Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances for solving large optimization problems. This article proposes a new decoder based on Seri...Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances for solving large optimization problems. This article proposes a new decoder based on Serial Genetic Algorithm Decoder (SGAD) for decoding Low Density Parity Check (LDPC) codes. The results show that the proposed algorithm gives large gains over sum-product decoder, which proves its efficiency.展开更多
Brain encoding and decoding via functional magnetic resonance imaging(fMRI)are two important aspects of visual perception neuroscience.Although previous researchers have made significant advances in brain encoding and...Brain encoding and decoding via functional magnetic resonance imaging(fMRI)are two important aspects of visual perception neuroscience.Although previous researchers have made significant advances in brain encoding and decoding models,existing methods still require improvement using advanced machine learning techniques.For example,traditional methods usually build the encoding and decoding models separately,and are prone to overfitting on a small dataset.In fact,effectively unifying the encoding and decoding procedures may allow for more accurate predictions.In this paper,we first review the existing encoding and decoding methods and discuss the potential advantages of a“bidirectional”modeling strategy.Next,we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules.Furthermore,deep generative models(e.g.,variational autoencoders(VAEs)and generative adversarial networks(GANs))have produced promising results in studies on brain encoding and decoding.Finally,we propose that the dual learning method,which was originally designed for machine translation tasks,could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.展开更多
The paper deals with factorial experimental design models decoding.For the ease of calculation of the experimental mathematical models,it is convenient first to code the independent variables.When selecting independen...The paper deals with factorial experimental design models decoding.For the ease of calculation of the experimental mathematical models,it is convenient first to code the independent variables.When selecting independent variables,it is necessary to take into account the range covered by each.A wide range of choices of different variables is presented in this paper.After calculating the regression model,its variables must be returned to their original values for the model to be easy recognized and represented.In the paper,the procedures of simple first order models,with interactions and with second order models,are presented,which could be a very complicated process.Models without and with the mutual influence of independent variables differ.The encoding and decoding procedure on a model with two independent first-order parameters is presented in details.Also,the procedure of model decoding is presented in the experimental surface roughness parameters models’determination,in the face milling machining process,using the first and second order model central compositional experimental design.The simple calculation procedure is recommended in the case study.Also,a large number of examples using mathematical models obtained on the basis of the presented methodology are presented throughout the paper.展开更多
Objective To compare the effects of combined en bloc liver - pancreas transplantation ( LPT) with portal vein drainage and simultaneous combined kidney - pancreas transplantation ( KPT) with systemic venous drainage o...Objective To compare the effects of combined en bloc liver - pancreas transplantation ( LPT) with portal vein drainage and simultaneous combined kidney - pancreas transplantation ( KPT) with systemic venous drainage on the pancreatic endocrine function and related me-展开更多
针对串行抵消列表(Successive Cancellation List,SCL)译码框架下基于搜索集的路径分裂选择策略的缺陷,提出两种改进策略:基于可靠性函数的路径分裂策略和依靠辅助路径度量值(Auxiliary Path Metric,APM)的剪枝策略。在此基础上,提出一...针对串行抵消列表(Successive Cancellation List,SCL)译码框架下基于搜索集的路径分裂选择策略的缺陷,提出两种改进策略:基于可靠性函数的路径分裂策略和依靠辅助路径度量值(Auxiliary Path Metric,APM)的剪枝策略。在此基础上,提出一种新的译码算法——基于可靠性函数的路径分裂选择策略辅助串行抵消列表(Path Splitting Selecting Strategy Based on Reliability Function under the Successive Cancellation List,PSS-RF-SCL)译码算法。该算法在译码阶段,每个信息比特在进行路径分裂前,会计算所有路径的路径度量(Path Metric,PM)值。利用这些PM值,进一步计算该比特的可靠性函数值。算法将可靠性函数值低于其平均值(即阈值α)的信息比特视为需要进行路径分裂的比特,从而减少了多余的路径分裂次数。此外,算法计算每条路径的APM值,并将APM值高于正确译码路径的APM平均值(即阈值β)的路径视为不可靠路径,对不可靠路径进行剪枝,有效控制了译码列表总数。仿真结果表明,相较于传统的基于搜索集的路径分裂策略辅助的SCL译码算法,所提出的PSS-RF-SCL译码算法在保持相同译码性能的前提条件下,显著降低了译码复杂度。展开更多
文摘Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances for solving large optimization problems. This article proposes a new decoder based on Serial Genetic Algorithm Decoder (SGAD) for decoding Low Density Parity Check (LDPC) codes. The results show that the proposed algorithm gives large gains over sum-product decoder, which proves its efficiency.
基金This work was supported by the National Key Research and Development Program of China(2018YFC2001302)National Natural Science Foundation of China(91520202)+2 种基金Chinese Academy of Sciences Scientific Equipment Development Project(YJKYYQ20170050)Beijing Municipal Science and Technology Commission(Z181100008918010)Youth Innovation Promotion Association of Chinese Academy of Sciences,and Strategic Priority Research Program of Chinese Academy of Sciences(XDB32040200).
文摘Brain encoding and decoding via functional magnetic resonance imaging(fMRI)are two important aspects of visual perception neuroscience.Although previous researchers have made significant advances in brain encoding and decoding models,existing methods still require improvement using advanced machine learning techniques.For example,traditional methods usually build the encoding and decoding models separately,and are prone to overfitting on a small dataset.In fact,effectively unifying the encoding and decoding procedures may allow for more accurate predictions.In this paper,we first review the existing encoding and decoding methods and discuss the potential advantages of a“bidirectional”modeling strategy.Next,we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules.Furthermore,deep generative models(e.g.,variational autoencoders(VAEs)and generative adversarial networks(GANs))have produced promising results in studies on brain encoding and decoding.Finally,we propose that the dual learning method,which was originally designed for machine translation tasks,could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.
文摘The paper deals with factorial experimental design models decoding.For the ease of calculation of the experimental mathematical models,it is convenient first to code the independent variables.When selecting independent variables,it is necessary to take into account the range covered by each.A wide range of choices of different variables is presented in this paper.After calculating the regression model,its variables must be returned to their original values for the model to be easy recognized and represented.In the paper,the procedures of simple first order models,with interactions and with second order models,are presented,which could be a very complicated process.Models without and with the mutual influence of independent variables differ.The encoding and decoding procedure on a model with two independent first-order parameters is presented in details.Also,the procedure of model decoding is presented in the experimental surface roughness parameters models’determination,in the face milling machining process,using the first and second order model central compositional experimental design.The simple calculation procedure is recommended in the case study.Also,a large number of examples using mathematical models obtained on the basis of the presented methodology are presented throughout the paper.
文摘Objective To compare the effects of combined en bloc liver - pancreas transplantation ( LPT) with portal vein drainage and simultaneous combined kidney - pancreas transplantation ( KPT) with systemic venous drainage on the pancreatic endocrine function and related me-
文摘针对串行抵消列表(Successive Cancellation List,SCL)译码框架下基于搜索集的路径分裂选择策略的缺陷,提出两种改进策略:基于可靠性函数的路径分裂策略和依靠辅助路径度量值(Auxiliary Path Metric,APM)的剪枝策略。在此基础上,提出一种新的译码算法——基于可靠性函数的路径分裂选择策略辅助串行抵消列表(Path Splitting Selecting Strategy Based on Reliability Function under the Successive Cancellation List,PSS-RF-SCL)译码算法。该算法在译码阶段,每个信息比特在进行路径分裂前,会计算所有路径的路径度量(Path Metric,PM)值。利用这些PM值,进一步计算该比特的可靠性函数值。算法将可靠性函数值低于其平均值(即阈值α)的信息比特视为需要进行路径分裂的比特,从而减少了多余的路径分裂次数。此外,算法计算每条路径的APM值,并将APM值高于正确译码路径的APM平均值(即阈值β)的路径视为不可靠路径,对不可靠路径进行剪枝,有效控制了译码列表总数。仿真结果表明,相较于传统的基于搜索集的路径分裂策略辅助的SCL译码算法,所提出的PSS-RF-SCL译码算法在保持相同译码性能的前提条件下,显著降低了译码复杂度。
