Moszkowski’s “Caprice Espagnol” Piano Playing Techniques Exploration

Moritz Moszkowski is a famous European renowned Polish composer, conductor, pianist and educator. His creation involves many aspects, among which the most numerous and the most well-known is the piano works, in these piano works is good for using Spanish folk music elements to create. In this paper through the Moszkowski piano “Spanish style” creative background, creative characteristics and playing techniques, make us to the writing background, creative characteristics and piano playing techniques have a certain grasp and more profound understanding, and the charm of the work to form a deeper understanding, more thorough experience, so as to form a better effect in the process of interpretation

Bayesian Predictive Inference with Survey Weights for Binary Response: A Simulation Study and a Numerical Example

We consider the problem of Bayesian predictive inference for binary response with covariates and survey weights. Our method makes use of the combination of probability survey samples that have been enhanced by auxiliary data. The incorporation of survey weights into a logistic regression model, which creates a thorough and logical analytical paradigm, is at the core of our methodology. Our investigation covers six different models that were carefully created to include both normalized and unnormalized weighted likelihoods. Three iterations of adjusted survey weights—original, trimmed, and calibrated—are taken into account within this spectrum. The Metropolis-Hastings sampler is the implementation algorithm for our analysis. Building on this foundation, we use the stratification and surrogate sampling technique to expand our inferences to finite population parameters. We conduct a thorough evaluation that includes a simulation study and a real-world dataset focused on body mass index (BMI) in order to assess the performance and efficacy of our models. Our findings show how powerful models with normalized density functions and adjusted trimmed weights are. These models exhibit a unique capability for higher estimation accuracy while maintaining fidelity to the fundamental principles of Bayesian inference. The results of our study have broad implications for the field of research as a whole, highlighting the significance of the framework we proposed and the exceptional value of trimmed weights that have been adjusted for the purpose of driving effective predictive inference in survey-oriented research studies

Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks

The applications of traditional statistical feature selection methods to high-dimension, low sample-size data often struggle and encounter challenging problems, such as overfitting, curse of dimensionality, computational infeasibility, and strong model assumption. In this paper, we propose a novel two-step nonparametric approach called Deep Feature Screening (DeepFS) that can overcome these problems and identify significant features with high precision for ultra high-dimensional, low-sample-size data. This approach first extracts a low-dimensional representation of input data and then applies feature screening based on multivariate rank distance correlation recently developed by Deb and Sen (2021). This approach combines the strengths of both deep neural networks and feature screening, and thereby has the following appealing features in addition to its ability of handling ultra high-dimensional data with small number of samples: (1) it is model free and distribution free; (2) it can be used for both supervised and unsupervised feature selection; and (3) it is capable of recovering the original input data. The superiority of DeepFS is demonstrated via extensive simulation studies and real data analyses

Anti‐windup controller design for singularly perturbed systems subject to actuator saturation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166157/1/cth2bf00153.pd

An Improved StatCom Model for Power Flow Analysis

The StatCom is traditionally modeled for power flow analysis as a PV or PQ bus depending on its primary application. The active power is either set to zero (neglecting the StatCom losses) or calculated iteratively. The StatCom voltage and reactive power compensation are usually related through the magnetics of the StatCom. This traditional power flow model of the StatCom neglects the impact of the high-frequency effects and the switching characteristics of the power electronics on the active power losses and the reactive power injection (absorption). In this paper, the authors propose a new StatCom model appropriate for power flow analysis derived directly from the dynamic model of the StatCom. The proposed model can therefore account for the high-frequency effects and power electronic losses, and more accurately predict the active and reactive power outputs of the StatCom

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications

Ore Extension of Group-cograded Hopf Coquasigroups

The aim of this paper is the Ore extension of group-cograded Hopf coquasigroups. This paper first shows a categorical interpretation and some examples of group-cograded Hopf coquasigroups, and then gives a necessary and sufficient conditions for the Ore extensions of group-cograded Hopf coquasigroups to be group-cograded Hopf coquasigroups. Finally, a certain isomorphism between Ore extensions are considered.Comment: 15page

Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes

We present a multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRDFs. Unlike previous representations, where the global illumination of large scenes is simplified as multiple environment maps, we propose a compact representation called Texture-based Lighting (TBL). It consists of 3D meshs and HDR textures, and efficiently models direct and infinite-bounce indirect lighting of the entire large scene. Based on TBL, we further propose a hybrid lighting representation with precomputed irradiance, which significantly improves the efficiency and alleviate the rendering noise in the material optimization. To physically disentangle the ambiguity between materials, we propose a three-stage material optimization strategy based on the priors of semantic segmentation and room segmentation. Extensive experiments show that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively, and enables physically-reasonable mixed-reality applications such as material editing, editable novel view synthesis and relighting. The project page is at https://lzleejean.github.io/TexIR.Comment: The project page is at: https://lzleejean.github.io/TexI