Focused information criterion and model averaging for generalized additive partial linear models

We study model selection and model averaging in generalized additive partial linear models (GAPLMs). Polynomial spline is used to approximate nonparametric functions. The corresponding estimators of the linear parameters are shown to be asymptotically normal. We then develop a focused information criterion (FIC) and a frequentist model average (FMA) estimator on the basis of the quasi-likelihood principle and examine theoretical properties of the FIC and FMA. The major advantages of the proposed procedures over the existing ones are their computational expediency and theoretical reliability. Simulation experiments have provided evidence of the superiority of the proposed procedures. The approach is further applied to a real-world data example.Comment: Published in at http://dx.doi.org/10.1214/10-AOS832 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

The use of dance and movement for the embodied healing of interpersonal trauma in women and girls: A systematic review

Interpersonal trauma is a serious and devastating problem for women and girls from all walks of life. Research has shown that there are physiological consequences for experiencing trauma, and as such, treatment for trauma may need to target the body. Dance/Movement Therapy (DMT) has been emerging in the current literature as one body-oriented treatment approach that is effective in helping women and girls heal from interpersonal trauma. This review examines how practitioners are currently using DMT for this population, what treatment outcomes have been observed, and what the racial/ethnic identities and international contexts are for survivors who have benefited from DMT. Through textual narrative evidence synthesis, this review systematically examined recent literature to find that the characteristics and structure of DMT vary greatly between different practitioners, the participants of DMT are very diverse, and there are many commonly observed outcomes: increased physical ability, increased emotional capacity, mind-body integration, safety, aid with trauma processing, empowerment, social support, and fun. This review also gives recommendations for practitioners who wish to utilize this treatment method: conduct DMT in groups; use the body to create metaphor, imagery, and symbolism; give survivors choices in how they participate in DMT; use music purposefully; and don’t forget to have fun

EAST: An Efficient and Accurate Scene Text Detector

Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3

Static Voltage Stability Analysis for Islanded Microgrids

The ongoing development of renewable energy and microgrid technologies has gradually transformed the conventional energy infrastructure into a modernized system with more distributed generation and localized energy storage options. Compared with power grids utilizing synchronous generation, inverter-based networks cannot physically provide large amounts of inertia. Therefore, more advanced, and extensive studies regarding stability considerations are required for such systems. Appropriate analytical methods are needed for the voltage stability analysis of renewable-dominated power systems, which incorporate many inverters and distributed energy sources. Microgrid voltage stability is being challenged as the power output of renewable energy generation is not as stable as the traditional generation used in the main grid. Therefore, the choice of voltage stability analysis techniques plays an important role in the stable operation of the microgrid. This thesis comprehensively studies static voltage stability analyses of islanded microgrids with high levels of renewable energy penetration. Firstly, a series of generalized evaluation schemes and improvement methods relating to the voltage stability of power systems integrated with various distributed energy resources are discussed. This study presents guidelines for voltage stability analysis and instability mitigation methods for modern renewable-rich power systems. Then, four dominant VSI techniques for microgrids are studied and compared in this paper. An islanded microgrid system is modelled based on the IEEE-14-bus system in PSCAD. The model evaluates the stability results analyzed by different voltage stability indices (VSIs). Four simulation scenarios are applied in this thesis, including changing the output power of distributed generations (DGs) and the connection position of the DGs. The advantages and disadvantages of each technique are discussed based on the simulation results. A ranking of bus voltage stability is obtained based on the simulation and the VSI calculation. Finally, a novel static voltage stability analysis technique is proposed

The Anneal Temperature Effect on the BTO and NZFO Flims and Their Capacitance - Inductance Integrated Device

In this paper, a novel capacitor-inductor integrated structure was proposed. The dielectric material BaTiO3 (BTO) and ferromagnetic material Ni0.5Zn0.5Fe2O4 (NZFO) was prepared by sol-gel method. Phase composition and morphology of the thin films were characterized by XRD, SEM and AFM. The effect of annealing temperature on film crystallinity, surface morphology, dielectric properties and ferromagnetism were investigated. When the annealing temperature was 700 °C, the BTO film and the NZFO film got the better dielectric properties and ferromagnetic properties. Then the BTO thin film was spin-coated on the substrate, and the NZFO thin film was in-situ sintered on the BTO thin film. The composite film possessed both ferromagnetism and dielectric properties. Finally, an inductive coil was fabricated on the BTO/NZFO composite film to produce a capacitance and inductance integrated device

Analysis of the impact on the gravity field determination from the data with the ununiform noise distribution using block-diagonal least squares method

AbstractThe block-diagonal least squares method, which theoretically has specific requirements for the observation data and the spatial distribution of its precision, plays an important role in ultra-high degree gravity field determination. On the basis of block-diagonal least squares method, three data processing strategies are employed to determine the gravity field models using three kinds of simulated global grid data with different noise spatial distribution in this paper. The numerical results show that when we employed the weight matrix corresponding to the noise of the observation data, the model computed by the least squares using the full normal matrix has much higher precision than the one estimated only using the block part of the normal matrix. The model computed by the block-diagonal least squares method without the weight matrix has slightly lower precision than the model computed using the rigorous least squares with the weight matrix. The result offers valuable reference to the using of block-diagonal least squares method in ultra-high gravity model determination

Improving tensor regression by optimal model averaging

Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensionality-reduction and thus plays a key role in tensor regression. However, in CP decomposition, there is uncertainty which rank to use. In this article, we develop a model averaging method to handle this uncertainty by weighting the estimators from candidate tensor regression models with different ranks. When all candidate models are misspecified, we prove that the model averaging estimator is asymptotically optimal. When correct models are included in the candidate models, we prove the consistency of parameters and the convergence of the model averaging weight. Simulations and empirical studies illustrate that the proposed method has superiority over the competition methods and has promising applications

Structural damage detection based on cloud model and Dempster-Shafer evidence theory

Cloud model and D-S theory have been widely used in uncertainty reasoning. Meanwhile, modal strain energy and Inner Product Vector are also utilized as damage-sensitive features to detect structural damage. In this paper, a new structural damage identification approach is proposed based on Dempster-Shafer theory and cloud model. Cloud models were created to make uncertainty reasoning in damage structures by modal strain energy and the Inner Product Vector of acceleration. Then the results of the two methods were combined by using the Dempster-Shafer theory. Due to the classical D-S theory involves counter – intuitive behavious when the high conflicting evidences exists, the distance function was introduced to correct the conflict factor K and combine the evidences. Moreover, a model of simple beam was created to verify the feasibility and accuracy for the single-damage and the multiple-damage. The effects of noise on damage detection were investigated simultaneously. The results show that the method has strong anti-noise ability and high accuracy