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32 results on '"Yang, Guoguo"'

1. On the Mathematics of RNA Velocity II: Algorithmic Aspects

Author

Li, Tiejun, Wang, Yizhuo, Yang, Guoguo, and Zhou, Peijie

Subjects
Mathematics - Numerical Analysis, Quantitative Biology - Genomics
Abstract

In a previous paper [CSIAM Trans. Appl. Math. 2 (2021), 1-55], the authors proposed a theoretical framework for the analysis of RNA velocity, which is a promising concept in scRNA-seq data analysis to reveal the cell state-transition dynamical processes underlying snapshot data. The current paper is devoted to the algorithmic study of some key components in RNA velocity workflow. Four important points are addressed in this paper: (1) We construct a rational time-scale fixation method which can determine the global gene-shared latent time for cells. (2) We present an uncertainty quantification strategy for the inferred parameters obtained through the EM algorithm. (3) We establish the optimal criterion for the choice of velocity kernel bandwidth with respect to the sample size in the downstream analysis and discuss its implications. (4) We propose a temporal distance estimation approach between two cell clusters along the cellular development path. Some illustrative numerical tests are also carried out to verify our analysis. These results are intended to provide tools and insights in further development of RNA velocity type methods in the future., Comment: 32 pages, 5 figures

Published
2023

2. Formulae for mixed moments of Wiener processes and a stochastic area integral

Author

Komori, Yoshio, Yang, Guoguo, and Burrage, Kevin

Subjects
Mathematics - Probability, Mathematics - Numerical Analysis, 60H10, 60H30, 65C30
Abstract

This paper deals with the expectation of monomials with respect to the stochastic area integral $A_{1,2}(t,t+h)=\int_{t}^{t+h}\int_{t}^{s}{\rm d} W_{1}(r){\rm d} W_{2}(s) -\int_{t}^{t+h}\int_{t}^{s}{\rm d} W_{2}(r){\rm d} W_{1}(s)$ and the increments of two Wiener processes, $\Delta{W}_{i}(t,t+h)=W_{i}(t+h)-W_{i}(t),\ i=1,2$. In a monomial, if the exponent of one of the Wiener increments or the stochastic area integral is an odd number, then the expectation of the monomial is zero. However, if the exponent of any of them is an even number, then the expectation is nonzero and its exact value is not known in general. In the present paper, we derive formulae to give the value in general. As an application of the formulae, we will utilize the formulae for a careful stability analysis on a Magnus-type Milstein method. As another application, we will give some mixed moments of the increments of Wiener processes and stochastic double integrals., Comment: This is a preprint of a paper, which has been accepted for publication in SIAM Journal on Numerical Analysis

Published
2023
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3. A convergence study of SGD-type methods for stochastic optimization

Author

Xiao, Tiannan and Yang, Guoguo

Subjects
Mathematics - Optimization and Control, Mathematics - Probability, 60F05, 60J22, 37N40
Abstract

In this paper, we first reinvestigate the convergence of vanilla SGD method in the sense of $L^2$ under more general learning rates conditions and a more general convex assumption, which relieves the conditions on learning rates and do not need the problem to be strongly convex. Then, by taking advantage of the Lyapunov function technique, we present the convergence of the momentum SGD and Nesterov accelerated SGD methods for the convex and non-convex problem under $L$-smooth assumption that extends the bounded gradient limitation to a certain extent. The convergence of time averaged SGD was also analyzed., Comment: 14 pages

Published
2022

4. Revisiting the central limit theorems for the SGD-type methods

Author

Li, Tiejun, Xiao, Tiannan, and Yang, Guoguo

Subjects
Mathematics - Optimization and Control, Mathematics - Probability, 60F05, 60J22, 37N40
Abstract

We revisited the central limit theorem (CLT) for stochastic gradient descent (SGD) type methods, including the vanilla SGD, momentum SGD and Nesterov accelerated SGD methods with constant or vanishing damping parameters. By taking advantage of Lyapunov function technique and $L^p$ bound estimates, we established the CLT under more general conditions on learning rates for broader classes of SGD methods compared with previous results. The CLT for the time average was also investigated, and we found that it held in the linear case, while it was not generally true in nonlinear situation. Numerical tests were also carried out to verify our theoretical analysis., Comment: 23 pages, 2 figures

Published
2022

5. 辣木籽醒酒肽的酶解制备工艺优化Optimization of enzymatic hydrolysis preparation process of Moringa oleifera seed sobering peptide

Author

彭宇航，孙立，杨果果，孙爱娣，张璐，王雪峰 PENG Yuhang, SUN Li, YANG Guoguo, SUN Aidi, ZHANG Lu, WANG Xuefeng

Subjects
辣木籽；醒酒肽；水解度；adh激活率, moringa oleifera seed, sobering peptide, hydrolysis degree, adh activation rate, Oils, fats, and waxes, TP670-699
Abstract

旨在促进辣木籽醒酒肽的开发利用，以辣木籽蛋白粉为原料，蛋白水解度和辣木籽醒酒肽的乙醇脱氢酶(ADH)激活率为考察指标，采用单因素实验和响应面法优化辣木籽醒酒肽的制备工艺。结果表明，辣木籽醒酒肽制备的最佳酶解工艺条件为料液比1∶ 20、碱性蛋白酶添加量5.0%、酶解pH 9.0、酶解温度63 ℃、酶解时间142 min，在此条件下辣木籽蛋白水解度为12.23%，辣木籽醒酒肽的ADH激活率为22.16%。综上，以辣木籽为原料制备醒酒肽具有良好的发展前景。To promote the development and utilization of Moringa oleifera seed sobering peptide, Moringa oleifera seed protein powder was used as raw material, and the hydrolysis degree of protein and the alcohol dehydrogenase (ADH) activation rate of sobering peptide were used as indicators, single factor experiment and response surface methodology were used to optimize the preparation process of Moringa oleifera seed sobering peptide. The results showed that the optimum enzymatic hydrolysis conditions for the preparation of Moringa oleifera seed sobering peptide were as follows: solid-liquid ratio 1∶ 20, addition amount of alkaline protease 5.0%, enzymatic hydrolysis pH 9.0, enzymatic hydrolysis temperature 63 ℃, and enzymatic hydrolysis time 142 min. Under these conditions, the hydrolysis degree of Moringa oleifera seed protein was 12.23%, and the ADH activation rate of Moringa oleifera seed sobering peptide was 22.16%. In conclusion, the preparation of sobering peptide from Moringa oleifera seed has a good prospect for development.

Published
2024
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13. Comparative analysis of quality characteristics of non fermented cheese processed by two kinds of rennet.

Author

YANG Guoguo, ZHONG Yuwang, LI Tianqiang, HUANG Aixiang, and WANG Xuefeng

Subjects
FATTY acids, MORINGA oleifera, OLEIC acid, AMINO acids, PRINCIPAL components analysis
Abstract

In this experiment, non-fermented cheese was processed by Moringa oleifera seed rennet, and commercial calf rennet was used as control. The sensory, nutritional components, texture characteristics, color difference, free amino acids and free fatty acids were compared and analyzed. The results showed that there was no significant difference in sensory, color difference ΔE value and water content between the cheese processed by two kinds of rennet (P>0.05).The cheese processed by Moringa oleifera seed rennet had higher protein content, lower fat content, soft texture and better elasticity and resilience.The total content of free amino acids and free fatty acids in Moringa oleifera seed chymosin processed cheese was lower than that in calf rennet processed cheese. Taurine was the unique component in Moringa oleifera seed chymosin processed cheese. Principal component analysis (PCA)and partial least squares discriminant analysis (PLS-DA) showed that four amino acids (glutamic acid, lysine, proline, leucine) and five fatty acids (oleic acid, stearic acid, palmitic acid, ginkgo acid, myristic acid) were the characteristic flavor substances to distinguish the two kinds of cheeses. This study provides an important reference for the application of Moringa oleifera seed chymosin in cheese processing. [ABSTRACT FROM AUTHOR]

Published
2024
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14. How to choose an appropriate initial condition to simulate stochastic differential equations stably.

Author

Yang, Guoguo, Burrage, Kevin, Burarge, Pamela, and Ding, Xiaohua

Subjects
*NONLINEAR oscillators, *STOCHASTIC analysis, *LYAPUNOV stability, *STABILITY theory, *STOCHASTIC differential equations
Abstract

A method for choosing an appropriate initial condition to simulate stochastic differential equations stably is proposed. We apply Lyapunov stability and asymptotic stability theories to show how to choose initial conditions without causing blow up of numerical simulations, and carry out specific analysis through the stochastic nonlinear Kubo oscillator. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Abnormal Crystallization Sequence of Calcium Carbonate in the Presence of Synechococcus sp. PCC 7942.

Author

Yang, Guoguo, Li, Fuchun, Deng, Zelan, Wang, Yazhi, Su, Zhimeng, Huang, Lingjie, Yin, Ling, and Ji, Chen

Subjects
*CALCITE, *CALCIUM carbonate, *CARBONATE rocks, *SYNECHOCOCCUS, *CRYSTALLIZATION, *VATERITE, *CARBONIC anhydrase, *BICARBONATE ions
Abstract

Studying the crystallization sequence of calcium carbonate (CaCO3) polymorphs provides great insights into bacterial mineralization mechanisms, and carbonate rock genesis in recent geological history. CaCO3 biomineralization studies were conducted using Synechococcus sp. PCC 7942. Changes in bacterial optical density, polysaccharide content, carbonic anhydrase activity, pH, Ca2+, Mg2+, phosphate, and bicarbonate concentrations in medium, and precipitation levels were recorded, while precipitate morphology and polymorphisms were characterized. We identified an abnormal crystallization sequence order of carbonate polymorphs: calcite, aragonite, and vaterite, which failed to obey Ostwald's rule. We investigated saturation indices (SI) and calculated calcite, aragonite, and vaterite quantities. Also, two mineralization studies were performed using PCC 7942 in the medium-plus different phosphates levels and extracellular polymeric substances (EPS) secreted by PCC 7942. Calcite was precipitated due to phosphate levels >7 µmol·L−1 and SIvaterite values <2.1. SIaragonite values >2.3 and phosphate levels <7 µmol·L−1 exerted crucial roles in aragonite formation. Finally, decreased phosphate and Mg2+ levels promoted vaterite crystallization. We suggest this unique crystallization order may be attributed to changes in bacterial density and PO43− levels. Our study provides new insights into bacterial mineralization mechanisms. Show that calcite, aragonite, and vaterite crystallized in order in MICP. The abnormal crystallization sequence of CaCO3 polymorphs does not follow Ostward's theory. The metabolic activity of Synechococcus sp. PCC 7942 and phosphate levels should be responsible for this unique crystallization order. [ABSTRACT FROM AUTHOR]

Published
2023
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28. Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

Author

Junfeng Gao, Liu Ziyi, Yong He, Huan Zhang, and Yang Guoguo

Subjects
Crops, Agricultural, 0301 basic medicine, China, Insecta, Databases, Factual, Computer science, Pipeline (computing), 02 engineering and technology, computer.software_genre, Convolutional neural network, Article, Pattern Recognition, Automated, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, Animals, Saliency map, Dropout (neural networks), Multidisciplinary, business.industry, Contrast (statistics), Pattern recognition, Function (mathematics), Construct (python library), G400 Computer Science, 030104 developmental biology, Feature (computer vision), Test set, C910 Applied Biological Sciences, 020201 artificial intelligence & image processing, Neural Networks, Computer, Artificial intelligence, Data mining, business, computer, Algorithms
Abstract

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

Published
2016

31. Analytical Inverse Kinematics for Two-Segment Extensible Continuum Manipulators

Author

Xing, Zhiguang, Yang, Guoguo, Wang, Pengyuan, Zheng, Zheng, and Zhao, Jianwen

Abstract

Fast inverse kinematics (IK) algorithm is significant for real-time precise motion control for continuum and soft manipulators. In this paper, we studied an explicit expression of a two-segment continuum manipulator based on constant curvature assumptions and discussed the existence of the IK solutions. Utilizing the pseudo-rigid-body method, we contrast this model with the traditional six-axis rigid industrial robot arm, revealing that two-segment extensible continuum manipulators exhibit limited rotation angles around the directional vector of their tips, thereby showcasing reduced dexterity. By pre-constraining five degrees-of-freedom (DOF) and addressing the definition of the remaining DOF, we streamline the IK-solving process, resulting in minimal computational overhead suitable for a wide range of applications. This model promises a robust and real-time approach for controlling two-segment extensible continuum manipulators, enhancing their operational efficiency and effectiveness.

Published
2025
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32. Localization and classification of paddy field pests using a saliency map and deep convolutional neural network

Author

Liu, Ziyi, Gao, Junfeng, Yang, Guoguo, Zhang, Huan, He, Yong, Liu, Ziyi, Gao, Junfeng, Yang, Guoguo, Zhang, Huan, and He, Yong

Abstract

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

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