTs of TCM and sufficiently biological half-life period of Chinese medicine compound

    Neutral top-pion and lepton flavor violating processes

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    In the context of topcolor-assisted techicolor(TC2) models, we study the contributions of the neutral top-pion πt0\pi^{0}_{t} to the lepton flavor violating(LFV) processes liljγl_{i}\to l_{j}\gamma and liljlklll_{i}\to l_{j}l_{k}l_{l}. We find that the present experimental bound on μeγ\mu\to e\gamma gives severe constraints on the free parameters of TC2TC2 models. Taking into account these constraints, we consider the processes liljlklll_{i}\to l_{j}l_{k}l_{l} generated by top-pion exchange at the tree-level and the one loop level, and obtain Br(μ3e)2.87×1014Br(\mu\to 3e)\simeq 2.87\times 10^{-14}, 1.1×1015Br(τ3e)Br(τ2eμ)4.4×10151.1\times 10^{-15}\leq Br(\tau\to 3e)\simeq Br(\tau\to 2e\mu)\leq 4.4 \times 10^{-15} , 3.1×1015Br(τ2μe)Br(τ3μ)1.5×10143.1\times 10^{-15} \leq Br(\tau\to 2\mu e)\simeq Br(\tau\to 3\mu)\leq 1.5 \times 10^{-14} in most of the parameter space.Comment: latex files,16 pages, 6 figures. Submitted to Phys. Rev.

    Study on Foundation Pit Construction Cost Prediction Based on the Stacked Denoising Autoencoder

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    To accurately predict the construction costs of foundation pit projects, a model based on the stacked denoising autoencoder (SDAE) is constructed in this work. The influencing factors of foundation pit project construction costs are identified from the four attributes of construction cost management, namely, engineering, the environment, the market, and management. Combined with Chinese national standards and the practice of foundation pit project management, a method of the quantization of the influencing factors is presented. 60 deep foundation pit projects in China are selected to obtain 13 main characteristic factors affecting these project construction cost by using the rough set. Then, considering the advantages of the SDAE in dealing with complex nonlinear problems, a prediction model of foundation pit project construction costs is created. Finally, this paper employs these 60 projects for a case analysis. The case study demonstrates that, compared with the actual construction costs, the calculation error of the proposed method is less than 3%, and the average error is only 1.54%. In addition, three error analysis tools commonly used in machine learning (the determination coefficient, root mean square error, and mean absolute error) emphasize that the calculation accuracy of the proposed method is notably higher than those of other methods (Chinese national code, the multivariate return method, the BP algorithm, the BP model optimized by the genetic algorithm, the support vector machine, and the RBF model). The relevant research results of this paper provide a useful reference for the prediction of the construction costs of foundation pit projects

    The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization

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    The prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro shield construction costs. The model accounts for the complexity of metro shield construction and the nonlinear relationship between the construction cost factors. First, the factors affecting the construction cost were determined by referring to the Chinese National Standards and analysing the engineering practice of typical metro shield projects. The rough set theory was used to simplify the system of influencing factors to extract the dominant influencing factors and reduce the number of input variables in the BP neural network. Since the BP neural network easily falls into a local minimum and has a slow convergence speed, QPSO was used to optimize the weights and thresholds of the BP neural network. This method combined the strong nonlinear analysis capabilities of the BP and the global search capabilities of the QPSO. Finally, we selected 50 projects in China for a case analysis. The results showed the dominant factors affecting the construction cost of these projects included ten indicators, such as the type of tunnelling machine and the geological characteristics. The determination coefficient, mean absolute percentage error, root mean square error, and mean absolute error, which are frequently used error analysis tools, were used to analyse the calculation errors of different models (the proposed model, a multiple regression method, a traditional BP model, a BP model optimized by the genetic algorithm, and the BP model optimized by the particle swarm optimization). The results showed that the proposed method had the highest prediction accuracy and stability, demonstrating the effectiveness and excellent performance of this proposed method
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