From academia to industry, big data has become a buzzword in information technology. The US Federal Government is paying much attention to the big-data revolution. In 2012, fourteen US government departments allocated...From academia to industry, big data has become a buzzword in information technology. The US Federal Government is paying much attention to the big-data revolution. In 2012, fourteen US government departments allocated funds to 87 big-data projects [1].展开更多
We investigate light meson mass spectra with massive u, d, and s quarks and with a spin effect under a bound system in 3 + 1 dimensional QCD by using the first order perturbation correction. In the process of determin...We investigate light meson mass spectra with massive u, d, and s quarks and with a spin effect under a bound system in 3 + 1 dimensional QCD by using the first order perturbation correction. In the process of determining charged kaon and neutral kaonmasses, we obtain masses of u, d, and s quarks that are slightly smaller than the currently accepted values. Using these masses, we obtain light meson mass spectra that includes mass splitting of charged and neutral kaons and <em>ρ</em> mesons. The most interesting of our results is that the pion mass remains unchanged even though u, d, and s quarks become massive.展开更多
One of the most dangerous diseases that affect people worldwide is lung cancer.The survival rate is minimal,because of the complexity in identifying lung cancer at developed stages.Henceforth,earlier detection of lung...One of the most dangerous diseases that affect people worldwide is lung cancer.The survival rate is minimal,because of the complexity in identifying lung cancer at developed stages.Henceforth,earlier detection of lung cancer is significant.Several Machine Learning(ML)approaches have been modeled for lung cancer recognition with the advent of Artificial Intelligence.However,small-scale datasets and deprived generalizability to recognize unknown data are considered challenges in lung cancer detection.This work proposes an advanced deep learning model,named Generative Adversarial Network-Attention Gated Network(GA-AGN),which is the integration of Generative Adversarial Network(GAN)and Attention Gated Network(AGN).Initially,the chest CT scan images are subjected to the pre-processing phase,where image resizing and normalization are used to preprocess the images.Then,the data augmentation is performed using the GAN model that is trained by Elk Herd Optimizer(EHO).Subsequently,lung cancer detection is done by means of GA-AGN model.Ultimately analysis is performed by using three measures,like accuracy,sensitivity as well as specificity with values of 0.938,0.948 and 0.927.The overall analysis states that the proposed model attained better outcomes than the conventional models.展开更多
This article surveys the state-of-the-art crowd simulation techniques and their selected applications, with its focus on our recent research advances in this rapidly growing research field. We first give a categorized...This article surveys the state-of-the-art crowd simulation techniques and their selected applications, with its focus on our recent research advances in this rapidly growing research field. We first give a categorized overview on the mainstream methodologies of crowd simulation. Then, we describe our recent research advances on crowd evacuation,pedestrian crowds, crowd formation, traffic simulation, and swarm simulation. Finally, we offer our viewpoints on open crowd simulation research challenges and point out potential future directions in this field.展开更多
Datacenters have played an increasingly essential role as the underlying infrastructure in cloud computing. As implied by the essence of cloud computing, resources in these datacenters are shared by multiple competing...Datacenters have played an increasingly essential role as the underlying infrastructure in cloud computing. As implied by the essence of cloud computing, resources in these datacenters are shared by multiple competing entities, which can be either tenants that rent virtual machines（VMs） in a public cloud such as Amazon EC2, or applications that embrace data parallel frameworks like MapReduce in a private cloud maintained by Google. It has been generally observed that with traditional transport-layer protocols allocating link bandwidth in datacenters, network traffic from competing applications interferes with each other, resulting in a severe lack of predictability and fairness of application performance. Such a critical issue has drawn a substantial amount of recent research attention on bandwidth allocation in datacenter networks, with a number of new mechanisms proposed to efficiently and fairly share a datacenter network among competing entities. In this article, we present an extensive survey of existing bandwidth allocation mechanisms in the literature, covering the scenarios of both public and private clouds. We thoroughly investigate their underlying design principles, evaluate the trade-off involved in their design choices and summarize them in a unified design space, with the hope of conveying some meaningful insights for better designs in the future.展开更多
文摘From academia to industry, big data has become a buzzword in information technology. The US Federal Government is paying much attention to the big-data revolution. In 2012, fourteen US government departments allocated funds to 87 big-data projects [1].
文摘We investigate light meson mass spectra with massive u, d, and s quarks and with a spin effect under a bound system in 3 + 1 dimensional QCD by using the first order perturbation correction. In the process of determining charged kaon and neutral kaonmasses, we obtain masses of u, d, and s quarks that are slightly smaller than the currently accepted values. Using these masses, we obtain light meson mass spectra that includes mass splitting of charged and neutral kaons and <em>ρ</em> mesons. The most interesting of our results is that the pion mass remains unchanged even though u, d, and s quarks become massive.
文摘One of the most dangerous diseases that affect people worldwide is lung cancer.The survival rate is minimal,because of the complexity in identifying lung cancer at developed stages.Henceforth,earlier detection of lung cancer is significant.Several Machine Learning(ML)approaches have been modeled for lung cancer recognition with the advent of Artificial Intelligence.However,small-scale datasets and deprived generalizability to recognize unknown data are considered challenges in lung cancer detection.This work proposes an advanced deep learning model,named Generative Adversarial Network-Attention Gated Network(GA-AGN),which is the integration of Generative Adversarial Network(GAN)and Attention Gated Network(AGN).Initially,the chest CT scan images are subjected to the pre-processing phase,where image resizing and normalization are used to preprocess the images.Then,the data augmentation is performed using the GAN model that is trained by Elk Herd Optimizer(EHO).Subsequently,lung cancer detection is done by means of GA-AGN model.Ultimately analysis is performed by using three measures,like accuracy,sensitivity as well as specificity with values of 0.938,0.948 and 0.927.The overall analysis states that the proposed model attained better outcomes than the conventional models.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61202207,61100086,61272298,61210005,61472370,61170214,and 61328204the National Key Technology Research and Development Program of Chinaunder Grant Nos.2013BAH23F01,2013BAK03B07,and 2013BAK03B0+2 种基金the Postdoctoral Science Foundation of China under Grant Nos.2012M520067 and 2013T60706the National Nonprofit Industry Specific Program of China under Grant No.2013467058the Research Fund for the Doctoral Program of Higher Education of China under Grant No.20124101120005
文摘This article surveys the state-of-the-art crowd simulation techniques and their selected applications, with its focus on our recent research advances in this rapidly growing research field. We first give a categorized overview on the mainstream methodologies of crowd simulation. Then, we describe our recent research advances on crowd evacuation,pedestrian crowds, crowd formation, traffic simulation, and swarm simulation. Finally, we offer our viewpoints on open crowd simulation research challenges and point out potential future directions in this field.
基金support in part by the Research Grants Council(RGC)of Hong Kong under Grant No.615613the National Natural Science Foundation of China(NSFC)/RGC of Hong Kong under Grant No.N HKUST610/11+1 种基金the NSFC under Grant No.U1301253the China Cache Int.Corp.under Contract No.CCNT12EG01
文摘Datacenters have played an increasingly essential role as the underlying infrastructure in cloud computing. As implied by the essence of cloud computing, resources in these datacenters are shared by multiple competing entities, which can be either tenants that rent virtual machines（VMs） in a public cloud such as Amazon EC2, or applications that embrace data parallel frameworks like MapReduce in a private cloud maintained by Google. It has been generally observed that with traditional transport-layer protocols allocating link bandwidth in datacenters, network traffic from competing applications interferes with each other, resulting in a severe lack of predictability and fairness of application performance. Such a critical issue has drawn a substantial amount of recent research attention on bandwidth allocation in datacenter networks, with a number of new mechanisms proposed to efficiently and fairly share a datacenter network among competing entities. In this article, we present an extensive survey of existing bandwidth allocation mechanisms in the literature, covering the scenarios of both public and private clouds. We thoroughly investigate their underlying design principles, evaluate the trade-off involved in their design choices and summarize them in a unified design space, with the hope of conveying some meaningful insights for better designs in the future.
