The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these pro...The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these projects(including the Lexicon Etymologicum Digitale Indoeuropaeum(LEDI)directed by me)and analyse their approaches,features,and peculiarities(e.g.,commercial vs.open access).Special attention will paid to the projects’inclusions of phonetic rules and affixes,which makes derivation transparent and is helpful for didactic purposes.展开更多
Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr...Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.展开更多
The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functio...The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.展开更多
A Chinese-English Dictionary (Revised Edition) is even more standardized andconsistent than its 1978 edition in the inclusion of words, the arrangement of lexical units. thedefinition and the exemplification, but it i...A Chinese-English Dictionary (Revised Edition) is even more standardized andconsistent than its 1978 edition in the inclusion of words, the arrangement of lexical units. thedefinition and the exemplification, but it is still slightly blemished by some unfair coverage andinappropriate. unbalanced or unconcerted explanations, specifications and illustrations. Its meritsand demerits are made evident by comparing it with Far East Chinese-English Dictionary andother dictionaries.展开更多
Dictionary has many functions, in which the function of definition is of very importance because the main purpose of dictionary is providing the entry's meaning information for the readers so that the readers can ...Dictionary has many functions, in which the function of definition is of very importance because the main purpose of dictionary is providing the entry's meaning information for the readers so that the readers can understand and use the entry-word and the realization of the purpose completely depends on lexicographical definition. However, the function of definition is limited, which need the exemplification to assist it. Therefore, the exemplification becomes very important, too. Good exemplification can assist definition, provide grammatical information, and supplement the information usage and so on. Many researches studied the exemplification of dictionary, its principles and so on. Dictionary changed much with the development of technology and many kinds of electronic dictionaries appeared. Few studies are involved with the new-type dictionary. Based on the general principles of the exemplification in a learner's printed dictionary, it is necessary to construct the general principles about the exemplification in the electronic learner's dictionary.展开更多
文摘The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these projects(including the Lexicon Etymologicum Digitale Indoeuropaeum(LEDI)directed by me)and analyse their approaches,features,and peculiarities(e.g.,commercial vs.open access).Special attention will paid to the projects’inclusions of phonetic rules and affixes,which makes derivation transparent and is helpful for didactic purposes.
基金the National Natural Science Foundation of China(No.61861023)the Yunnan Fundamental Research Project(No.202301AT070452)。
文摘Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.
基金Supported by the National"863"Project(No.2014AA06A605)
文摘The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.
文摘A Chinese-English Dictionary (Revised Edition) is even more standardized andconsistent than its 1978 edition in the inclusion of words, the arrangement of lexical units. thedefinition and the exemplification, but it is still slightly blemished by some unfair coverage andinappropriate. unbalanced or unconcerted explanations, specifications and illustrations. Its meritsand demerits are made evident by comparing it with Far East Chinese-English Dictionary andother dictionaries.
文摘Dictionary has many functions, in which the function of definition is of very importance because the main purpose of dictionary is providing the entry's meaning information for the readers so that the readers can understand and use the entry-word and the realization of the purpose completely depends on lexicographical definition. However, the function of definition is limited, which need the exemplification to assist it. Therefore, the exemplification becomes very important, too. Good exemplification can assist definition, provide grammatical information, and supplement the information usage and so on. Many researches studied the exemplification of dictionary, its principles and so on. Dictionary changed much with the development of technology and many kinds of electronic dictionaries appeared. Few studies are involved with the new-type dictionary. Based on the general principles of the exemplification in a learner's printed dictionary, it is necessary to construct the general principles about the exemplification in the electronic learner's dictionary.
