Testicular descent occurs in two consecutive stages:the transabdominal stage and the inguinoscrotal stage.Androgens play a crucial role in the second stage by influencing the development of the gubernaculum,a structur...Testicular descent occurs in two consecutive stages:the transabdominal stage and the inguinoscrotal stage.Androgens play a crucial role in the second stage by influencing the development of the gubernaculum,a structure that pulls the testis into the scrotum.However,the mechanisms of androgen actions underlying many of the processes associated with gubernaculum development have not been fully elucidated.To identify the androgen-regulated genes,we conducted large-scale gene expression analyses on the gubernaculum harvested from luteinizing hormone/choriogonadotropin receptor knockout(Lhcgr KO)mice,an animal model of inguinoscrotal testis maldescent resulting from androgen deficiency.We found that the expression of secreted protein acidic and rich in cysteine(SPARC)-related modular calcium binding 1(Smoc1)was the most severely suppressed at both the transcript and protein levels,while its expression was the most dramatically induced by testosterone administration in the gubernacula of Lhcgr KO mice.The upregulation of Smoc1 expression by testosterone was curtailed by the addition of an androgen receptor antagonist,flutamide.In addition,in vitro studies demonstrated that SMOC1 modestly but significantly promoted the proliferation of gubernacular cells.In the cultures of myogenic differentiation medium,both testosterone and SMOC1 enhanced the expression of myogenic regulatory factors such as paired box 7(Pax7)and myogenic factor 5(Myf5).After short-interfering RNA-mediated knocking down of Smoc1,the expression of Pax7 and Myf5 diminished,and testosterone alone did not recover,but additional SMOC1 did.These observations indicate that SMOC1 is pivotal in mediating androgen action to regulate gubernaculum development during inguinoscrotal testicular descent.展开更多
Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk o...Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.展开更多
The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller ob...The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller objective function values than the K-means algorithm.However,CDKM is sensitive to initialization,which makes the K-means objective function values not small enough.Since selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of CDKM.The proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value further.The split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains unchanged.The algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition matrix.Experiments on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional datasets.The proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.展开更多
Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for ga...Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.展开更多
We prove,under mild conditions,the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model,both in batch gradient descent and stochastic gradient descent.We also discuss a ...We prove,under mild conditions,the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model,both in batch gradient descent and stochastic gradient descent.We also discuss a Riemannian version of the Adam algorithm.We show numerical simulations of these algorithms on various benchmarks.展开更多
With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rej...With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.展开更多
The current work aims at employing a gradient descent algorithm for optimizing the thrust of a flapping wing. An in-house solver has been employed, along with mesh movement methodologies to capture the dynamics of flo...The current work aims at employing a gradient descent algorithm for optimizing the thrust of a flapping wing. An in-house solver has been employed, along with mesh movement methodologies to capture the dynamics of flow around the airfoil. An efficient framework for implementing the coupled solver and optimization in a multicore environment has been implemented for the generation of optimized solutionsmaximizing thrust performance & computational speed.展开更多
为了保障网络环境的安全性,提出了基于集成式图卷积神经网络算法的网络入侵检测技术。研究方法采用随机梯度下降算法和均方根传播(Root Mean Square Propagation,RMSProp)优化器提升了检测模型的训练效率,强化了检测模型的分类效果。研...为了保障网络环境的安全性,提出了基于集成式图卷积神经网络算法的网络入侵检测技术。研究方法采用随机梯度下降算法和均方根传播(Root Mean Square Propagation,RMSProp)优化器提升了检测模型的训练效率,强化了检测模型的分类效果。研究结果显示,研究模型的入侵检测准确率为96.41%~97.18%。可见经过研究模型优化后,入侵检测技术在模型训练效率和模型训练精度上都有明显提升。研究模型可以根据访问来源进行数据分类,提升了入侵检测模型对访问行为的分类效果。同时,分类效果的提升优化了计算机对攻击行为的识别效率,使计算机的防御效果增强,有效保障了用户的网络安全环境。因此,研究为网络入侵行为的检测提供了一个识别效果较好的技术方法。展开更多
基金supported in part by the Department of OB/GYN research funds(University of Louisville,Louisville,KY,USA)Jilin Province Health Technology Capability Enhancement funds(No.2022JC055).
文摘Testicular descent occurs in two consecutive stages:the transabdominal stage and the inguinoscrotal stage.Androgens play a crucial role in the second stage by influencing the development of the gubernaculum,a structure that pulls the testis into the scrotum.However,the mechanisms of androgen actions underlying many of the processes associated with gubernaculum development have not been fully elucidated.To identify the androgen-regulated genes,we conducted large-scale gene expression analyses on the gubernaculum harvested from luteinizing hormone/choriogonadotropin receptor knockout(Lhcgr KO)mice,an animal model of inguinoscrotal testis maldescent resulting from androgen deficiency.We found that the expression of secreted protein acidic and rich in cysteine(SPARC)-related modular calcium binding 1(Smoc1)was the most severely suppressed at both the transcript and protein levels,while its expression was the most dramatically induced by testosterone administration in the gubernacula of Lhcgr KO mice.The upregulation of Smoc1 expression by testosterone was curtailed by the addition of an androgen receptor antagonist,flutamide.In addition,in vitro studies demonstrated that SMOC1 modestly but significantly promoted the proliferation of gubernacular cells.In the cultures of myogenic differentiation medium,both testosterone and SMOC1 enhanced the expression of myogenic regulatory factors such as paired box 7(Pax7)and myogenic factor 5(Myf5).After short-interfering RNA-mediated knocking down of Smoc1,the expression of Pax7 and Myf5 diminished,and testosterone alone did not recover,but additional SMOC1 did.These observations indicate that SMOC1 is pivotal in mediating androgen action to regulate gubernaculum development during inguinoscrotal testicular descent.
文摘Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
基金funded by National Defense Basic Research Program,grant number JCKY2019411B001funded by National Key Research and Development Program,grant number 2022YFC3601305funded by Key R&D Projects of Jilin Provincial Science and Technology Department,grant number 20210203218SF.
文摘The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of K-means.It identifies better locally optimal solutions than the original K-means algorithm.That is,it achieves solutions that yield smaller objective function values than the K-means algorithm.However,CDKM is sensitive to initialization,which makes the K-means objective function values not small enough.Since selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of CDKM.The proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value further.The split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains unchanged.The algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition matrix.Experiments on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional datasets.The proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.
基金the Natural Science Foundation of Ningxia Province(No.2021AAC03230).
文摘Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.
基金partially supported by NSF Grants DMS-1854434,DMS-1952644,and DMS-2151235 at UC Irvinesupported by NSF Grants DMS-1924935,DMS-1952339,DMS-2110145,DMS-2152762,and DMS-2208361,and DOE Grants DE-SC0021142 and DE-SC0002722.
文摘We prove,under mild conditions,the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model,both in batch gradient descent and stochastic gradient descent.We also discuss a Riemannian version of the Adam algorithm.We show numerical simulations of these algorithms on various benchmarks.
基金the 2021 Key Project of Natural Science and Technology of Yangzhou Polytechnic Institute,Active Disturbance Rejection and Fault-Tolerant Control of Multi-Rotor Plant ProtectionUAV Based on QBall-X4(Grant Number 2021xjzk002).
文摘With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.
文摘The current work aims at employing a gradient descent algorithm for optimizing the thrust of a flapping wing. An in-house solver has been employed, along with mesh movement methodologies to capture the dynamics of flow around the airfoil. An efficient framework for implementing the coupled solver and optimization in a multicore environment has been implemented for the generation of optimized solutionsmaximizing thrust performance & computational speed.
文摘为了保障网络环境的安全性,提出了基于集成式图卷积神经网络算法的网络入侵检测技术。研究方法采用随机梯度下降算法和均方根传播(Root Mean Square Propagation,RMSProp)优化器提升了检测模型的训练效率,强化了检测模型的分类效果。研究结果显示,研究模型的入侵检测准确率为96.41%~97.18%。可见经过研究模型优化后,入侵检测技术在模型训练效率和模型训练精度上都有明显提升。研究模型可以根据访问来源进行数据分类,提升了入侵检测模型对访问行为的分类效果。同时,分类效果的提升优化了计算机对攻击行为的识别效率,使计算机的防御效果增强,有效保障了用户的网络安全环境。因此,研究为网络入侵行为的检测提供了一个识别效果较好的技术方法。
