Efficient Interconnect Network Model for Linear Circuit Reduction

A new interconnect network model for linear netw ork reduction is presented.In this new model,the ports of the interconnect network are classified into two groups:active and passive ports.After the classification,some proprieties of the interconnect network are found to be redundant and pruned before reduction.For common interconnect networks,the scale of reduced models is smaller than 50% of the scale of previous works.

Review of Dimension Reduction Methods

Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR techniques were explored. The different data types that have been applied on the various DR techniques were also explored.

复杂性科学视野中的还原论问题

还原论问题是哲学界及科学界激烈争论的问题,本文尝试沿着科学进路在特定视角下分析该问题。在把“不可还原”理解为“有新特性且此特性不可预知”的意义上,于复杂性科学视野中存在着一个考察还原论问题的标度阶梯:线性、非线性、不可计算性,每一个视角下都有其相应的可还原性和不可还原性。可见,复杂性科学进路中可还原与否是依赖于考察问题的视角的。

线性思维、化约主义与高台“说教”——评梁涛等学者对“亲亲相隐”及相关文本的误读

梁涛等学者按自己主观需要,运用有学术争议的出土资料,构造自己想象的"学理",错误地解读了"亲亲相隐"等案例。该研究思路存在以下误区:第一,以线性思维解读儒家经典,形成早期儒家思想是从简单原始的"情"逐渐发展为抽象僵硬的"理"之机械的线性过程;第二,以化约主义的方法解读儒家经典,将当代社会中腐败等复杂问题,简单化约为儒家思想与现代化的问题;第三,把情理绝对地对立起来,把人情都讲成私情,再以空洞的"普遍正义"的"理"作为立足点,将自己作为"人子"之天然存在的主体身份拱手让渡,抽离于活生生的生活世界之外。儒家伦理与其中有关"亲亲相隐"的论说,其实都是极为复杂的问题。复杂性的问题不能简单处理,在理解上应避免直线式思考与非此即彼的方式。以终极信仰与道德的普遍性为背景的儒家的角色伦理、处境伦理的体验方式,饱含着具体理性,以简单抽象的方式去对待,在理解上或不能相应。

AN APPROACH TOWARDS HOLISM IN SCIENCE AND ENGINEERING

This paper posits the desirability of a shift towards a holistic approach over reductionist approaches in the understanding of complex phenomena encountered in science and engineering. An argument based on set theory is used to analyze three examples that illustrate the shortcomings of the reductionist approach. Using these cases as motivational points, a holistic approach to understand complex phenomena is proposed, whereby the human brain acts as a template to do so. Recognizing the need to maintain the transparency of the analysis provided by reductionism, a promising computational approach is offered by which the brain is used as a template for understanding complex phenomena. Some of the details of implementing this approach are also addressed.