A Clustering-guided Contrastive Fusion for Multi-view Representation Learning

The past two decades have seen increasingly rapid advances in the field of multi-view representation learning due to it extracting useful information from diverse domains to facilitate the development of multi-view applications. However, the community faces two challenges: i) how to learn robust representations from a large amount of unlabeled data to against noise or incomplete views setting, and ii) how to balance view consistency and complementary for various downstream tasks. To this end, we utilize a deep fusion network to fuse view-specific representations into the view-common representation, extracting high-level semantics for obtaining robust representation. In addition, we employ a clustering task to guide the fusion network to prevent it from leading to trivial solutions. For balancing consistency and complementary, then, we design an asymmetrical contrastive strategy that aligns the view-common representation and each view-specific representation. These modules are incorporated into a unified method known as CLustering-guided cOntrastiVE fusioN (CLOVEN). We quantitatively and qualitatively evaluate the proposed method on five datasets, demonstrating that CLOVEN outperforms 11 competitive multi-view learning methods in clustering and classification. In the incomplete view scenario, our proposed method resists noise interference better than those of our competitors. Furthermore, the visualization analysis shows that CLOVEN can preserve the intrinsic structure of view-specific representation while also improving the compactness of view-commom representation. Our source code will be available soon at https://github.com/guanzhou-ke/cloven.Comment: 13 pages, 9 figure

Global Convergence of a New Nonmonotone Filter Method for Equality Constrained Optimization

A new nonmonotone filter trust region method is introduced for solving optimization problems with equality constraints. This method directly uses the dominated area of the filter as an acceptability criterion for trial points and allows the dominated area decreasing nonmonotonically. Compared with the filter-type method, our method has more flexible criteria and can avoid Maratos effect in a certain degree. Under reasonable assumptions, we prove that the given algorithm is globally convergent to a first order stationary point for all possible choices of the starting point. Numerical tests are presented to show the effectiveness of the proposed algorithm

Data mining on varieties, therapeutic uses and medicinal characteristics of Traditional Chinese Medicine preparations for treating hair loss

Purpose: To review the varieties, therapeutic uses, and medicinal characteristics of traditional Chinese medicine (TCM) preparations for treating hair loss, and to provide a reference for research and development of new drugs.Methods: For this review, literature from the last 50 years pertaining to the treatment of hair loss via TCM were collected mainly from China National Knowledge Infrastructure database and Wanfang Data Resource System database. Information on Chinese traditional patent medicines and ethnomedicines for treating hair loss was drawn from books.Results: A total of 322 preparations were identified, including 135 preparations made by medical institutions, 108 Chinese traditional patent medicines, 60 preparations produced by doctors themselves, and 19 ethnomedicines. The forms of dosage included  decoctions, pills, capsules, tablets, granules, tinctures, liniments, and powders. These preparations are traditionally used in the treatment of skin and subcutaneous tissue diseases. A total of 400 medicinal materials were used in preparations, including 339 from plants, 40 from animals, 14 from minerals. The most commonly used Chinese medicinal materials in order of frequency were Rehmannia glutinosa (Gaertn.) DC., Fallopia multiflora (Thunb.) Haraldson, Angelica sinensis (Oliv.) Diels, Ligustrum lucidum W.T. Aiton and Ligusticum chuanxiong Hort.Conclusion: TCM preparations for treating hair loss are abundant in variety. They are mainly decoctions, and primarily botanical medicinal materials. Most of the preparations are composed of Chinese medicinal materials for ‘toning the kidneys’ and ‘nourishing the liver’. They are used mainly in the treatment of seborrheic alopecia and alopecia areat

Generation and Bioenergetic Profiles of Cybrids with East Asian mtDNA Haplogroups

Human mitochondrial DNA (mtDNA) variants and haplogroups may contribute to susceptibility to various diseases and pathological conditions, but the underlying mechanisms are not well understood. To address this issue, we established a cytoplasmic hybrid (cybrid) system to investigate the role of mtDNA haplogroups in human disease; specifically, we examined the effects of East Asian mtDNA genetic backgrounds on oxidative phosphorylation (OxPhos). We found that mtDNA single nucleotide polymorphisms such as m.489T>C, m.10398A>G, m.10400C>T, m.C16223T, and m.T16362C affected mitochondrial function at the level of mtDNA, mtRNA, or the OxPhos complex. Macrohaplogroup M exhibited higher respiratory activity than haplogroup N owing to its higher mtDNA content, mtRNA transcript levels, and complex III abundance. Additionally, haplogroup M had higher reactive oxygen species levels and NAD+/NADH ratios than haplogroup N, suggesting difference in mitonuclear interactions. Notably, subhaplogroups G2, B4, and F1 appeared to contribute significantly to the differences between haplogroups M and N. Thus, our cybrid-based system can provide insight into the mechanistic basis for the role of mtDNA haplogroups in human diseases and the effect of mtDNA variants on mitochondrial OxPhos function. In addition, studies of mitonuclear interaction using this system can reveal predisposition to certain diseases conferred by variations in mtDNA

Improved Multiobjective Harmony Search Algorithm with Application to Placement and Sizing of Distributed Generation

To solve the comprehensive multiobjective optimization problem, this study proposes an improved metaheuristic searching algorithm with combination of harmony search and the fast nondominated sorting approach. This is a kind of the novel intelligent optimization algorithm for multiobjective harmony search (MOHS). The detailed description and the algorithm formulating are discussed. Taking the optimal placement and sizing issue of distributed generation (DG) in distributed power system as one example, the solving procedure of the proposed method is given. Simulation result on modified IEEE 33-bus test system and comparison with NSGA-II algorithm has proved that the proposed MOHS can get promising results for engineering application