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
One of the most significant challenges in the neuroscience community is to understand how the human brain works.Recent progress in neuroimaging techniques have validated that it is possible to decode a person′s thoug...One of the most significant challenges in the neuroscience community is to understand how the human brain works.Recent progress in neuroimaging techniques have validated that it is possible to decode a person′s thoughts,memories,and emotions via functional magnetic resonance imaging(i.e.,fMRI)since it can measure the neural activation of human brains with satisfied spatiotemporal resolutions.However,the unprecedented scale and complexity of the fMRI data have presented critical computational bottlenecks requiring new scientific analytic tools.Given the increasingly important role of machine learning in neuroscience,a great many machine learning algorithms are presented to analyze brain activities from the fMRI data.In this paper,we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects,i.e.,brain image functional alignment,brain activity pattern analysis,and visual stimuli reconstruction.In addition,online resources and open research problems on brain pattern analysis are also provided for the convenience of future research.展开更多
In this study, a novel approach by combining base-catalyzed epoxide ring-opening and thiol-ene click chemistry is presented for the side-chain modification of dextran. The vinyl-modified dextran is prepared by a basic...In this study, a novel approach by combining base-catalyzed epoxide ring-opening and thiol-ene click chemistry is presented for the side-chain modification of dextran. The vinyl-modified dextran is prepared by a basic epoxide ring opening reaction of allyl glycidyl ether in 0.1 mol/L NaOH, followed by thiol-addition click reaction of three model sulfhydryl compounds using water-soluble Irgacure 2959 as the photoinitiator, leading to side-chain functionalized dextran modified with carboxyl, bidentate dicarboxyl or amino groups. This is the first example of combining epoxide ring-opening and thiol- ene click chemistry for side-chain modification of dextran in aqueous media. Importantly, it may also be extended as a convenient and efficient method for the side-chain modification of other polysaccharides.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-
基金This work was supported by National Natural Science Foundation of China(Nos.61876082,61861130366,6173-2006 and 61902183)National Key Research and Development Program of China(Nos.2018 YFC2001600,2018YFC 2001602)+1 种基金the Royal Society-Academy of Medical Sciences Newton Advanced Fellowship(No.NAF\R1\180371)China Postdoctoral Science Foundation funded project(No.2019M661831).
文摘One of the most significant challenges in the neuroscience community is to understand how the human brain works.Recent progress in neuroimaging techniques have validated that it is possible to decode a person′s thoughts,memories,and emotions via functional magnetic resonance imaging(i.e.,fMRI)since it can measure the neural activation of human brains with satisfied spatiotemporal resolutions.However,the unprecedented scale and complexity of the fMRI data have presented critical computational bottlenecks requiring new scientific analytic tools.Given the increasingly important role of machine learning in neuroscience,a great many machine learning algorithms are presented to analyze brain activities from the fMRI data.In this paper,we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects,i.e.,brain image functional alignment,brain activity pattern analysis,and visual stimuli reconstruction.In addition,online resources and open research problems on brain pattern analysis are also provided for the convenience of future research.
基金financially supported by the National Natural Science Foundation of China(Nos.51373168,51390484,51233004 and 51321062)
文摘In this study, a novel approach by combining base-catalyzed epoxide ring-opening and thiol-ene click chemistry is presented for the side-chain modification of dextran. The vinyl-modified dextran is prepared by a basic epoxide ring opening reaction of allyl glycidyl ether in 0.1 mol/L NaOH, followed by thiol-addition click reaction of three model sulfhydryl compounds using water-soluble Irgacure 2959 as the photoinitiator, leading to side-chain functionalized dextran modified with carboxyl, bidentate dicarboxyl or amino groups. This is the first example of combining epoxide ring-opening and thiol- ene click chemistry for side-chain modification of dextran in aqueous media. Importantly, it may also be extended as a convenient and efficient method for the side-chain modification of other polysaccharides.
