With the purpose of improving the accuracy of text categorization and reducing the dimension of the feature space,this paper proposes a two-stage feature selection method based on a novel category correlation degree(C...With the purpose of improving the accuracy of text categorization and reducing the dimension of the feature space,this paper proposes a two-stage feature selection method based on a novel category correlation degree(CCD)method and latent semantic indexing(LSI).In the first stage,a novel CCD method is proposed to select the most effective features for text classification,which is more effective than the traditional feature selection method.In the second stage,document representation requires a high dimensionality of the feature space and does not take into account the semantic relation between features,which leads to a poor categorization accuracy.So LSI method is proposed to solve these problems by using statistically derived conceptual indices to replace the individual terms which can discover the important correlative relationship between features and reduce the feature space dimension.Firstly,each feature in our algorithm is ranked depending on their importance of classification using CCD method.Secondly,we construct a new semantic space based on LSI method among features.The experimental results have proved that our method can reduce effectively the dimension of text vector and improve the performance of text categorization.展开更多
Let F be an algebraically closed field of characteristic 0,H be an eight-dimensional non-semisimple Hopf algebra which is neither pointed nor unimodular and M2（F）be the full matrix algebra of 2×2 over F.In this...Let F be an algebraically closed field of characteristic 0,H be an eight-dimensional non-semisimple Hopf algebra which is neither pointed nor unimodular and M2（F）be the full matrix algebra of 2×2 over F.In this paper,we discuss and classify all H-module algebra structures on M2（F）.展开更多
In this work,we compute the Grothendieck groups of finite 2-Calabi-Yau triangulated categories with maximal rigid objects which are not cluster tilting.These finite 2-Calabi-Yau triangulated categories are divided int...In this work,we compute the Grothendieck groups of finite 2-Calabi-Yau triangulated categories with maximal rigid objects which are not cluster tilting.These finite 2-Calabi-Yau triangulated categories are divided into,by the work of Amiot[Bull.Soc.Math.France,2007,135(3):435-474](see also[Adv.Math.,2008,217(6):2443-2484]and[J.Algebra,2016,446:426-449]),three classes:type A,type D and type E.展开更多
Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision.Few-shot segmentation methods aim to address this problem by recognizing objects from specifi...Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision.Few-shot segmentation methods aim to address this problem by recognizing objects from specific target classes with a few provided examples.Previous approaches for few-shot semantic segmentation typically represent target classes using class prototypes.These prototypes are matched with the features of the query set to get segmentation results.However,class prototypes are usually obtained by applying global average pooling on masked support images.Global pooling discards much structural information,which may reduce the accuracy of model predictions.To address this issue,we propose a Category-Guided Frequency Modulation(CGFM)method.CGFM is designed to learn category-specific information in the frequency space and leverage it to provide a twostage guidance for the segmentation process.First,to self-adaptively activate class-relevant frequency bands while suppressing irrelevant ones,we leverage the Dual-Perception Gaussian Band Pre-activation(DPGBP)module to generate Gaussian filters using class embedding vectors.Second,to further enhance category-relevant frequency components in activated bands,we design a Support-Guided Category Response Enhancement(SGCRE)module to effectively introduce support frequency components into the modulation of query frequency features.Experiments on the PASCAL-5^(i) and COCO-20^(i) datasets demonstrate the promising performance of our model.展开更多
Stock return prediction has been in the spotlight because it involves numerous factors.Improving the accuracy of stock return prediction and quantifying the impact of individual factors on forecasting remain challengi...Stock return prediction has been in the spotlight because it involves numerous factors.Improving the accuracy of stock return prediction and quantifying the impact of individual factors on forecasting remain challenging tasks.Motivated by these challenges,we propose a novel forecasting method that entails proxy variables of category factors and the random forest technique.This new method aims to quantify the information and importance of category factors,thereby enhancing the predictability of stock returns.Specifically,we categorize a large set of return predictors into several category factors.We then utilize the importance of the original variables to construct proxy variables for these category factors.Subsequently,we use the proxy variables to build a random forest model for predicting stock returns.Our empirical analysis results demonstrate that the proposed method effectively quantifies the importance of both the original factors and category factors.Furthermore,we find that the fundamental information factor consistently ranks as the most crucial category factor for stock return forecasting.Additionally,the proposed method exhibits a more robust and prominent prediction performance than competing models such as single-category-factor-based random forest models,dimension-reduction,and forecast-combination methods.Most importantly,the proposed method produces forecast results that can assist investors with understanding stock market dynamics and facilitate higher investment returns.展开更多
The article presents an original concept of a universal philosophical language capable of transcending the boundaries between individual sciences and serving as a foundation for transdisciplinary thinking.This approac...The article presents an original concept of a universal philosophical language capable of transcending the boundaries between individual sciences and serving as a foundation for transdisciplinary thinking.This approach,developed by the author since the 1980s,is based on particular and general comparative concepts-concepts of practical mind and categories of pure mind.Therefore,the key element of the concept is the category of"particular and general",which fundamentally differs from the traditional category of"part and whole".This allows for the description of both structural and functional aspects of complex systems not only at the interdisciplinary but also at the transdisciplinary level.The primary categories of thought-Identity,Difference,Correlated,Opposite,and others-are regarded as universal notions that connect levels of reality and ensure the integration of individual sciences.Unlike contemporary transdisciplinary concepts based on Basarab Nicolescu's logic of the included middle and Edgar Morin's dialogics,the author's theory is built on the ultimate general Hegelian notion of"concrete identity"and its differentiation into a multitude of"concrete differences"-comparative concepts.As a result,a unique philosophical language has been developed,presented within the framework of the Philosophical Matrix as a system of categories of pure mind capable of describing the dynamics and wholeness of complex processes at the transdisciplinary level.The article is intended for researchers interested in the philosophical foundations of transdisciplinarity,the theory of complexity,and the development of universal categories of thought.展开更多
The question of how the category of entwined modules can be made into a braided monoidal category is studied. First, the sufficient and necessary conditions making the category into a monoidal category are obtained by...The question of how the category of entwined modules can be made into a braided monoidal category is studied. First, the sufficient and necessary conditions making the category into a monoidal category are obtained by using the fact that if （A, C, ψ） is an entwining structure, then A × C can be made into an entwined module. The conditions are that the algebra and coalgebra in question are both bialgebras with some extra compatibility relations. Then given a monodial category of entwined modules, the braiding is constructed by means of a twisted convolution invertible map Q, and the conditions making the category form into a braided monoidal category are obtained similarly. Finally, the construction is applied to the category of Doi-Hopf modules and （α, β ）-Yetter-Drinfeld modules as examples.展开更多
Let H be a commutative, noetherian, semisimple and cosemisimple Hopf algebra with a bijective antipode over a field k. Then the semisimplicity of YD（H） is considered, where YD （H） means the disjoint union of the c...Let H be a commutative, noetherian, semisimple and cosemisimple Hopf algebra with a bijective antipode over a field k. Then the semisimplicity of YD（H） is considered, where YD （H） means the disjoint union of the category of generalized Yetter-Drinfeld modules nYD^H（ α, β） for any α, β E Aut Hopf（H）. First, the fact that YD（H） is closed under Mor is proved. Secondly, based on the properties of finitely generated projective modules and semisimplicity of H, YD（H） satisfies the exact condition. Thus each object in YD（H） can be decomposed into simple ones since H is noetherian and cosemisimple. Finally, it is proved that YD （H） is a sernisimple category.展开更多
基金the National Natural Science Foundation of China(Nos.61073193 and 61300230)the Key Science and Technology Foundation of Gansu Province(No.1102FKDA010)+1 种基金the Natural Science Foundation of Gansu Province(No.1107RJZA188)the Science and Technology Support Program of Gansu Province(No.1104GKCA037)
文摘With the purpose of improving the accuracy of text categorization and reducing the dimension of the feature space,this paper proposes a two-stage feature selection method based on a novel category correlation degree(CCD)method and latent semantic indexing(LSI).In the first stage,a novel CCD method is proposed to select the most effective features for text classification,which is more effective than the traditional feature selection method.In the second stage,document representation requires a high dimensionality of the feature space and does not take into account the semantic relation between features,which leads to a poor categorization accuracy.So LSI method is proposed to solve these problems by using statistically derived conceptual indices to replace the individual terms which can discover the important correlative relationship between features and reduce the feature space dimension.Firstly,each feature in our algorithm is ranked depending on their importance of classification using CCD method.Secondly,we construct a new semantic space based on LSI method among features.The experimental results have proved that our method can reduce effectively the dimension of text vector and improve the performance of text categorization.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1127104311471186)
文摘Let F be an algebraically closed field of characteristic 0,H be an eight-dimensional non-semisimple Hopf algebra which is neither pointed nor unimodular and M2（F）be the full matrix algebra of 2×2 over F.In this paper,we discuss and classify all H-module algebra structures on M2（F）.
文摘In this work,we compute the Grothendieck groups of finite 2-Calabi-Yau triangulated categories with maximal rigid objects which are not cluster tilting.These finite 2-Calabi-Yau triangulated categories are divided into,by the work of Amiot[Bull.Soc.Math.France,2007,135(3):435-474](see also[Adv.Math.,2008,217(6):2443-2484]and[J.Algebra,2016,446:426-449]),three classes:type A,type D and type E.
文摘Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision.Few-shot segmentation methods aim to address this problem by recognizing objects from specific target classes with a few provided examples.Previous approaches for few-shot semantic segmentation typically represent target classes using class prototypes.These prototypes are matched with the features of the query set to get segmentation results.However,class prototypes are usually obtained by applying global average pooling on masked support images.Global pooling discards much structural information,which may reduce the accuracy of model predictions.To address this issue,we propose a Category-Guided Frequency Modulation(CGFM)method.CGFM is designed to learn category-specific information in the frequency space and leverage it to provide a twostage guidance for the segmentation process.First,to self-adaptively activate class-relevant frequency bands while suppressing irrelevant ones,we leverage the Dual-Perception Gaussian Band Pre-activation(DPGBP)module to generate Gaussian filters using class embedding vectors.Second,to further enhance category-relevant frequency components in activated bands,we design a Support-Guided Category Response Enhancement(SGCRE)module to effectively introduce support frequency components into the modulation of query frequency features.Experiments on the PASCAL-5^(i) and COCO-20^(i) datasets demonstrate the promising performance of our model.
基金supported by the National Natural Science Foundation of China(Grant No.72403117,U1901223,72271095)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20241435)+1 种基金Guangzhou Philosophy and Social Science Project(Grant No.x2gsN5180360)the Fundamental Research Funds for the Central Universities(Grant No.30923011034).
文摘Stock return prediction has been in the spotlight because it involves numerous factors.Improving the accuracy of stock return prediction and quantifying the impact of individual factors on forecasting remain challenging tasks.Motivated by these challenges,we propose a novel forecasting method that entails proxy variables of category factors and the random forest technique.This new method aims to quantify the information and importance of category factors,thereby enhancing the predictability of stock returns.Specifically,we categorize a large set of return predictors into several category factors.We then utilize the importance of the original variables to construct proxy variables for these category factors.Subsequently,we use the proxy variables to build a random forest model for predicting stock returns.Our empirical analysis results demonstrate that the proposed method effectively quantifies the importance of both the original factors and category factors.Furthermore,we find that the fundamental information factor consistently ranks as the most crucial category factor for stock return forecasting.Additionally,the proposed method exhibits a more robust and prominent prediction performance than competing models such as single-category-factor-based random forest models,dimension-reduction,and forecast-combination methods.Most importantly,the proposed method produces forecast results that can assist investors with understanding stock market dynamics and facilitate higher investment returns.
文摘The article presents an original concept of a universal philosophical language capable of transcending the boundaries between individual sciences and serving as a foundation for transdisciplinary thinking.This approach,developed by the author since the 1980s,is based on particular and general comparative concepts-concepts of practical mind and categories of pure mind.Therefore,the key element of the concept is the category of"particular and general",which fundamentally differs from the traditional category of"part and whole".This allows for the description of both structural and functional aspects of complex systems not only at the interdisciplinary but also at the transdisciplinary level.The primary categories of thought-Identity,Difference,Correlated,Opposite,and others-are regarded as universal notions that connect levels of reality and ensure the integration of individual sciences.Unlike contemporary transdisciplinary concepts based on Basarab Nicolescu's logic of the included middle and Edgar Morin's dialogics,the author's theory is built on the ultimate general Hegelian notion of"concrete identity"and its differentiation into a multitude of"concrete differences"-comparative concepts.As a result,a unique philosophical language has been developed,presented within the framework of the Philosophical Matrix as a system of categories of pure mind capable of describing the dynamics and wholeness of complex processes at the transdisciplinary level.The article is intended for researchers interested in the philosophical foundations of transdisciplinarity,the theory of complexity,and the development of universal categories of thought.
基金Specialized Research Fund for the Doctoral Program of Higher Education(No.20060286006)the National Natural Science Founda-tion of China(No.10571026)
文摘The question of how the category of entwined modules can be made into a braided monoidal category is studied. First, the sufficient and necessary conditions making the category into a monoidal category are obtained by using the fact that if （A, C, ψ） is an entwining structure, then A × C can be made into an entwined module. The conditions are that the algebra and coalgebra in question are both bialgebras with some extra compatibility relations. Then given a monodial category of entwined modules, the braiding is constructed by means of a twisted convolution invertible map Q, and the conditions making the category form into a braided monoidal category are obtained similarly. Finally, the construction is applied to the category of Doi-Hopf modules and （α, β ）-Yetter-Drinfeld modules as examples.
基金The National Natural Science Foundation of China(No.11371088)the Fundamental Research Funds for the Central Universities(No.3207013906)the Natural Science Foundation of Jiangsu Province(No.BK2012736)
文摘Let H be a commutative, noetherian, semisimple and cosemisimple Hopf algebra with a bijective antipode over a field k. Then the semisimplicity of YD（H） is considered, where YD （H） means the disjoint union of the category of generalized Yetter-Drinfeld modules nYD^H（ α, β） for any α, β E Aut Hopf（H）. First, the fact that YD（H） is closed under Mor is proved. Secondly, based on the properties of finitely generated projective modules and semisimplicity of H, YD（H） satisfies the exact condition. Thus each object in YD（H） can be decomposed into simple ones since H is noetherian and cosemisimple. Finally, it is proved that YD （H） is a sernisimple category.
