Lingyanshanren shi ji : 40 juan /

Fu: Yanshan Bi gong nian pu : 1 juan / Shi Shanchang zhuan.

Wei jian shi ji : 5 juan /

Chong kan ben.

Xu You xitang ni Ming shi yue fu : fu: Lun shi jue ju /

Double leaves, oriental style, in case.

Dong gang shi sheng : shi si juan, juan shou, juan mo /

Jiaqing er shi nian Shu Wei xu shu ji ke shu shi.

Nan gai shi gao : yi juan /

Jiaqing wu yin Zhu Dong xu shu ji Ding shi ke shu shi.

Yi an zhai shi ji : liu juan, juan shou /

Nei feng you shang: Jiaqing shi si nian juan, zuo xia: De fen hou pu cang ban.

Dan cui xuan shi cao : er juan /

Nei feng you shang: Jiaqing geng chen xin juan, zuo xia: Jiang zhou shou ju cang ban.

Quan Tang jin ti shi chao : wu juan /

Daoguang er nian Yao Wentian xu shu ji qi ke shu shi.

Lu'an shi chao qian bian : si juan ; Lu'an shi chao hou bian : shi er juan /

Nei feng you shang juan: Daoguang ji hai xin ke, zuo xia juan: Gua guo wei neng zhai cang ban.

Xi wen guo zhai wen ji : shi er juan /

Nei feng tian tou juan: Daoguang ji hai zhong qiu juan, zuo xia juan: Hou yuan tang cang ban.

Jimo shi sheng : shi er juan /

Nei feng you shang juan: Daoguang geng zi juan, zuo xia juan: Xiao xian shan fang cang ban.

Hōrei zensho.

Some issues published in parts.

Nan Yang Qun Dao xin di tu = Map of East Indies /

Insets: Zhaowa xiang tu -- Nan Yang Qun Dao di xing tu -- Malai Ban Dao xiang tu.

Direct mapping of hippocampal surfaces with intrinsic shape context

We propose in this paper a new method for the mapping of hippocampal (HC) surfaces to establish correspondences between points on HC surfaces and enable localized HC shape analysis. A novel geometric feature, the intrinsic shape context, is defined to capture the global characteristics of the HC shapes. Based on this intrinsic feature, an automatic algorithm is developed to detect a set of landmark curves that are stable across population. The direct map between a source and target HC surface is then solved as the minimizer of a harmonic energy function defined on the source surface with landmark constraints. For numerical solutions, we compute the map with the approach of solving partial differential equations on implicit surfaces. The direct mapping method has the following properties: (1) it has the advantage of being automatic; (2) it is invariant to the pose of HC shapes. In our experiments, we apply the direct mapping method to study temporal changes of HC asymmetry in Alzheimer's disease (AD) using HC surfaces from 12 AD patients and 14 normal controls. Our results show that the AD group has a different trend in temporal changes of HC asymmetry than the group of normal controls. We also demonstrate the flexibility of the direct mapping method by applying it to construct spherical maps of HC surfaces. Spherical harmonics (SPHARM) analysis is then applied and it confirms our results on temporal changes of HC asymmetry in AD.

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developed to explore patterns in high-dimensional multivariate data. The conventional version of the algorithm involves the use of Euclidean metric in the process of adaptation of the model vectors, thus rendering in theory a whole methodology incompatible with non-Euclidean geometries. In this contribution we explore the two main aspects of the problem: 1. Whether the conventional approach using Euclidean metric can shed valid results with compositional data. 2. If a modification of the conventional approach replacing vectorial sum and scalar multiplication by the canonical operators in the simplex (i.e. perturbation and powering) can converge to an adequate solution. Preliminary tests showed that both methodologies can be used on compositional data. However, the modified version of the algorithm performs poorer than the conventional version, in particular, when the data is pathological. Moreover, the conventional ap- proach converges faster to a solution, when data is \well-behaved". Key words: Self Organizing Map; Artificial Neural networks; Compositional data