Projected principal component analysis in factor models

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which employs principal component analysis to the projected (smoothed) data matrix onto a given linear space spanned by covariates. When it applies to high-dimensional factor analysis, the projection removes noise components. We show that the unobserved latent factors can be more accurately estimated than the conventional PCA if the projection is genuine, or more precisely, when the factor loading matrices are related to the projected linear space. When the dimensionality is large, the factors can be estimated accurately even when the sample size is finite. We propose a flexible semiparametric factor model, which decomposes the factor loading matrix into the component that can be explained by subject-specific covariates and the orthogonal residual component. The covariates' effects on the factor loadings are further modeled by the additive model via sieve approximations. By using the newly proposed Projected-PCA, the rates of convergence of the smooth factor loading matrices are obtained, which are much faster than those of the conventional factor analysis. The convergence is achieved even when the sample size is finite and is particularly appealing in the high-dimension-low-sample-size situation. This leads us to developing nonparametric tests on whether observed covariates have explaining powers on the loadings and whether they fully explain the loadings. The proposed method is illustrated by both simulated data and the returns of the components of the S&P 500 index.Comment: Published at http://dx.doi.org/10.1214/15-AOS1364 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

MA-NeRF: Motion-Assisted Neural Radiance Fields for Face Synthesis from Sparse Images

We address the problem of photorealistic 3D face avatar synthesis from sparse images. Existing Parametric models for face avatar reconstruction struggle to generate details that originate from inputs. Meanwhile, although current NeRF-based avatar methods provide promising results for novel view synthesis, they fail to generalize well for unseen expressions. We improve from NeRF and propose a novel framework that, by leveraging the parametric 3DMM models, can reconstruct a high-fidelity drivable face avatar and successfully handle the unseen expressions. At the core of our implementation are structured displacement feature and semantic-aware learning module. Our structured displacement feature will introduce the motion prior as an additional constraints and help perform better for unseen expressions, by constructing displacement volume. Besides, the semantic-aware learning incorporates multi-level prior, e.g., semantic embedding, learnable latent code, to lift the performance to a higher level. Thorough experiments have been doen both quantitatively and qualitatively to demonstrate the design of our framework, and our method achieves much better results than the current state-of-the-arts

Towards Arbitrary Text-driven Image Manipulation via Space Alignment

The recent GAN inversion methods have been able to successfully invert the real image input to the corresponding editable latent code in StyleGAN. By combining with the language-vision model (CLIP), some text-driven image manipulation methods are proposed. However, these methods require extra costs to perform optimization for a certain image or a new attribute editing mode. To achieve a more efficient editing method, we propose a new Text-driven image Manipulation framework via Space Alignment (TMSA). The Space Alignment module aims to align the same semantic regions in CLIP and StyleGAN spaces. Then, the text input can be directly accessed into the StyleGAN space and be used to find the semantic shift according to the text description. The framework can support arbitrary image editing mode without additional cost. Our work provides the user with an interface to control the attributes of a given image according to text input and get the result in real time. Ex tensive experiments demonstrate our superior performance over prior works.Comment: 8 pages, 12 figure

knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable

Abstract Background Testing the dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting nonlinear dependence between one continuous variable X and one binary dependent variables Y (0 or 1). Results We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/ ). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). Conclusions We concluded that knnAUC is an efficient R package to test non-linear dependence between one continuous variable and one binary dependent variable especially in computational biology area.https://deepblue.lib.umich.edu/bitstream/2027.42/146514/1/12859_2018_Article_2427.pd

Predictive model for inflammation grades of chronic hepatitis B: Large‐scale analysis of clinical parameters and gene expressions

BackgroundLiver biopsy is the gold standard to assess pathological features (eg inflammation grades) for hepatitis B virus‐infected patients although it is invasive and traumatic; meanwhile, several gene profiles of chronic hepatitis B (CHB) have been separately described in relatively small hepatitis B virus (HBV)‐infected samples. We aimed to analyse correlations among inflammation grades, gene expressions and clinical parameters (serum alanine amino transaminase, aspartate amino transaminase and HBV‐DNA) in large‐scale CHB samples and to predict inflammation grades by using clinical parameters and/or gene expressions.MethodsWe analysed gene expressions with three clinical parameters in 122 CHB samples by an improved regression model. Principal component analysis and machine‐learning methods including Random Forest, K‐nearest neighbour and support vector machine were used for analysis and further diagnosis models. Six normal samples were conducted to validate the predictive model.ResultsSignificant genes related to clinical parameters were found enriching in the immune system, interferon‐stimulated, regulation of cytokine production, anti‐apoptosis, and etc. A panel of these genes with clinical parameters can effectively predict binary classifications of inflammation grade (area under the ROC curve [AUC]: 0.88, 95% confidence interval [CI]: 0.77‐0.93), validated by normal samples. A panel with only clinical parameters was also valuable (AUC: 0.78, 95% CI: 0.65‐0.86), indicating that liquid biopsy method for detecting the pathology of CHB is possible.ConclusionsThis is the first study to systematically elucidate the relationships among gene expressions, clinical parameters and pathological inflammation grades in CHB, and to build models predicting inflammation grades by gene expressions and/or clinical parameters as well.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139116/1/liv13427.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139116/2/liv13427_am.pd

INFLUENCING FACTOR ANALYSIS OF FATIGUE CRACK INITIATION LIFE ON CRANE LATTICE BOOM

The crane lattice boom is complex and the load is diversity,part of the structure may suffer plastic deformation because of the high stress concentration or poor weld quality under exceptional poor load conditions. So using strain analysis parameter is reasonable compared to stress parameter. The paper was focused on the fatigue crack initiation stage of crane lattice boom. The method of local stress strain was used combined with the correction Neuber method. A crane fatigue crack initiation life evaluation algorithm model was built. According to the model,three main factors which will influence the initiation life calculation were analyzed and compared. Fatigue tests were also carried out with K nodes of crane lattice boom. When the material was determined under certain circumstances,the stress concentration factor had the most impact result on the life. The mean stress on the material had the less impact but it cannot be ignored. According to the Factors,optimization suggestions to lattice boom spatial structure and work process improvement measures were proposed

Lotus-Inspired Multiscale Superhydrophobic AA5083 Resisting Surface Contamination and Marine Corrosion Attack

The massive and long-term service of 5083 aluminum alloy (AA5083) is restricted by several shortcomings in marine and industrial environments, such as proneness to localized corrosion attack, surface contamination, etc. Herein, we report a facile and cost-effective strategy to transform intrinsic hydrophilicity into water-repellent superhydrophobicity, combining fluorine-free chemisorption of a hydrophobic agent with etching texture. Dual-scale hierarchical structure, surface height relief and surface chemical elements were studied by field emission scanning electron microscopy (FE-SEM), atomic force microscopy (AFM), energy dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS), successively. Detailed investigations of the wetting property, self-cleaning effect, NaCl-particle self-propelling, corrosion and long-term behavior of the consequent superhydrophobic AA5083 surface were carried out, demonstrating extremely low adhesivity and outstanding water-repellent, self-cleaning and corrosion-resisting performance with long-term stability. We believe that the low cost, scalable and fluorine-free transforming of metallic surface wettability into waterproof superhydrophobicity is a possible strategy towards anti-contamination and marine anti-corrosion