On the estimation of integrated covariance matrices of high dimensional diffusion processes

We consider the estimation of integrated covariance (ICV) matrices of high dimensional diffusion processes based on high frequency observations. We start by studying the most commonly used estimator, the realized covariance (RCV) matrix. We show that in the high dimensional case when the dimension $p$ and the observation frequency $n$ grow in the same rate, the limiting spectral distribution (LSD) of RCV depends on the covolatility process not only through the targeting ICV, but also on how the covolatility process varies in time. We establish a Mar\v{c}enko--Pastur type theorem for weighted sample covariance matrices, based on which we obtain a Mar\v{c}enko--Pastur type theorem for RCV for a class $\mathcal{C}$ of diffusion processes. The results explicitly demonstrate how the time variability of the covolatility process affects the LSD of RCV. We further propose an alternative estimator, the time-variation adjusted realized covariance (TVARCV) matrix. We show that for processes in class $\mathcal {C}$, the TVARCV possesses the desirable property that its LSD depends solely on that of the targeting ICV through the Mar\v{c}enko--Pastur equation, and hence, in particular, the TVARCV can be used to recover the empirical spectral distribution of the ICV by using existing algorithms.Comment: Published in at http://dx.doi.org/10.1214/11-AOS939 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Statistical Properties of Microstructure Noise

We study the estimation of moments and joint moments of microstructure noise. Estimators of arbitrary order of (joint) moments are provided, for which we establish consistency as well as central limit theorems. In particular, we provide estimators of auto-covariances and auto-correlations of the noise. Simulation studies demonstrate excellent performance of our estimators even in the presence of jumps and irregular observation times. Empirical studies reveal (moderate) positive auto-correlation of the noise for the stocks tested

Session 6: \u3cem\u3eModel-based Clustering Analysis on the Spatial-Temporal and Intensity Patterns of Tornadoes

Tornadoes are one of the nature’s most violent windstorms that can occur all over the world except Antarctica. Previous scientific efforts were spent on studying this nature hazard from facets such as: genesis, dynamics, detection, forecasting, warning, measuring, and assessing. While we want to model the tornado datasets by using modern sophisticated statistical and computational techniques. The goal of the paper is developing novel finite mixture models and performing clustering analysis on the spatial-temporal and intensity patterns of the tornadoes. To analyze the tornado dataset, we firstly try a Gaussian distribution with the mean vector and variance-covariance matrix represented as exponential functions of intensity and time. Then, a Gaussian mixture model is employed, with mean vector and variance-covariance represented as exponential functions of intensity and time. Thirdly, manly transform parameters are added to the Gaussian mixture model to take care of the potential skewness in the tornado dataset. Results are obtained by computer algorithms. we provide a summary of insights about tornado forecasting and assessing

Effects of compositional variables on fouling behavior of thin stillage

In the US, ethanol is produced primarily from corn. There are two major commercial processes: corn wet milling (CWM) and dry grind corn (DGC). The DGC industry has grown and made 86% of corn ethanol by the end of 2008. During DGC processing, after distillation, the remaining nonfermentable material known as whole stillage is centrifuged to produce two processing streams; wet cake (30 to 35% solids) and thin stillage (5 to 10% solids). Thin stillage is concentrated to 25 to 30% solids in multi effect evaporators. The presence of fouling in evaporators can increase energy consumption as well as capital and labor costs. Limited studies have been conducted on fouling of corn ethanol processing. An annular fouling probe was used to evaluate compositional variables on fouling behavior of DGC thin stillage. The objectives of this study were to evaluate effects of starch and sucrose solids in fouling of thin stillage evaporators and to assess effects of wet cake in fouling of thin stillage evaporators. Four 100 L batches of thin stillage were collected from a dry grind plant and total solids concentrations were measured. Thin stillage was diluted with tap water so thin stillage plus starch or sucrose was 7% total solids. Fisher’s least significant difference method was used to detect differences among treatments for maximum fouling resistance and fouling rates after 25, 60, 120, 150 and 300 min (P < 0.5). Adding 2% starch to thin stillage increased fouling rates compared with adding 2% sucrose or thin stillage alone. The treatment with additional sucrose showed similar fouling behavior compared to raw thin stillage with 7% total solids. Batches of thin stillage (60 L) were collected to investigate effects of wet cake solids on fouling behavior. Adding 2% wet cake to thin stillage increased the fouling rates compared to thin stillage with 7% total solids. Fouling resistances increased with starch addition, as well as with wet cake addition, at equal total solids contents. Insoluble starch addition had larger effects than soluble sucrose addition. Sucrose alone did not cause increased rapid fouling

An Efficient Synthesis and Photoelectric Properties of Green Carbon Quantum Dots with High Fluorescent Quantum Yield

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)To greatly improve the production quality and efficiency of carbon quantum dots (CQDs), and provide a new approach for the large-scale production of high-quality CQDs, green carbon quantum dots (g-CQDs) with high product yield (PY) and high fluorescent quantum yield (QY) were synthesized by an efficient one-step solvothermal method with 2,7-dihydroxynaphthalene as the carbon source and ethylenediamine as the nitrogen dopant in this study. The PY and QY of g-CQDs were optimised by adjusting reaction parameters such as an amount of added ethylenediamine, reaction temperature, and reaction duration. The results showed that the maximum PY and QY values of g-CQDs were achieved, which were 70.90% and 62.98%, respectively when the amount of added ethylenediamine, reaction temperature, and reaction duration were 4 mL, 180 °C, and 12 h, respectively. With the optimised QY value of g-CQDs, white light emitting diodes (white LEDs) were prepared by combining g-CQDs and blue chip. The colour rendering index of white LEDs reached 87, and the correlated colour temperature was 2520 K, which belongs to the warm white light area and is suitable for indoor lighting. These results indicate that g-CQDs have potential and wide application prospects in the field of white LEDs.Peer reviewedFinal Published versio

Attention-based Pyramid Aggregation Network for Visual Place Recognition

Visual place recognition is challenging in the urban environment and is usually viewed as a large scale image retrieval task. The intrinsic challenges in place recognition exist that the confusing objects such as cars and trees frequently occur in the complex urban scene, and buildings with repetitive structures may cause over-counting and the burstiness problem degrading the image representations. To address these problems, we present an Attention-based Pyramid Aggregation Network (APANet), which is trained in an end-to-end manner for place recognition. One main component of APANet, the spatial pyramid pooling, can effectively encode the multi-size buildings containing geo-information. The other one, the attention block, is adopted as a region evaluator for suppressing the confusing regional features while highlighting the discriminative ones. When testing, we further propose a simple yet effective PCA power whitening strategy, which significantly improves the widely used PCA whitening by reasonably limiting the impact of over-counting. Experimental evaluations demonstrate that the proposed APANet outperforms the state-of-the-art methods on two place recognition benchmarks, and generalizes well on standard image retrieval datasets.Comment: Accepted to ACM Multimedia 201