Optical multilayer thin film structures have been widely used in numerous photonic applications.However,existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design...Optical multilayer thin film structures have been widely used in numerous photonic applications.However,existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets,or are difficult to suit for different types of structures,e.g.,designing for different materials at each layer.These methods also cannot accommodate versatile design situations under different angles and polarizations.In addition,how to benefit practical fabrications and manufacturing has not been extensively considered yet.In this work,we introduce OptoGPT(Opto Generative Pretrained Transformer),a decoder-only transformer,to solve all these drawbacks and issues simultaneously.展开更多
The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthca...The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.展开更多
Thanks to the strong perpendicular magnetic anisotropy(PMA), excellent processing compatibility as well as novel spintronic phenomenon, Co/Pt multilayers have been attracting massive attention and widely used in magne...Thanks to the strong perpendicular magnetic anisotropy(PMA), excellent processing compatibility as well as novel spintronic phenomenon, Co/Pt multilayers have been attracting massive attention and widely used in magnetic storage.However, reversed magnetic domains come into being with the increasing layer repetition ‘N’ to reduce magneto-static energy, resulting in the remarkable diminishment of the remanent magnetization(Mr). As a result, the product of Mrand thickness(i.e., the remanent moment-thickness product, Mrt), a key parameter in magnetic recording for reliable data storing and reading, also decreases dramatically. To overcome this issue, we deposit an ultra-thick granular [Co/Pt]80multilayer with a total thickness of 68 nm on granular SiNxbuffer layer. The Mrt value, Mrto saturation magnetization(Ms) ratio as well as out of plane(OOP) coercivity(Hcoop) are high up to 2.97 memu/cm^(2), 67%, and 1940 Oe(1 Oe = 79.5775 A·m^(-1)),respectively, which is remarkably improved compared with that of continuous [Co/Pt]80multilayers. That is because large amounts of grain boundaries in the granular multilayers can efficiently impede the propagation and expansion of reversed magnetic domains, which is verified by experimental investigations and micromagnetic simulation results. The simulation results also indicate that the value of Mrt, Mr/Msratio, and Hcoopcan be further improved through optimizing the granule size, which can be experimentally realized by manipulating the process parameter of SiNxbuffer layer. This work provides an alternative solution for achieving high Mrt value in ultra-thick Co/Pt multilayers, which is of unneglectable potential in applications of high-density magnetic recording.展开更多
Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control sy...Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.展开更多
This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential p...This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.展开更多
目的·探究“行为干预研究单位孤独症网络家长培训”[the Research Units in Behavioral Intervention(RUBI)Autism Network Parent Training,RUBI-PT]方案的中国本土化改编并对其适应性进行调查。方法·按照文化改编的4个步骤...目的·探究“行为干预研究单位孤独症网络家长培训”[the Research Units in Behavioral Intervention(RUBI)Autism Network Parent Training,RUBI-PT]方案的中国本土化改编并对其适应性进行调查。方法·按照文化改编的4个步骤对RUBI-PT方案进行改编,包括信息收集、初步改编设计、初步改编测试、进一步调整。信息收集阶段邀请了6位儿科专家和2位心理治疗师进行6次焦点小组访谈,并根据专家意见从语言、治疗形式、治疗设置等方面对RUBI-PT方案进行初步改编;初步改编测试阶段招募了16位孤独症谱系障碍(autism spectrum disorder,ASD)患儿的家长,分2批参加线上RUBI-PT,结束后收集项目反馈问卷并行适应性调查分析,最后根据测试结果进行方案的进一步调整。结果·RUBI-PT的初步改编方案由个体培训调整为团体培训,包含8次核心技能课程,采用线上会议形式实施。初步测试结果显示,家长对于上课进度、上课过程、课后作业完成情况、作业点评情况的满意度分别为90%、80%、100%和100%;课程难度方面,第7次课(功能性沟通训练)和第8次课(教授技能)的难度最大。依据上述调查结果和专家小组意见完成进一步调整,最终形成本土化RUBI-PT的改编方案。结论·经过改编和适应性调查,形成了适用于中国ASD儿童家庭的家长行为训练策略即RUBI-PT。展开更多
基金the National Science Foundation(PFI-008513 and FET-2309403)for the support of this work.
文摘Optical multilayer thin film structures have been widely used in numerous photonic applications.However,existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets,or are difficult to suit for different types of structures,e.g.,designing for different materials at each layer.These methods also cannot accommodate versatile design situations under different angles and polarizations.In addition,how to benefit practical fabrications and manufacturing has not been extensively considered yet.In this work,we introduce OptoGPT(Opto Generative Pretrained Transformer),a decoder-only transformer,to solve all these drawbacks and issues simultaneously.
基金funded by King Saud University through Researchers Supporting Program Number (RSP2024R499).
文摘The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.
基金supported by the National Natural Science Foundation of China (Grant No. 51901008)the National Key Research and Development Program of China (Grant No. 2021YFB3201800)。
文摘Thanks to the strong perpendicular magnetic anisotropy(PMA), excellent processing compatibility as well as novel spintronic phenomenon, Co/Pt multilayers have been attracting massive attention and widely used in magnetic storage.However, reversed magnetic domains come into being with the increasing layer repetition ‘N’ to reduce magneto-static energy, resulting in the remarkable diminishment of the remanent magnetization(Mr). As a result, the product of Mrand thickness(i.e., the remanent moment-thickness product, Mrt), a key parameter in magnetic recording for reliable data storing and reading, also decreases dramatically. To overcome this issue, we deposit an ultra-thick granular [Co/Pt]80multilayer with a total thickness of 68 nm on granular SiNxbuffer layer. The Mrt value, Mrto saturation magnetization(Ms) ratio as well as out of plane(OOP) coercivity(Hcoop) are high up to 2.97 memu/cm^(2), 67%, and 1940 Oe(1 Oe = 79.5775 A·m^(-1)),respectively, which is remarkably improved compared with that of continuous [Co/Pt]80multilayers. That is because large amounts of grain boundaries in the granular multilayers can efficiently impede the propagation and expansion of reversed magnetic domains, which is verified by experimental investigations and micromagnetic simulation results. The simulation results also indicate that the value of Mrt, Mr/Msratio, and Hcoopcan be further improved through optimizing the granule size, which can be experimentally realized by manipulating the process parameter of SiNxbuffer layer. This work provides an alternative solution for achieving high Mrt value in ultra-thick Co/Pt multilayers, which is of unneglectable potential in applications of high-density magnetic recording.
基金partly supported by the University of Malaya Impact Oriented Interdisci-plinary Research Grant under Grant IIRG008(A,B,C)-19IISS.
文摘Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.
文摘This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.
文摘目的·探究“行为干预研究单位孤独症网络家长培训”[the Research Units in Behavioral Intervention(RUBI)Autism Network Parent Training,RUBI-PT]方案的中国本土化改编并对其适应性进行调查。方法·按照文化改编的4个步骤对RUBI-PT方案进行改编,包括信息收集、初步改编设计、初步改编测试、进一步调整。信息收集阶段邀请了6位儿科专家和2位心理治疗师进行6次焦点小组访谈,并根据专家意见从语言、治疗形式、治疗设置等方面对RUBI-PT方案进行初步改编;初步改编测试阶段招募了16位孤独症谱系障碍(autism spectrum disorder,ASD)患儿的家长,分2批参加线上RUBI-PT,结束后收集项目反馈问卷并行适应性调查分析,最后根据测试结果进行方案的进一步调整。结果·RUBI-PT的初步改编方案由个体培训调整为团体培训,包含8次核心技能课程,采用线上会议形式实施。初步测试结果显示,家长对于上课进度、上课过程、课后作业完成情况、作业点评情况的满意度分别为90%、80%、100%和100%;课程难度方面,第7次课(功能性沟通训练)和第8次课(教授技能)的难度最大。依据上述调查结果和专家小组意见完成进一步调整,最终形成本土化RUBI-PT的改编方案。结论·经过改编和适应性调查,形成了适用于中国ASD儿童家庭的家长行为训练策略即RUBI-PT。
